2018/07/27 Deep Learning JP: http://deeplearning.jp/seminar-2/

1. 1
DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
Conditional Neural Processes (ICML2018)
Neural Processes (ICML2018Workshop)
Kazuki Fujikawa, DeNA

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y µX) ,
1
KX,X⇤
.
n matrix of
X⇤ and X.
X and X⇤,
valuated at
s implicitly
dels which
space, and
ese models
or support
of any con-
Indeed the
induced by
ernel func-
In this section we show how we can contruct kernels
which encapsulate the expressive power of deep archi-
tectures, and how to learn the properties of these ker-
nels as part of a scalable probabilistic GP framework.
Specifically, starting from a base kernel k(xi, xj|✓)
with hyperparameters ✓, we transform the inputs (pre-
dictors) x as
k(xi, xj|✓) ! k(g(xi, w), g(xj, w)|✓, w) , (5)
where g(x, w) is a non-linear mapping given by a deep
architecture, such as a deep convolutional network,
parametrized by weights w. The popular RBF kernel
(Eq. (3)) is a sensible choice of base kernel k(xi, xj|✓).
For added flexibility, we also propose to use spectral
mixture base kernels (Wilson and Adams, 2013):
kSM(x, x0
|✓) = (6)
QX
q=1
aq
|⌃q|
1
2
(2⇡)
D
2
exp
✓
1
2
||⌃
1
2
q (x x0
)||2
◆
coshx x0
, 2⇡µqi .
372
he marginal likeli-
des significant ad-
ied to the output
twork. Moreover,
ng non-parametric
very recent KISS-
, 2015) and exten-
iently representing
e deep kernels.
ses (GPs) and the
dictions and kernel
Williams (2006) for
.
(predictor) vectors
on D, which index
(x1), . . . , y(xn))>
.
ection of function
bution,
N (µ, KX,X ) , (1)
e matrix defined
nce kernel of the
2
encodes the inductive bias that function values closer
together in the input space are more correlated. The
complexity of the functions in the input space is deter-
mined by the interpretable length-scale hyperparam-
eter `. Shorter length-scales correspond to functions
which vary more rapidly with the inputs x.
The structure of our data is discovered through
learning interpretable kernel hyperparameters. The
marginal likelihood of the targets y, the probability
of the data conditioned only on kernel hyperparame-
ters , provides a principled probabilistic framework
for kernel learning:
log p(y| , X) / [y>
(K + 2
I) 1
y+log |K + 2
I|] ,
(4)
where we have used K as shorthand for KX,X given .
Note that the expression for the log marginal likeli-
hood in Eq. (4) pleasingly separates into automatically
calibrated model fit and complexity terms (Rasmussen
and Ghahramani, 2001). Kernel learning is performed
by optimizing Eq. (4) with respect to .
The computational bottleneck for inference is solving
the linear system (KX,X + 2
I) 1
y, and for kernel
learning is computing the log determinant log |KX,X +
2
I|. The standard approach is to compute the
Cholesky decomposition of the n ⇥ n matrix KX,X ,
Deep Kernel Learning
. .
h
(L)
1
h
(L)
C
(L)
h1(✓)
h1(✓)
ers
1 layer
y1
yP
Output layer
. .
...
......
...
...
ing: A Gaussian process with
onal inputs x through L para-
by a hidden layer with an in-
ns, with base kernel hyperpa-
an process with a deep kernel
ping with an infinite number
parametrized by = {w, ✓}.
jointly through the marginal
ocess.
For kernel learning, we use the chain rule to compute
derivatives of the log marginal likelihood with respect
to the deep kernel hyperparameters:
@L
@✓
=
@L
@K
@K
@✓
,
@L
@w
=
@L
@K
@K
@g(x, w)
@g(x, w)
@w
.
The implicit derivative of the log marginal likelihood
with respect to our n ⇥ n data covariance matrix K
is given by
@L
@K
=
1
2
(K 1
yy>
K 1
K 1
) , (7)
where we have absorbed the noise covariance 2
I into
our covariance matrix, and treat it as part of the base
kernel hyperparameters ✓.
@K
@✓ are the derivatives of
the deep kernel with respect to the base kernel hyper-
parameters (such as length-scale), conditioned on the
fixed transformation of the inputs g(x, w). Similarly,

