/paper-notes

Paper reading notes

paper-notes

Paper reading notes. Only notes on checked papers are carefully written. This repo also includes other papers that are not listed here. I actually gitignores the pdf files so some directories are empty.

Core-A

  • Auto-Encoding Variational Bayes (half)
  • Importance Weighted Autoencoders
  • Weight Uncertainty in Neural Networks
  • Categorical Reparameterization with Gumbel-Softmax
  • beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
  • VRNN: A Recurrent Latent Variable Model for Sequential Data
  • Towards Conceptual Compression
  • Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
  • Towards a Neural Statistician
  • Variational Continual Learning
  • Multi-Object Representation Learning with Iterative Variational Inference
  • SQAIR: Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects

Core-B

  • Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks
  • Multi-Object Representation Learning with Iterative Variational Inference
  • Generative Temporal Models with Spatial Memory for Partially Observed Environments
  • Neural Scene Representation and Rendering (+Supplementary Material)
  • Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

Core-C

  • Structured Inference Networks for Nonlinear State Space Models
  • Auto-Encoding Sequential Monte Carlo
  • Deep Variational Reinforcement Learning for POMDPs
  • Neural Discrete Representation Learning
  • Neural Relational Inference for Interacting Systems
  • Amortized Bayesian Meta-Learning
  • Variational Information Maximizing Exploration
  • Variational Memory Addressing in Generative Models
  • The Kanerva Machine: A Generative Distributed Memory
  • Learning Latent Dynamics for Planning from Pixels

Other:

  • NEM/RNEM

  • GENESIS

  • Spatial Broadcast Decoder

  • Semi-supervised Learning with Deep Generative Models

  • Variational Inference with Normalizing Flows

  • DRAW: A Recurrent Neural Network For Image Generation

  • Neural Variational Inference and Learning

  • A simple neural network module for relational reasoning

  • MONet: Unsupervised Scene Decomposition and Representation

  • REASONING ABOUT PHYSICAL INTERACTIONS WITH OBJECT-ORIENTED PREDICTION AND PLANNING

  • Learning to Decompose and Disentangle Representations for Video Prediction

  • Avoiding Latent Variable Collapse with Generative Skip Models

  • MADE: Masked Autoencoder for Distribution Estimation

  • How to train deep variational autoencoders and probabilistic ladder networks.

  • Gradient Estimation Using Stochastic Computation Graphs

  • Stochastic backpropagation and approximate inference in deep generative models.

  • Deep Convolutional Inverse Graphics Network

  • Understanding Disentanglement in beta-VAE

  • Deep autoregressive network

  • Hierarchical variational models.

  • Neural Turing Machine

  • Capsule Net

  • A note on the evaluation of generative models

  • Black box variational inference.

  • Variational Inference for Monte Carlo Objectives

  • Stacked Capsule Autoencoders

  • Tigher Variational bounds are not necessarily better

  • Learning deep representations by mutual information estimation and maximization

  • Unsupervised State Representation Learning in Atari

  • Understanding posterior collapse in VAE

  • Isolating sources of disentanglemnt in VAE

  • Variational memory addressing in generative models

  • Lifelong Generative Modeling

  • unsupervised learning of object key points for perception and control

  • Memorization in Overparameterized Autoencoders

  • Neural ODEs

  • Why GAN can generate real images while VAE cannot?

  • Csgnet: Neural shape parser for constructive solid geometry

  • Peeking into the Future: Predicting Future Person Activities and Locations in Videos

  • Neural Processes

  • COPHY: C OUNTERFACTUAL L EARNING OF P HYSICAL DYNAMICS

  • Variational Inference: A Review for Statisticians

  • Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

  • Learning to see physics via visual de-animation

  • CLEVRER: CoLlision Events for Video REpresentation and Reasoning

  • LEARNING TO DESCRIBE SCENES WITH PROGRAMS

  • Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning

  • Activity forecasting

  • Neural scene de-rendering

  • DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactions

  • Physics 101: Learning Physical Object Properties from Unlabeled Videos

  • A compositional object-based approach to learning physical dynamics (NPE)

  • STOCHASTIC PREDICTION OF MULTI-AGENT INTERACTIONS FROM PARTIAL OBSERVATIONS

  • UNSUPERVISED DISCOVERY OF PARTS, STRUCTURE, AND DYNAMICS

  • Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks

  • A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

  • Neural relational inference for interacting systems

  • Deep State-Space Models in Multi-Agent Systems

  • Fixing a broken ELBO

  • Interaction networks for learning about objects, relations and physics.

