Sangwoo Mo

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machine learning deep learning generative model computer vision reinforcement learning meta learning natural language processing gan bayesian generative adversarial network disentangled representation neural network artificial inteligence video representation transformer self-attention domain adaptation domain transfer gaussian process rnn recursive neural network nlp topological data analysis applied mathematics statistics vae uncertainty dropout approximation optimization complexity algorithm model nonparametric topic lda language processing natural multi armed bandit online advertising

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Sangwoo Mo

Brief History of Visual Representation Learning

Learning Visual Representations from Uncurated Data

Hyperbolic Deep Reinforcement Learning

A Unified Framework for Computer Vision Tasks: (Conditional) Generative Model is All You Need

Self-supervised Learning Lecture Note

Deep Learning Theory Seminar (Chap 3, part 2)

Deep Learning Theory Seminar (Chap 1-2, part 1)

Introduction to Diffusion Models

Object-Region Video Transformers

Deep Implicit Layers: Learning Structured Problems with Neural Networks

Learning Theory 101 ...and Towards Learning the Flat Minima

Sharpness-aware minimization (SAM)

Explicit Density Models

Score-Based Generative Modeling through Stochastic Differential Equations

Self-Attention with Linear Complexity

Meta-Learning with Implicit Gradients

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Generative Models for General Audiences

Bayesian Model-Agnostic Meta-Learning

Deep Learning for Natural Language Processing