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denote its output. The ith component of the activations in the lth layer, post-nonlinearity and post-
affine transformation, are denoted xl
i and zl
i respectively. We will refer to these as the post- and
pre-activations. (We let x0
i ⌘ xi for the input, dropping the Arabic numeral superscript, and instead
use a Greek superscript x↵
to denote a particular input ↵). Weight and bias parameters for the lth
layer have components Wl
ij, bl
i, which are independent and randomly drawn, and we take them all to
have zero mean and variances 2
w/Nl and 2
b , respectively. GP(µ, K) denotes a Gaussian process
with mean and covariance functions µ(·), K(·, ·), respectively.
2.2 REVIEW OF GAUSSIAN PROCESSES AND SINGLE-LAYER NEURAL NETWORKS
We briefly review the correspondence between single-hidden layer neural networks and GPs (Neal
(1994a;b); Williams (1997)). The ith component of the network output, z1
i , is computed as,
z1
i (x) = b1
i +
N1X
j=1
W1
ijx1
j (x), x1
j (x) =
✓
b0
j +
dinX
k=1
W0
jkxk
◆
, (1)
where we have emphasized the dependence on input x. Because the weight and bias parameters are
taken to be i.i.d., the post-activations x1
j , x1
j0 are independent for j 6= j0
. Moreover, since z1
i (x) is
a sum of i.i.d terms, it follows from the Central Limit Theorem that in the limit of infinite width
N1 ! 1, z1
i (x) will be Gaussian distributed. Likewise, from the multidimensional Central Limit
Theorem, any finite collection of {z1
i (x↵=1
), ..., z1
i (x↵=k
)} will have a joint multivariate Gaussian
distribution, which is exactly the definition of a Gaussian process. Therefore we conclude that
z1
i ⇠ GP(µ1
, K1
), a GP with mean µ1
and covariance K1
, which are themselves independent of i.
Because the parameters have zero mean, we have that µ1
(x) = E
⇥
z1
i (x)
⇤
= 0 and,
K1
(x, x0
) ⌘ E
⇥
z1
i (x)z1
i (x0
)
⇤
= 2
b + 2
w E
⇥
x1
i (x)x1
i (x0
)
⇤
⌘ 2
b + 2
wC(x, x0
), (2)
where we have introduced C(x, x0
) as in Neal (1994a); it is obtained by integrating against the
distribution of W0
, b0
. Note that, as any two z1
i , z1
j for i 6= j are joint Gaussian and have zero
covariance, they are guaranteed to be independent despite utilizing the same features produced by
the hidden layer.
3
We briefly review the correspondence between single-hidden layer neural networks and GPs (Neal
(1994a;b); Williams (1997)). The ith component of the network output, z1
i , is computed as,
z1
i (x) = b1
i +
N1X
j=1
W1
ijx1
j (x), x1
j (x) =
✓
b0
j +
dinX
k=1
W0
jkxk
◆
, (1)
where we have emphasized the dependence on input x. Because the weight and bias parameters are
taken to be i.i.d., the post-activations x1
j , x1
j0 are independent for j 6= j0
. Moreover, since z1
i (x) is
a sum of i.i.d terms, it follows from the Central Limit Theorem that in the limit of infinite width
N1 ! 1, z1
i (x) will be Gaussian distributed. Likewise, from the multidimensional Central Limit
Theorem, any finite collection of {z1
i (x↵=1
), ..., z1
i (x↵=k
)} will have a joint multivariate Gaussian
distribution, which is exactly the definition of a Gaussian process. Therefore we conclude that
z1
i ⇠ GP(µ1
, K1
), a GP with mean µ1
and covariance K1
, which are themselves independent of i.
Because the parameters have zero mean, we have that µ1
(x) = E
⇥
z1
i (x)
⇤
= 0 and,
K1
(x, x0
) ⌘ E
⇥
z1
i (x)z1
i (x0
)
⇤
= 2
b + 2
w E
⇥
x1
i (x)x1
i (x0
)
⇤
⌘ 2
b + 2
wC(x, x0
), (2)
where we have introduced C(x, x0
) as in Neal (1994a); it is obtained by integrating against the
distribution of W0
, b0
. Note that, as any two z1
i , z1
j for i 6= j are joint Gaussian and have zero
covariance, they are guaranteed to be independent despite utilizing the same features produced by
the hidden layer.
3
2.3 GAUSSIAN PROCESSES AND DEEP NEURAL NETWORKS
The arguments of the previous section can be extended to deeper layers by induction. We proceed
by taking the hidden layer widths to be infinite in succession (N1 ! 1, N2 ! 1, etc.) as we
continue with the induction, to guarantee that the input to the layer under consideration is already
governed by a GP. In Appendix C we provide an alternative derivation, in terms of marginalization
over intermediate layers, which does not depend on the order of limits, in the case of a Gaussian
prior on the weights. A concurrent work (Anonymous, 2018) further derives the convergence rate
towards a GP if all layers are taken to infinite width simultaneously.
Suppose that zl 1
j is a GP, identical and independent for every j (and hence xl
j(x) are independent
and identically distributed). After l 1 steps, the network computes
zl
i(x) = bl
i +
NlX
j=1
Wl
ijxl
j(x), xl
j(x) = (zl 1
j (x)). (3)
As before, zl
i(x) is a sum of i.i.d. random terms so that, as Nl ! 1, any finite collection
{z1
i (x↵=1
), ..., z1
i (x↵=k
)} will have joint multivariate Gaussian distribution and zl
i ⇠ GP(0, Kl
).
The covariance is
Kl
(x, x0
) ⌘ E
⇥
zl
i(x)zl
i(x0
)
⇤
= 2
b + 2
w Ezl 1
i ⇠GP(0,Kl 1)
⇥
(zl 1
i (x)) (zl 1
i (x0
))
⇤
. (4)
By induction, the expectation in Equation (4) is over the GP governing zl 1
i , but this is equivalent
to integrating against the joint distribution of only zl 1
i (x) and zl 1
i (x0
). The latter is described by
a zero mean, two-dimensional Gaussian whose covariance matrix has distinct entries Kl 1
(x, x0
),
continue with the induction, to guarantee that the input to the layer under consideration is already
governed by a GP. In Appendix C we provide an alternative derivation, in terms of marginalization
over intermediate layers, which does not depend on the order of limits, in the case of a Gaussian
prior on the weights. A concurrent work (Anonymous, 2018) further derives the convergence rate
towards a GP if all layers are taken to infinite width simultaneously.
Suppose that zl 1
j is a GP, identical and independent for every j (and hence xl
j(x) are independent
and identically distributed). After l 1 steps, the network computes
zl
i(x) = bl
i +
NlX
j=1
Wl
ijxl
j(x), xl
j(x) = (zl 1
j (x)). (3)
As before, zl
i(x) is a sum of i.i.d. random terms so that, as Nl ! 1, any finite collection
{z1
i (x↵=1
), ..., z1
i (x↵=k
)} will have joint multivariate Gaussian distribution and zl
i ⇠ GP(0, Kl
).
The covariance is
Kl
(x, x0
) ⌘ E
⇥
zl
i(x)zl
i(x0
)
⇤
= 2
b + 2
w Ezl 1
i ⇠GP(0,Kl 1)
⇥
(zl 1
i (x)) (zl 1
i (x0
))
⇤
. (4)
By induction, the expectation in Equation (4) is over the GP governing zl 1
i , but this is equivalent
to integrating against the joint distribution of only zl 1
i (x) and zl 1
i (x0
). The latter is described by
a zero mean, two-dimensional Gaussian whose covariance matrix has distinct entries Kl 1
(x, x0
),
Kl 1
(x, x), and Kl 1
(x0
, x0
). As such, these are the only three quantities that appear in the result.
We introduce the shorthand
Kl
(x, x0
) = 2
b + 2
w F
⇣
Kl 1
(x, x0
), Kl 1
(x, x), Kl 1
(x0
, x0
)
⌘
(5)
to emphasize the recursive relationship between Kl
and Kl 1
via a deterministic function F whose
form depends only on the nonlinearity . This gives an iterative series of computations which can
2.3 GAUSSIAN PROCESSES AND DEEP NEURAL NETWORKS
The arguments of the previous section can be extended to deeper layers by induction. We proceed
by taking the hidden layer widths to be infinite in succession (N1 ! 1, N2 ! 1, etc.) as we
continue with the induction, to guarantee that the input to the layer under consideration is already
governed by a GP. In Appendix C we provide an alternative derivation, in terms of marginalization
over intermediate layers, which does not depend on the order of limits, in the case of a Gaussian
prior on the weights. A concurrent work (Anonymous, 2018) further derives the convergence rate
towards a GP if all layers are taken to infinite width simultaneously.
Suppose that zl 1
j is a GP, identical and independent for every j (and hence xl
j(x) are independent
and identically distributed). After l 1 steps, the network computes
zl
i(x) = bl
i +
NlX
j=1
Wl
ijxl
j(x), xl
j(x) = (zl 1
j (x)). (3)
As before, zl
i(x) is a sum of i.i.d. random terms so that, as Nl ! 1, any finite collection
{z1
i (x↵=1
), ..., z1
i (x↵=k
)} will have joint multivariate Gaussian distribution and zl
i ⇠ GP(0, Kl
).
The covariance is
Kl
(x, x0
) ⌘ E
⇥
zl
i(x)zl
i(x0
)
⇤
= 2
b + 2
w Ezl 1
i ⇠GP(0,Kl 1)
⇥
(zl 1
i (x)) (zl 1
i (x0
))
⇤
. (4)
By induction, the expectation in Equation (4) is over the GP governing zl 1
i , but this is equivalent
l 1 l 1 0

16. • 76:
Learning to Learn
134
Losses
+ , 2 0 1

17. •
7 : + 12 ,0
Losses
Learning to Learn
Model Based
● Santoro et al. ’16
● Duan et al. ’17
● Wang et al. ‘17
● Munkhdalai & Yu ‘17
● Mishra et al. ‘17
Optimization Based
● Schmidhuber ’87, ’92
● Bengio et al. ’90, ‘92
● Hochreiter et al. ’01
● Li & Malik ‘16
● Andrychowicz et al. ’16
● Ravi & Larochelle ‘17
● Finn et al ‘17
Metric Based
● Koch ’15
● Vinyals et al. ‘16
● Snell et al. ‘17
● Shyam et al. ‘17
Trends: Learning to Learn / Meta Learning 135

