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Complex Models for Big Data

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Data Science Research Center
Data Science Research Center

Max Welling (http://www.ics.uci.edu/~welling/) describes the how big data, massive simulation and advanced models go together to help us start solving challenging problems. He also describes his links to other computer science disciplines within the DSRC.

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DS RC Data Science Research Center Complex Models for Big Data Max Welling UvA
DS RC The Four Paradigms We have added big data to computer simulation, experiment and theory. Not replaced it…
DS RC Big Simulation Computer simulations have become increasingly complex (e.g. weather, earthquake models) The Computational Wall: If a model has hundreds of parameters, how can we: 1) Find the parameter values that match the observations best? 2) Determine if we underfit (model too simple) or overfit (model too complex)? 3) Compare two models?
DS RC Parameter Inference Parameter Update Parameters Simulation Observations
DS RC Challenge I The “posterior probability” in closed form. can not be computed Solution: Markov Chain Monte Carlo Sampling (MCMC)
DS RC Challenge II We cannot run MCMC because the likelihood is not given in closed form (but rather as a simulation) Solution: Likelihood Free MCMC (or Approximate Bayesian Computation) Run many simulations and compare samples With observations. Source: Csillery, Katalin, et al. "Approximate Bayesian computation (ABC) in practice."Trends in ecology & evolution 25.7 (2010): 410-418.
DS RC Challenge III We need thousands of simulations to infer the posterior (infeasible if every simulation takes a day or so) Ted Meeds If surrogate ~ log(P) with high confidence then use surrogate to draw sample. If not: simulate until enough confidence. Surrogate of log(P) Solution: Learn log(P) using Gaussian Process Surrogate functions (GPS)
D S Two Kinds of Complex Model RC Machine Learning Computational Science Model Capacity “Let the model speak” “Let the data speak”
DS RC 3x Exponential Growth in Machine Learning Computer Power Data Volume Model Capacity
D S Growth in Model Capacity RC 2020-2050 Human Brain (N=+/- 100T) ? Model Capacity over Time 2009: Hinton’s Deep Belief Net (+/- N=10M) 2013: Google/Y! (N=+/- 10B) 1943: First NN (+/- N=10) 1988: NetTalk (+/- N=20K)
D S Deep Learning: Neural Nets Strike R C Back(again) 1970: NN discredited (Minsky & Papert) 2 layers 1943: NN invented (McCulloch & Pitts) -Model Size: 10B parameters -Used by: Yahoo!, Google, Microsoft, Baidu, IBM, Scyfer  1986: Backpropagation (Rumelhart, Hinton & Williams ) 1995: SVM (Vapnik) 3 layers 2009: Deep Learning (Hinton) many layers
DS RC Paradox Why does model capacity grow exponentially? Raw Information: O(N) Predictive Information: log(N) Noise ?
DS RC Big Challenges from Industry Scyfer connects industry to academia: -inspire academia w/ relevant problems -deliver ML products to industry -host student projects -provide employment for our students = VALORISATION What industry needs. What academics are interested in.
DS RC Intelligent Autonomous Systems Lab - UvA Visual Analytics Shimon Whiteson Leo Dorst Business Analytics Decision Theory (Geometric Algebra) Understand and decide (Reinforcement Learning & Planning) Joris Mooij (Causality) Distributed Processing Data Reasoning Knowledge representati on Large Scale Databases Store and process Software Eng. System / Network Eng. Analyze and model Multimedia Retrieval Modeling and simulation Information Retrieval Machine Learning Ben Kröse (Ambient Robotics) Dariu Gavrilla (Human-aware Intelligent Systems) Max Welling (Machine Learning)
DS RC Our Future Need Visual Analytics Shimon Whiteson Leo Dorst Business Analytics Decision Theory (Geometric Algebra) Understand and decide (Reinforcement Learning & Planning) Joris Mooij (Causality) Distributed Processing Data Reasoning Knowledge representati on Large Scale Databases Store and process Software Eng. System / Network Eng. Analyze and model Multimedia Retrieval Modeling and simulation Information Retrieval Machine Learning Ben Kröse (Ambient Robotics) Dariu Gavrilla (Human-aware Intelligent Systems) Max Welling (Machine Learning)
DS RC Questions?