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2 + g
)
Oriol Vinyals, NIPS 17
Model Based Meta Learning
6 L 0-1 ( I LC C

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• 6 I N ca d c ! "# Sg e p "#(%&, !)
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Oriol Vinyals, NIPS 17
Matching Networks, Vinyals et al, NIPS 2016
Metric Based Meta Learning

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Model-Agnostic Meta-Learning for Fast Adaptation o
Algorithm 1 Model-Agnostic Meta-Learning
Require: p(T ): distribution over tasks
Require: ↵, : step size hyperparameters
1: randomly initialize ✓
2: while not done do
3: Sample batch of tasks Ti ⇠ p(T )
4: for all Ti do
5: Evaluate r✓LTi
(f✓) with respect to K examples
6: Compute adapted parameters with gradient de-
scent: ✓0
i = ✓ ↵r✓LTi
(f✓)
7: end for
8: Update ✓ ✓ r✓
P
Ti⇠p(T ) LTi (f✓0
i
)
9: end while
make no assumption on the form of the model, other than
products, which
braries such as
experiments, w
this backward p
which we discu
3. Species of
In this section,
meta-learning a
forcement learn
function and in
sented to the m
nism can be app
3.1. Supervised
meta-learning
learning/adaptation
✓
rL1
rL2
rL3
✓⇤
1 ✓⇤
2
✓⇤
3
Figure 1. Diagram of our model-agnostic meta-learning algo-
rithm (MAML), which optimizes for a representation ✓ that can
quickly adapt to new tasks.
Oriol Vinyals, NIPS 17
Summing Up
Model Based
Metric Based
Optimization Based
Oriol Vinyals, NIPS 17
Examples of Optimization Based Meta Learning
Finn et al, 17
Ravi et al, 17

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Conditional Neural Processes
Marta Garnelo 1
Dan Rosenbaum 1
Chris J. Maddison 1
Tiago Ramalho 1
David Saxton 1
Murray Shanahan 1 2
Yee Whye Teh 1
Danilo J. Rezende 1
S. M. Ali Eslami 1
Abstract
Deep neural networks excel at function approxi-
mation, yet they are typically trained from scratch
for each new function. On the other hand,
Bayesian methods, such as Gaussian Processes
(GPs), exploit prior knowledge to quickly infer
the shape of a new function at test time. Yet GPs
are computationally expensive, and it can be hard
to design appropriate priors. In this paper we
propose a family of neural models, Conditional
Neural Processes (CNPs), that combine the bene-
fits of both. CNPs are inspired by the flexibility
of stochastic processes such as GPs, but are struc-
tured as neural networks and trained via gradient
descent. CNPs make accurate predictions after
observing only a handful of training data points,
yet scale to complex functions and large datasets.
We demonstrate the performance and versatility
of the approach on a range of canonical machine
learning tasks, including regression, classification
and image completion.
1. Introduction
Deep neural networks have enjoyed remarkable success
in recent years, but they require large datasets for effec-
tive training (Lake et al., 2017; Garnelo et al., 2016). One
bSupervisedLearning
PredictTrain
…x3x1 x5 x6x4
y5 y6y4
y3y1 g gg
x2
y2
g
…
Observations Targets
…x6
y6
f
x5
y5
f
x4
y4
f
x3
y3
f
x2
y2
f
x1
y1
f
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…
PredictObserve Aggregate
r3r2r1
…x3x2x1 x5 x6x4
ahhh y5 y6y4
ry3y2y1 g gg
cOurModel
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Figure 1. Conditional Neural Process. a) Data description
b) Training regime of conventional supervised deep learning mod-
els c) Our model.
of these can be framed as function approximation given a
v:1807.01613v1[cs.LG]4Jul2018

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Abstract
Deep neural networks excel at function approxi-
mation, yet they are typically trained from scratch
for each new function. On the other hand,
Bayesian methods, such as Gaussian Processes
(GPs), exploit prior knowledge to quickly infer
the shape of a new function at test time. Yet GPs
are computationally expensive, and it can be hard
to design appropriate priors. In this paper we
propose a family of neural models, Conditional
Neural Processes (CNPs), that combine the bene-
fits of both. CNPs are inspired by the flexibility
of stochastic processes such as GPs, but are struc-
tured as neural networks and trained via gradient
descent. CNPs make accurate predictions after
observing only a handful of training data points,
yet scale to complex functions and large datasets.
We demonstrate the performance and versatility
of the approach on a range of canonical machine
learning tasks, including regression, classification
and image completion.
1. Introduction
Deep neural networks have enjoyed remarkable success
in recent years, but they require large datasets for effec-
bSupervisedLearning
PredictTrain
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x2
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Observations Targets
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y5
f
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y4
f
x3
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f
x2
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f
x1
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PredictObserve Aggregate
r3r2r1
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Figure 1. Conditional Neural Process. a) Data description
b) Training regime of conventional supervised deep learning mod-
els c) Our model.
:1807.01613v1[cs.LG]4Jul2018
Conditional Neural Processes
spect to some prior process. CNPs parametrize distributions
over f(T) given a distributed representation of O of fixed
dimensionality. By doing so we give up the mathematical
guarantees associated with stochastic processes, trading this
off for functional flexibility and scalability.
Specifically, given a set of observations O, a CNP is a con-
ditional stochastic process Q✓ that defines distributions over
f(x) for inputs x 2 T. ✓ is the real vector of all parame-
ters defining Q. Inheriting from the properties of stochastic
processes, we assume that Q✓ is invariant to permutations
of O and T. If O0
, T0
are permutations of O and T, re-
spectively, then Q✓(f(T) | O, T) = Q✓(f(T0
) | O, T0
) =
Q✓(f(T) | O0
, T). In this work, we generally enforce per-
mutation invariance with respect to T by assuming a fac-
tored structure. Specifically, we consider Q✓s that factor
Q✓(f(T) | O, T) =
Q
x2T Q✓(f(x) | O, x). In the absence
of assumptions on output space Y , this is the easiest way to
ensure a valid stochastic process. Still, this framework can
be extended to non-factored distributions, we consider such
a model in the experimental section.
The defining characteristic of a CNP is that it conditions on
O via an embedding of fixed dimensionality. In more detail,
we use the following architecture,
ri = h✓(xi, yi) 8(xi, yi) 2 O (1)
r = r1 r2 . . . rn 1 rn (2)
i = g✓(xi, r) 8(xi) 2 T (3)
where h✓ : X ⇥Y ! Rd
and g✓ : X ⇥Rd
! Re
are neural
ON = {(xi, yi)}N
i
minimize the nega
L(✓) = Ef⇠P
h
Thus, the targets i
and unobserved v
estimates of the gr
This approach shi
edge from an ana
the advantage of
specify an analyti
intended to summa
emphasize that the
conditionals for all
does not guarantee
1. A CNP is a
trained to mo
of functions f
2. A CNP is per
3. A CNP is sca
ity of O(n +
observations.
Within this specifi
aspects that can b
The exact impleme

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Conditional Neural Processes
metrize distributions
tation of O of fixed
p the mathematical
ocesses, trading this
lity.
s O, a CNP is a con-
es distributions over
ector of all parame-
perties of stochastic
ant to permutations
ns of O and T, re-
✓(f(T0
) | O, T0
) =
nerally enforce per-
by assuming a fac-
der Q✓s that factor
O, x). In the absence
is the easiest way to
this framework can
ns, we consider such
that it conditions on
ON = {(xi, yi)}N
i=0 ⇢ O, the first N elements of O. We
minimize the negative conditional log probability
L(✓) = Ef⇠P
h
EN
h
log Q✓({yi}n 1
i=0 |ON , {xi}n 1
i=0 )
ii
(4)
Thus, the targets it scores Q✓ on include both the observed
and unobserved values. In practice, we take Monte Carlo
estimates of the gradient of this loss by sampling f and N.
This approach shifts the burden of imposing prior knowl-
edge from an analytic prior to empirical data. This has
the advantage of liberating a practitioner from having to
specify an analytic form for the prior, which is ultimately
intended to summarize their empirical experience. Still, we
emphasize that the Q✓ are not necessarily a consistent set of
conditionals for all observation sets, and the training routine
does not guarantee that. In summary,
1. A CNP is a conditional distribution over functions
trained to model the empirical conditional distributions
of functions f ⇠ P.

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3.2. Meta-Learning
Deep learning models are generally more scalable and are
very successful at learning features and prior knowledge
from the data directly. However they tend to be less flex-
ible with regards to input size and order. Additionally, in
general they only approximate one function as opposed to
distributions over functions. Meta-learning approaches ad-
dress the latter and share our core motivations. Recently
meta-learning has been applied to a wide range of tasks
like RL (Wang et al., 2016; Finn et al., 2017) or program
induction (Devlin et al., 2017).
Often meta-learning algorithms are implemented as deep
generative models that learn to do few-shot estimations
of the underlying density of the data. Generative Query
Networks (GQN), for example, predict new viewpoints in
3D scenes given some context observations using a similar
training regime to NPs (Eslami et al., 2018). As such, NPs
can be seen as a generalisation of GQN to few-shot predic-
tion tasks beyond scene understanding, such as regression
and classification. Another way of carrying out few-shot
density estimation is by updating existing models like Pix-
elCNN (van den Oord et al., 2016) and augmenting them
with attention mechanisms (Reed et al., 2017) or including
a memory unit in a VAE model (Bornschein et al., 2017).
Another successful latent variable approach is to explicitly
condition on some context during inference (J. Rezende
et al., 2016). Given the generative nature of these models
they are usually applied to image generation tasks, but mod-
els that include a conditioning class-variable can be used for
classification as well.
Classification itself is another common task in meta-
learning. Few-shot classification algorithms usually rely
on some distance metric in feature space to compare target
images to the observations provided (Koch et al., 2015),
(Santoro et al., 2016). Matching networks (Vinyals et al.,
2016; Bartunov & Vetrov, 2016) are closely related to CNPs.
In their case features of samples are compared with target
features using an attention kernel. At a higher level one can
interpret this model as a CNP where the aggregator is just
the concatenation over all input samples and the decoder g
contains an explicitly defined distance kernel. In this sense
matching networks are closer to GPs than to CNPs, since
they require the specification of a distance kernel that CNPs
learn from the data instead. In addition, as MNs carry out all-
to-all comparisons they scale with O(n ⇥ m), although they
other generative models the neural statistician learns to esti-
mate the density of the observed data but does not allow for
targeted sampling at what we have been referring to as input
positions xi. Instead, one can only generate i.i.d. samples
from the estimated density. Finally, the latent variant of
CNP can also be seen as an approximated amortized version
of Bayesian DL (Gal & Ghahramani, 2016; Blundell et al.,
2015; Louizos et al., 2017; Louizos & Welling, 2017)
4. Experimental Results
Figure 2. 1-D Regression. Regression results on a 1-D curve
(black line) using 5 (left column) and 50 (right column) context
points (black dots). The first two rows show the predicted mean
and variance for the regression of a single underlying kernel for
GPs (red) and CNPs (blue). The bottom row shows the predictions
of CNPs for a curve with switching kernel parameters.

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Conditional Neural Processes
Figure 3. Pixel-wise image regression on MNIST. Left: Two examples of image regression with v
provide the model with 1, 40, 200 and 728 context points (top row) and query the entire image. T
variance (bottom row) at each pixel position is shown for each of the context images. Right: mode
observations that are either chosen at random (blue) or by selecting the pixel with the highest varia
4.1. Function Regression
As a first experiment we test CNP on the classical 1D re-
gression task that is used as a common baseline for GPs.
We generate two different datasets that consist of functions
generated from a GP with an exponential kernel. In the first
dataset we use a kernel with fixed parameters, and in the
second dataset the function switches at some random point
on the real line between two functions each sampled with
different kernel parameters.
At every training step we sample a curve from the GP, select
a subset of n points (xi, yi) as observations, and a subset of
points (xt, yt) as target points. Using the model described
that increases in accuracy
increases.
Furthermore the model ach
on the switching kernel t
is not trivial for GPs whe
change the dataset used fo
4.2. Image Completion
We consider image comp
functions in either f : [0,
or f : [0, 1]2
! [0, 1]3
fo
2D pixel coordinates nor
Conditional Neural Processes
Figure 3. Pixel-wise image regression on MNIST. Left: Two examples of image regression with varying numbers of observations. We
provide the model with 1, 40, 200 and 728 context points (top row) and query the entire image. The resulting mean (middle row) and
variance (bottom row) at each pixel position is shown for each of the context images. Right: model accuracy with increasing number of
observations that are either chosen at random (blue) or by selecting the pixel with the highest variance (red).
4.1. Function Regression
As a first experiment we test CNP on the classical 1D re-
that increases in accuracy as the number of context points
increases.