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Complex Models for Big Data

  • 1. DS RC Data Science Research Center Complex Models for Big Data Max Welling UvA
  • 2. DS RC The Four Paradigms We have added big data to computer simulation, experiment and theory. Not replaced it…
  • 3. DS RC Big Simulation Computer simulations have become increasingly complex (e.g. weather, earthquake models) The Computational Wall: If a model has hundreds of parameters, how can we: 1) Find the parameter values that match the observations best? 2) Determine if we underfit (model too simple) or overfit (model too complex)? 3) Compare two models?
  • 5. DS RC Challenge I The “posterior probability” in closed form. can not be computed Solution: Markov Chain Monte Carlo Sampling (MCMC)
  • 6. DS RC Challenge II We cannot run MCMC because the likelihood is not given in closed form (but rather as a simulation) Solution: Likelihood Free MCMC (or Approximate Bayesian Computation) Run many simulations and compare samples With observations. Source: Csillery, Katalin, et al. "Approximate Bayesian computation (ABC) in practice."Trends in ecology & evolution 25.7 (2010): 410-418.
  • 7. DS RC Challenge III We need thousands of simulations to infer the posterior (infeasible if every simulation takes a day or so) Ted Meeds If surrogate ~ log(P) with high confidence then use surrogate to draw sample. If not: simulate until enough confidence. Surrogate of log(P) Solution: Learn log(P) using Gaussian Process Surrogate functions (GPS)
  • 8. D S Two Kinds of Complex Model RC Machine Learning Computational Science Model Capacity “Let the model speak” “Let the data speak”
  • 9. DS RC 3x Exponential Growth in Machine Learning Computer Power Data Volume Model Capacity
  • 10. D S Growth in Model Capacity RC 2020-2050 Human Brain (N=+/- 100T) ? Model Capacity over Time 2009: Hinton’s Deep Belief Net (+/- N=10M) 2013: Google/Y! (N=+/- 10B) 1943: First NN (+/- N=10) 1988: NetTalk (+/- N=20K)
  • 11. D S Deep Learning: Neural Nets Strike R C Back(again) 1970: NN discredited (Minsky & Papert) 2 layers 1943: NN invented (McCulloch & Pitts) -Model Size: 10B parameters -Used by: Yahoo!, Google, Microsoft, Baidu, IBM, Scyfer  1986: Backpropagation (Rumelhart, Hinton & Williams ) 1995: SVM (Vapnik) 3 layers 2009: Deep Learning (Hinton) many layers
  • 12. DS RC Paradox Why does model capacity grow exponentially? Raw Information: O(N) Predictive Information: log(N) Noise ?
  • 13. DS RC Big Challenges from Industry Scyfer connects industry to academia: -inspire academia w/ relevant problems -deliver ML products to industry -host student projects -provide employment for our students = VALORISATION What industry needs. What academics are interested in.
  • 14. DS RC Intelligent Autonomous Systems Lab - UvA Visual Analytics Shimon Whiteson Leo Dorst Business Analytics Decision Theory (Geometric Algebra) Understand and decide (Reinforcement Learning & Planning) Joris Mooij (Causality) Distributed Processing Data Reasoning Knowledge representati on Large Scale Databases Store and process Software Eng. System / Network Eng. Analyze and model Multimedia Retrieval Modeling and simulation Information Retrieval Machine Learning Ben Kröse (Ambient Robotics) Dariu Gavrilla (Human-aware Intelligent Systems) Max Welling (Machine Learning)
  • 15. DS RC Our Future Need Visual Analytics Shimon Whiteson Leo Dorst Business Analytics Decision Theory (Geometric Algebra) Understand and decide (Reinforcement Learning & Planning) Joris Mooij (Causality) Distributed Processing Data Reasoning Knowledge representati on Large Scale Databases Store and process Software Eng. System / Network Eng. Analyze and model Multimedia Retrieval Modeling and simulation Information Retrieval Machine Learning Ben Kröse (Ambient Robotics) Dariu Gavrilla (Human-aware Intelligent Systems) Max Welling (Machine Learning)