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Conditional Neural Processes
4.2.1. MNIST
We first test CNP on the MNIST dataset and use the test
set to evaluate its performance. As shown in Figure 3a the
model learns to make good predictions of the underlying
digit even for a small number of context points. Crucially,
when conditioned only on one non-informative context point
(e.g. a black pixel on the edge) the model’s prediction corre-
sponds to the average over all MNIST digits. As the number
of context points increases the predictions become more
similar to the underlying ground truth. This demonstrates
the model’s capacity to extract dataset specific prior knowl-
edge. It is worth mentioning that even with a complete set
of observations the model does not achieve pixel-perfect
reconstruction, as we have a bottleneck at the representation
level.
Since this implementation of CNP returns factored outputs,
the best prediction it can produce given limited context
information is to average over all possible predictions that
agree with the context. An alternative to this is to add
latent variables in the model such that they can be sampled
conditioned on the context to produce predictions with high
probability in the data distribution. We consider this model
later in section 4.2.3.
An important aspect of the model is its ability to estimate
the uncertainty of the prediction. As shown in the bottom
row of Figure 3a, as we add more observations, the variance
shifts from being almost uniformly spread over the digit
positions to being localized around areas that are specific
to the underlying digit, specifically its edges. Being able to
model the uncertainty given some context can be helpful for
many tasks. One example is active exploration, where the
Figure 4. Pixel-wise image completion on CelebA. Two exam-
ples of CelebA image regression with varying numbers of obser-
vations. We provide the model with 1, 10, 100 and 1000 context
points (top row) and query the entire image. The resulting mean
(middle row) and variance (bottom row) at each pixel position is
shown for each of the context images.
An important aspect of CNPs demonstrated in Figure 5, is
its flexibility not only in the number of observations and
targets it receives but also with regards to their input values.
It is interesting to compare this property to GPs on one hand,
and to trained generative models (van den Oord et al., 2016;
Gregor et al., 2015) on the other hand.
The first type of flexibility can be seen when conditioning on
subsets that the model has not encountered during training.
Consider conditioning the model on one half of the image,
fox example. This forces the model to not only predict pixel
Figure 5. Flexible image completion. In contrast to standard con-
ditional models, CNPs can be directly conditioned on observed
pixels in arbitrary patterns, even ones which were never seen in
the training set. Similarly, the model can predict values for pixel
coordinates that were never included in the training set, like sub-
pixel values in different resolutions. The dotted white lines were
added for clarity after generation.
and prediction task, and has the capacity to extract domain
knowledge from a training set.
We compare CNPs quantitatively to two related models:
kNNs and GPs. As shown in Table 4.2.3 CNPs outperform
the latter when number of context points is small (empiri-
cally when half of the image or less is provided as context).
When the majority of the image is given as context exact
methods like GPs and kNN will perform better. From the ta-
ble we can also see that the order in which the context points
are provided is less important for CNPs, since providing the
context points in order from top to bottom still results in
good performance. Both insights point to the fact that CNPs
learn a data-specific ‘prior’ that will generate good samples
even when the number of context points is very small.
4.2.3. LATENT VARIABLE MODEL
The main model we use throughout this paper is a factored
model that predicts the mean and variance of the target
outputs. Although we have shown that the mean is by itself
a useful prediction, and that the variance is a good way to
capture the uncertainty, this factored model prevents us from
Figure 6. Image completion with a latent variable model. The
latent variables capture the global uncertainty, allowing the sam-
pling of different coherent images which conform to the obser-
vations. As the number of observations increases, uncertainty is
reduced and the samples converge to a single estimate.
servations and targets. This forms a multivariate Gaussian
which can be used to coherently draw samples. In order to
maintain this property in a trained model, one approach is to
train the model to predict a GP kernel (Wilson et al., 2016).
However the difficulty is the need to back-propagate through
the sampling which involves a large matrix inversion (or
some approximation of it).
Random Context Ordered Context
# 10 100 1000 10 100 1000
kNN 0.215 0.052 0.007 0.370 0.273 0.007
GP 0.247 0.137 0.001 0.257 0.220 0.002
CNP 0.039 0.016 0.009 0.057 0.047 0.021
Table 1. Pixel-wise mean squared error for all of the pixels in the
image completion task on the CelebA data set with increasing
number of context points (10, 100, 1000). The context points are
chosen either at random or ordered from the top-left corner to the
bottom-right. With fewer context points CNPs outperform kNNs
and GPs. In addition CNPs perform well regardless of the order of
the context points, whereas GPs and kNNs perform worse when
the context is ordered.
In contrast, the approach we use is to simply add latent
variables z to our decoder g, allowing our model to capture

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s gnC 8 .. I N
Conditional
Figure 5. Flexible image completion. In contrast to standard con
ditional models, CNPs can be directly conditioned on observed
pixels in arbitrary patterns, even ones which were never seen in
the training set. Similarly, the model can predict values for pixel
coordinates that were never included in the training set, like sub
pixel values in different resolutions. The dotted white lines were
added for clarity after generation.
and prediction task, and has the capacity to extract domain
knowledge from a training set.
Conditional Neural Processes
Figure 5. Flexible image completion. In contrast to standard con-
ditional models, CNPs can be directly conditioned on observed
pixels in arbitrary patterns, even ones which were never seen in
the training set. Similarly, the model can predict values for pixel
coordinates that were never included in the training set, like sub-
pixel values in different resolutions. The dotted white lines were
added for clarity after generation.
and prediction task, and has the capacity to extract domain
knowledge from a training set.
We compare CNPs quantitatively to two related models:
kNNs and GPs. As shown in Table 4.2.3 CNPs outperform
the latter when number of context points is small (empiri-
cally when half of the image or less is provided as context).
Figure 6. Image completi
latent variables capture th
pling of different coheren
vations. As the number o
reduced and the samples c
servations and targets.
which can be used to co
maintain this property in
train the model to predi
However the difficulty is
the sampling which inv
some approximation of
Random Co
# 10 100

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Conditional Neural Processes
a conditional Gaussian prior p(z|O) that is conditioned on
the observations, and a Gaussian posterior p(z|O, T) that is
also conditioned on the target points.
We apply this model to MNIST and CelebA (Figure 6).
We use the same models as before, but we concatenate the
representation r to a vector of latent variables z of size 64
(for CelebA we use bigger models where the sizes of r and
z are 1024 and 128 respectively). For both the prior and
posterior models, we use three layered MLPs and average
their outputs. We emphasize that the difference between the
prior and posterior is that the prior only sees the observed
pixels, while the posterior sees both the observed and the
target pixels. When sampling from this model with a small
number of observed pixels, we get coherent samples and
we see that the variability of the datasets is captured. As
the model is conditioned on more and more observations,
the variability of the samples drops and they eventually
converge to a single possibility.
4.3. Classification
Finally, we apply the model to one-shot classification using
the Omniglot dataset (Lake et al., 2015) (see Figure 7 for an
overview of the task). This dataset consists of 1,623 classes
of characters from 50 different alphabets. Each class has
only 20 examples and as such this dataset is particularly
suitable for few-shot learning algorithms. As in (Vinyals
et al., 2016) we use 1,200 randomly selected classes as
our training set and the remainder as our testing data set.
In addition we augment the dataset following the protocol
described in (Santoro et al., 2016). This includes cropping
the image from 32 ⇥ 32 to 28 ⇥ 28, applying small random
translations and rotations to the inputs, and also increasing
the number of classes by rotating every character by 90
degrees and defining that to be a new class. We generate
PredictObserve Aggregate
r5r4r3
ahhh
r
Class
E
Class
D
Class
C g gg
r2r1
hh
Class
B
Class
A
A
B
C
E
D
0 1 0 1 0 1
Figure 7. One-shot Omniglot classification. At test time the
model is presented with a labelled example for each class, and
outputs the classification probabilities of a new unlabelled example.
The model uncertainty grows when the new example comes from
an un-observed class.
5-way Acc 20-way Acc Runtime
1-shot 5-shot 1-shot 5-shot
MANN 82.8% 94.9% - - O(nm)
MN 98.1% 98.9% 93.8% 98.5% O(nm)
CNP 95.3% 98.5% 89.9% 96.8% O(n + m)
Table 2. Classification results on Omniglot. Results on the same
task for MANN (Santoro et al., 2016), and matching networks
(MN) (Vinyals et al., 2016) and CNP.
using a significantly simpler architecture (three convolu-
tional layers for the encoder and a three-layer MLP for the
decoder) and with a lower runtime of O(n + m) at test time
as opposed to O(nm).
Conditional Neural Processes
a conditional Gaussian prior p(z|O) that is conditioned on
the observations, and a Gaussian posterior p(z|O, T) that is
also conditioned on the target points.
We apply this model to MNIST and CelebA (Figure 6).
We use the same models as before, but we concatenate the
representation r to a vector of latent variables z of size 64
(for CelebA we use bigger models where the sizes of r and
z are 1024 and 128 respectively). For both the prior and
posterior models, we use three layered MLPs and average
their outputs. We emphasize that the difference between the
prior and posterior is that the prior only sees the observed
pixels, while the posterior sees both the observed and the
target pixels. When sampling from this model with a small
number of observed pixels, we get coherent samples and
we see that the variability of the datasets is captured. As
the model is conditioned on more and more observations,
the variability of the samples drops and they eventually
converge to a single possibility.
4.3. Classification
Finally, we apply the model to one-shot classification using
the Omniglot dataset (Lake et al., 2015) (see Figure 7 for an
overview of the task). This dataset consists of 1,623 classes
of characters from 50 different alphabets. Each class has
only 20 examples and as such this dataset is particularly
suitable for few-shot learning algorithms. As in (Vinyals
et al., 2016) we use 1,200 randomly selected classes as
our training set and the remainder as our testing data set.
In addition we augment the dataset following the protocol
described in (Santoro et al., 2016). This includes cropping
the image from 32 ⇥ 32 to 28 ⇥ 28, applying small random
translations and rotations to the inputs, and also increasing
the number of classes by rotating every character by 90
degrees and defining that to be a new class. We generate
PredictObserve Aggregate
r5r4r3
ahhh
r
Class
E
Class
D
Class
C g gg
r2r1
hh
Class
B
Class
A
A
B
C
E
D
0 1 0 1 0 1
Figure 7. One-shot Omniglot classification. At test time the
model is presented with a labelled example for each class, and
outputs the classification probabilities of a new unlabelled example.
The model uncertainty grows when the new example comes from
an un-observed class.
5-way Acc 20-way Acc Runtime
1-shot 5-shot 1-shot 5-shot
MANN 82.8% 94.9% - - O(nm)
MN 98.1% 98.9% 93.8% 98.5% O(nm)
CNP 95.3% 98.5% 89.9% 96.8% O(n + m)
Table 2. Classification results on Omniglot. Results on the same
task for MANN (Santoro et al., 2016), and matching networks
(MN) (Vinyals et al., 2016) and CNP.
using a significantly simpler architecture (three convolu-
tional layers for the encoder and a three-layer MLP for the
decoder) and with a lower runtime of O(n + m) at test time
as opposed to O(nm).

30. • 2 . 0 2 : ,+ 8 1
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Neural Processes
xC yT
C T
z
xTyC
(a) Graphical model
yC
xC h
rC ra z yT
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r
C T
Generation Inference
(b) Computational diagram
Figure 1. Neural process model. (a) Graphical model of a neural process. x and y correspond to the data where y = f(x). C and T
are the number of context points and target points respectively and z is the global latent variable. A grey background indicates that the
variable is observed. (b) Diagram of our neural process implementation. Variables in circles correspond to the variables of the graphical
ditional stochastic process Q✓ that defines distributions over
f(x) for inputs x 2 T. ✓ is the real vector of all parame-
ters defining Q. Inheriting from the properties of stochastic
processes, we assume that Q✓ is invariant to permutations
of O and T. If O0
, T0
are permutations of O and T, re-
spectively, then Q✓(f(T) | O, T) = Q✓(f(T0
) | O, T0
) =
Q✓(f(T) | O0
, T). In this work, we generally enforce per-
mutation invariance with respect to T by assuming a fac-
tored structure. Specifically, we consider Q✓s that factor
Q✓(f(T) | O, T) =
Q
x2T Q✓(f(x) | O, x). In the absence
of assumptions on output space Y , this is the easiest way to
ensure a valid stochastic process. Still, this framework can
be extended to non-factored distributions, we consider such
a model in the experimental section.
The defining characteristic of a CNP is that it conditions on
O via an embedding of fixed dimensionality. In more detail,
we use the following architecture,
ri = h✓(xi, yi) 8(xi, yi) 2 O (1)
r = r1 r2 . . . rn 1 rn (2)
i = g✓(xi, r) 8(xi) 2 T (3)
where h✓ : X ⇥Y ! Rd
and g✓ : X ⇥Rd
! Re
are neural
networks, is a commutative operation that takes elements
in Rd
and maps them into a single element of Rd
, and i are
parameters for Q✓(f(xi) | O, xi) = Q(f(xi) | i). Depend-
ing on the task the model learns to parametrize a different
output distribution. This architecture ensures permutation
invariance and O(n + m) scaling for conditional prediction.
We note that, since r1 . . . rn can be computed in O(1)
from r1 . . . rn 1, this architecture supports streaming
observations with minimal overhead.
For regression tasks we use i to parametrize the mean and
variance i = (µi, 2
i ) of a Gaussian distribution N(µi, 2
i )
for every xi 2 T. For classification tasks i parametrizes
and unobserved values. In practice, we take Monte Carlo
estimates of the gradient of this loss by sampling f and N.
This approach shifts the burden of imposing prior knowl-
edge from an analytic prior to empirical data. This has
the advantage of liberating a practitioner from having to
specify an analytic form for the prior, which is ultimately
intended to summarize their empirical experience. Still, we
emphasize that the Q✓ are not necessarily a consistent set of
conditionals for all observation sets, and the training routine
does not guarantee that. In summary,
1. A CNP is a conditional distribution over functions
trained to model the empirical conditional distributions
of functions f ⇠ P.
2. A CNP is permutation invariant in O and T.
3. A CNP is scalable, achieving a running time complex-
ity of O(n + m) for making m predictions with n
observations.
Within this specification of the model there are still some
aspects that can be modified to suit specific requirements.
The exact implementation of h, for example, can be adapted
to the data type. For low dimensional data the encoder
can be implemented as an MLP, whereas for inputs with
larger dimensions and spatial correlations it can also include
convolutions. Finally, in the setup described the model is not
able to produce any coherent samples, as it learns to model
only a factored prediction of the mean and the variances,
disregarding the covariance between target points. This
is a result of this particular implementation of the model.
One way we can obtain coherent samples is by introducing
a latent variable that we can sample from. We carry out
some proof-of-concept experiments on such a model in
section 4.2.3.
.
ditional stochastic process Q✓ that defines distributions over
f(x) for inputs x 2 T. ✓ is the real vector of all parame-
ters defining Q. Inheriting from the properties of stochastic
processes, we assume that Q✓ is invariant to permutations
of O and T. If O0
, T0
are permutations of O and T, re-
spectively, then Q✓(f(T) | O, T) = Q✓(f(T0
) | O, T0
) =
Q✓(f(T) | O0
, T). In this work, we generally enforce per-
mutation invariance with respect to T by assuming a fac-
tored structure. Specifically, we consider Q✓s that factor
Q✓(f(T) | O, T) =
Q
x2T Q✓(f(x) | O, x). In the absence
of assumptions on output space Y , this is the easiest way to
ensure a valid stochastic process. Still, this framework can
be extended to non-factored distributions, we consider such
a model in the experimental section.
The defining characteristic of a CNP is that it conditions on
O via an embedding of fixed dimensionality. In more detail,
we use the following architecture,
ri = h✓(xi, yi) 8(xi, yi) 2 O (1)
r = r1 r2 . . . rn 1 rn (2)
i = g✓(xi, r) 8(xi) 2 T (3)
where h✓ : X ⇥Y ! Rd
and g✓ : X ⇥Rd
! Re
are neural
networks, is a commutative operation that takes elements
in Rd
and maps them into a single element of Rd
, and i are
parameters for Q✓(f(xi) | O, xi) = Q(f(xi) | i). Depend-
ing on the task the model learns to parametrize a different
output distribution. This architecture ensures permutation
invariance and O(n + m) scaling for conditional prediction.
We note that, since r1 . . . rn can be computed in O(1)
from r1 . . . rn 1, this architecture supports streaming
observations with minimal overhead.
For regression tasks we use i to parametrize the mean and
variance i = (µi, 2
i ) of a Gaussian distribution N(µi, 2
i )
for every xi 2 T. For classification tasks i parametrizes
and unobserved values. In practice, we
estimates of the gradient of this loss by
This approach shifts the burden of imp
edge from an analytic prior to empiri
the advantage of liberating a practition
specify an analytic form for the prior, w
intended to summarize their empirical e
emphasize that the Q✓ are not necessaril
conditionals for all observation sets, and
does not guarantee that. In summary,
1. A CNP is a conditional distribut
trained to model the empirical cond
of functions f ⇠ P.
2. A CNP is permutation invariant in
3. A CNP is scalable, achieving a run
ity of O(n + m) for making m
observations.
Within this specification of the model t
aspects that can be modified to suit spe
The exact implementation of h, for exam
to the data type. For low dimensiona
can be implemented as an MLP, wher
larger dimensions and spatial correlation
convolutions. Finally, in the setup describ
able to produce any coherent samples, a
only a factored prediction of the mean
disregarding the covariance between t
is a result of this particular implement
One way we can obtain coherent sample
a latent variable that we can sample fr
some proof-of-concept experiments o
section 4.2.3.
.
Specifically, given a set of observations O, a CNP is a con-
ditional stochastic process Q✓ that defines distributions over
f(x) for inputs x 2 T. ✓ is the real vector of all parame-
ters defining Q. Inheriting from the properties of stochastic
processes, we assume that Q✓ is invariant to permutations
of O and T. If O0
, T0
are permutations of O and T, re-
spectively, then Q✓(f(T) | O, T) = Q✓(f(T0
) | O, T0
) =
Q✓(f(T) | O0
, T). In this work, we generally enforce per-
mutation invariance with respect to T by assuming a fac-
tored structure. Specifically, we consider Q✓s that factor
Q✓(f(T) | O, T) =
Q
x2T Q✓(f(x) | O, x). In the absence
of assumptions on output space Y , this is the easiest way to
ensure a valid stochastic process. Still, this framework can
be extended to non-factored distributions, we consider such
a model in the experimental section.
The defining characteristic of a CNP is that it conditions on
O via an embedding of fixed dimensionality. In more detail,
we use the following architecture,
ri = h✓(xi, yi) 8(xi, yi) 2 O (1)
r = r1 r2 . . . rn 1 rn (2)
i = g✓(xi, r) 8(xi) 2 T (3)
where h✓ : X ⇥Y ! Rd
and g✓ : X ⇥Rd
! Re
are neural
networks, is a commutative operation that takes elements
in Rd
and maps them into a single element of Rd
, and i are
parameters for Q✓(f(xi) | O, xi) = Q(f(xi) | i). Depend-
ing on the task the model learns to parametrize a different
output distribution. This architecture ensures permutation
invariance and O(n + m) scaling for conditional prediction.
We note that, since r1 . . . rn can be computed in O(1)
from r1 . . . rn 1, this architecture supports streaming
observations with minimal overhead.
For regression tasks we use i to parametrize the mean and
variance i = (µi, 2
) of a Gaussian distribution N(µi, 2
)
Thus, the targets it scores Q✓ on include
and unobserved values. In practice, we
estimates of the gradient of this loss by
This approach shifts the burden of imp
edge from an analytic prior to empiri
the advantage of liberating a practition
specify an analytic form for the prior, w
intended to summarize their empirical e
emphasize that the Q✓ are not necessaril
conditionals for all observation sets, and
does not guarantee that. In summary,
1. A CNP is a conditional distribut
trained to model the empirical cond
of functions f ⇠ P.
2. A CNP is permutation invariant in
3. A CNP is scalable, achieving a run
ity of O(n + m) for making m
observations.
Within this specification of the model t
aspects that can be modified to suit spe
The exact implementation of h, for exam
to the data type. For low dimensiona
can be implemented as an MLP, wher
larger dimensions and spatial correlation
convolutions. Finally, in the setup describ
able to produce any coherent samples, a
only a factored prediction of the mean
disregarding the covariance between t
is a result of this particular implement
One way we can obtain coherent sample
a latent variable that we can sample fr
some proof-of-concept experiments o
a less informed posterior of the underlying function. This
formulation makes it clear that the posterior given a subset
of the context points will serve as the prior when additional
context points are included. By using this setup, and train-
ing with different sizes of context, we encourage the learned
model to be flexible with regards to the number and position
of the context points.
2.4. The Neural process model
In our implementation of NPs we accommodate for two ad-
ditional desiderata: invariance to the order of context points
and computational efficiency. The resulting model can be
boiled down to three core components (see Figure 1b):
• An encoder h from input space into representation
space that takes in pairs of (x, y)i context values and
produces a representation ri = h((x, y)i) for each of
the pairs. We parameterise h as a neural network.
• An aggregator a that summarises the encoded inputs.
We are interested in obtaining a single order-invariant
global representation r that parameterises the latent dis-
tribution z ⇠ N(µ(r), I (r)). The simplest operation
that ensures order-invariance and works well in prac-
tice is the mean function r = a(ri) = 1
n
Pn
i=1 ri. Cru-
cially, the aggregator reduces the runtime to O(n + m)
where n and m are the number of context and target
points respectively.
• A conditional decoder g that takes as input the sam-
pled global latent variable z as well as the new target
locations xT and outputs the predictions ˆyT for the
corresponding values of f(xT ) = yT .
3. Related work
3.1. Conditional neural processes
Neural Processes (NPs) are a generalisation of Conditional
lack a latent variable that allows for global sampl
Figure 2c for a diagram of the model). As a resul
are unable to produce different function samples
same context data, which can be important if model
uncertainty is desirable. It is worth mentioning that
inal CNP formulation did include experiments with
variable in addition to the deterministic connectio
ever, given the deterministic connections to the p
variables, the role of the global latent variable is n
In contrast, NPs constitute a more clear-cut gener
of the original deterministic CNP with stronger p
to other latent variable models and approximate B
methods. These parallels allow us to compare ou
to a wide range of related research areas in the fo
sections.
Finally, NPs and CNPs themselves can be seen a
alizations of recently published generative query n
(GQN) which apply a similar training procedure to
new viewpoints in 3D scenes given some context
tions (Eslami et al., 2018). Consistent GQN (CG
an extension of GQN that focuses on generating co
samples and is thus also closely related to NPs (Kum
2018).
3.2. Gaussian processes
We start by considering models that, like NPs, li
spectrum between neural networks (NNs) and G
processes (GPs). Algorithms on the NN end of the s
fit a single function that they learn from a very large
of data directly. GPs on the other hand can rep
distribution over a family of functions, which is con
by an assumption on the functional form of the co
between two points.
Scattered across this spectrum, we can place recent
that has combined ideas from Bayesian non-para
with neural networks. Methods like (Calandra et a
Huang et al., 2015) remain fairly close to the GPs, b
the prior on the context. As such this prior is equivalent to
a less informed posterior of the underlying function. This
formulation makes it clear that the posterior given a subset
of the context points will serve as the prior when additional
context points are included. By using this setup, and train-
ing with different sizes of context, we encourage the learned
model to be flexible with regards to the number and position
of the context points.
2.4. The Neural process model
In our implementation of NPs we accommodate for two ad-
ditional desiderata: invariance to the order of context points
and computational efficiency. The resulting model can be
boiled down to three core components (see Figure 1b):
• An encoder h from input space into representation
space that takes in pairs of (x, y)i context values and
produces a representation ri = h((x, y)i) for each of
the pairs. We parameterise h as a neural network.
• An aggregator a that summarises the encoded inputs.
We are interested in obtaining a single order-invariant
global representation r that parameterises the latent dis-
tribution z ⇠ N(µ(r), I (r)). The simplest operation
that ensures order-invariance and works well in prac-
tice is the mean function r = a(ri) = 1
n
Pn
i=1 ri. Cru-
cially, the aggregator reduces the runtime to O(n + m)
where n and m are the number of context and target
points respectively.
• A conditional decoder g that takes as input the sam-
pled global latent variable z as well as the new target
locations xT and outputs the predictions ˆyT for the
corresponding values of f(xT ) = yT .
3. Related work
3.1. Conditional neural processes
a large part of the motivation behind n
lack a latent variable that allows for g
Figure 2c for a diagram of the model)
are unable to produce different funct
same context data, which can be import
uncertainty is desirable. It is worth men
inal CNP formulation did include exper
variable in addition to the deterministi
ever, given the deterministic connectio
variables, the role of the global latent
In contrast, NPs constitute a more clea
of the original deterministic CNP wit
to other latent variable models and app
methods. These parallels allow us to
to a wide range of related research are
sections.
Finally, NPs and CNPs themselves ca
alizations of recently published genera
(GQN) which apply a similar training p
new viewpoints in 3D scenes given so
tions (Eslami et al., 2018). Consisten
an extension of GQN that focuses on g
samples and is thus also closely related
2018).
3.2. Gaussian processes
We start by considering models that,
spectrum between neural networks (N
processes (GPs). Algorithms on the NN
fit a single function that they learn from
of data directly. GPs on the other ha
distribution over a family of functions,
by an assumption on the functional for
between two points.
Scattered across this spectrum, we can p
that has combined ideas from Bayesi
Neural Processes
yC
zT
yT
C T
z
Neural statistician
xC yT
C T
xTyC
(c) Conditional neural process
xC yT
C T
z
xTyC
(d) Neural process
elated models (a-c) and of the neural process (d). Gray shading indicates the variable is observed. C
T for target variables i.e. the variables to predict given C.
. . ]W b b
0 2 : ,+ 8 1
.
0 2 : ,+ 8 1

31. • 8C 8 8 2 =8 + , 1 3
– l hp
• u
– o t I C= 8 := D [ r d
P MLT
– l y
• u
– TI o o b ab M] ] y
W kc n T I
– o t I b ab M] y P
sN ei C= 0 C G I IL
Neural Processes
10 100 300 784
Number of context points
ContextSample1Sample2Sample3
15 30 90 1024
Number of context points
ContextSample1Sample2Sample3
Figure 4. Pixel-wise regression on MNIST and CelebA The diagram on the left visualises how pixel-wise image completion can be
framed as a 2-D regression task where f(pixel coordinates) = pixel brightness. The figures to the right of the diagram show the results on
image completion for MNIST and CelebA. The images on the top correspond to the context points provided to the model. For better
clarity the unobserved pixels have been coloured blue for the MNIST images and white for CelebA. Each of the rows corresponds to a

32. • C /C DD D C + , 3 C D
– le G 2 D D :
•
– iIg u [ w
– 2 D D : ST M d
•
– / / PR t STle drSR s ophm PR s
RWa N] PR r d
– 0 8 1 C d PR / / L r d PR d] R L
– / [ C 8 C ekIkonIg cR d PTNeural Processes
Figure 5. Thompson sampling with neural processes on a 1-D objective function. The plots show the optimisation process over five
iterations. Each prediction function (blue) is drawn by sampling a latent variable conditioned on an incresing number of context points
(black circles). The underlying ground truth function is depicted as a black dotted line. The red triangle indicates the next evaluation point
which corresponds to the minimum value of the sampled NP curve. The red circle in the following iteration corresponds to this evaluation
point with its underlying ground truth value that serves as a new context point to the NP.
Neural Processes
Figure 5. Thompson sampling with neural processes on a 1-D objective function. The plots show the optimisation process over five
iterations. Each prediction function (blue) is drawn by sampling a latent variable conditioned on an incresing number of context points
(black circles). The underlying ground truth function is depicted as a black dotted line. The red triangle indicates the next evaluation point
which corresponds to the minimum value of the sampled NP curve. The red circle in the following iteration corresponds to this evaluation
point with its underlying ground truth value that serves as a new context point to the NP.
4.3. Black-box optimisation with Thompson sampling
To showcase the utility of sampling entire consistent trajecto-
ries we apply neural processes to Bayesian optimisation on
1-D function using Thompson sampling (Thompson, 1933).
Thompson sampling (also known as randomised proba-
bility matching) is an approach to tackle the exploration-
exploitation dilemma by maintaining a posterior distribution
over model parameters. A decision is taken by drawing a
sample of model parameters and acting greedily under the
resulting policy. The posterior distribution is then updated
and the process is repeated. Despite its simplicity, Thomp-
son sampling has been shown to be highly effective both
empirically and in theory. It is commonly applied to black
box optimisation and multi-armed bandit problems (e.g.
Agrawal & Goyal, 2012; Shahriari et al., 2016).
function evaluations increases.
Neural process Gaussian process Random Search
0.26 0.14 1.00
Table 1. Bayesian Optimisation using Thompson sampling.
Average number of optimisation steps needed to reach the global
minimum of a 1-D function generated by a Gaussian process. The
values are normalised by the number of steps taken using random
search. The performance of the Gaussian process with the correct
kernel constitutes an upper bound on performance.
4.4. Contextual bandits

33. • e n
– u o a
– t k G
• o C l F
–
• F lC N
• o C G G
–
• / i C ikG a a C k
P
• / 3 /
– r r bF N Fv r GwN :
:
– N F G

34. • /
– 2 NFLO 8 S FS L" /ON JSJON L FT L ODFRRFR" 4N 4/87 "
– 2 NFLO 8 S FS L" FT L ODFRRFR" 4N 4/87 VO KRIOP S DK "
• 8FS 7F NJNH
– J DIJN N 3THO 7 ODIFLLF" :PSJMJY SJON R MO FL GO GFV RIOS LF NJNH" 4N 4/7 "
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– 1JNN /IFLRF JFSF .CCFFL N F HF 7F JNF" 8O FL HNORSJD MFS LF NJNH GO G RS PS SJON OG FFP
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• 2 TRRJ N ODFRRFR
– JLRON .N FV 2O ON FS L" 0FFP KF NFL LF NJNH" . SJGJDJ L 4NSFLLJHFNDF N S SJRSJDR" "
– 6FN LL .LFW N A JN 2 L" I S TNDF S JNSJFR O VF NFF JN C FRJ N FFP LF NJNH GO DOMPTSF
JRJON-" 4N 4 VO KRIOP S DK "
– 7FF FIOON FS L" 0FFP NFT L NFSVO KR R H TRRJ N P ODFRRFR" 4N 4/7 "