  • Compositional Video Prediction

  • Object-centric Forward Modeling for Model Predictive Control

  • Advances in variational inference

  • Visual interaction networks: Learning a physics simulator from video

  • Data-efficient model-based rl through unsupervised object discovery and curiosity-driven exploration

  • Faster attend-infer-repeat with tractable probabilistic models

  • Graph neural networks: A review of methods and applications.

  • RSQAIR

  • Factorized-VAE

  • Multivariate Time Series Imputation with Variational Autoencoders

  • STOCHASTIC VARIATIONAL VIDEO PREDICTION

  • Stochastic video generation with a learned prior.

  • Disentangled Sequential Autoencoder

  • THE VARIATIONAL GAUSSIAN PROCESS

  • Black Box Variational Inference

  • Tighter Variational Bounds are Not Necessarily Better

  • Isolating Sources of Disentanglement in Variational Autoencoders

  • REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models

  • On the Importance of Learning Aggregate Posteriors inMultimodal Variational Autoencoders

  • Invertible Residual Networks

  • Residual Flows for Invertible Generative Modeling

  • Explaining Image Classifiers by Counterfactual Generation

  • Isolating Souces of Disentanglement in Variational Autoencoders

  • Reinterpreting importance-weighted autoencoders

  • Inference Suboptimality in Variational Autoencoders

  • Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives

  • #### Design Motifs for Probabilistic Generative Design

  • Continual Unsupervised Representation Learning

  • Mathematical Reasoning in Latent Space

  • Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement.

  • Unreproducible Research is Reproducible

  • An exploration of dropout with LSTMs

  • On Causal and Anticausal Learning

  • End-to-End Robotic Reinforcement Learning without Reward Engineering

  • Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?

  • From Reinforcement Learning to Deep Reinforcement Learning: An Overview

  • A Variational Inequality Perspective on Generative Adversarial Nets

  • Normalizing Flows for Probabilistic Modeling and Inference

  • Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations

  • A Primer in BERTology: What we know about how BERT works

  • Between MDPs and semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning

  • Deep Infomax

  • DLRLSS, Yoshua Bengio, AGI and RNN

  • Don't Blame the Elbo! A Linear VAE Perspective on Posterior Collapse

  • Meta-Learning without Memorization

  • Unsupervised Learning of Object Keypoints for Perception and Control

  • VARIATIONAL STATE ENCODING AS INTRINSIC MOTIVATION IN REINFORCEMENT LEARNING

  • SuperDyna, Richard Sutton

  • Attention is all you need

  • InfoVAE: Information Maximizing Variational Autoencoders

  • Variational lossy encoder

  • Adversarial autoencoders

  • Hypernetwork

  • Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations

  • A Survey of Deep Reinforcement Learning in Video Games

  • Recurrent world models facilitates policy evaluation

  • A Closer Look at Memorization in Deep Networks

  • Hierarchical Decompositional Mixtures of Variational Autoencoders

  • Normalizing Flows for Probabilistic Modeling and Inference

  • On Learning Sets of Symmetric Elements

  • https://www.reddit.com/r/MachineLearning/comments/euab08/d_paper_recommendations_for_agi/?utm_source=dlvr.it&utm_medium=twitter

  • https://www.youtube.com/watch?v=Z56Jmr9Z34Q&list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J&index=2&t=0s

  • Disentangling the independently controllable factors of variation by interacting with the world

  • PolyGen: An Autoregressive Generative Model of 3D Meshes

  • Learning deep representations by mutual information estimation and maximization

  • BlockGAN

  • Inverse Graphics GAN

  • Self-Tuning Deep Reinforcement Learning

  • A review on generative adversarial networks: algorithms, theory and applications

  • Adversarial autoencoder

  • Stochastic Video Generation with a Learned Prior

  • Latex Style Guide

  • A Primer in BERTology: What we know about how BERT works

  • Computing Machinery and Intelligence

  • Kinds Of Minds: Toward An Understanding Of Consciousness .

  • Information generation as a functional basis of consciousness

  • RIM

  • Variational Temporal Abstraction

  • On Catastrophic Interference in Atari 2600 Games

  • Few-Shot Learning via Learning the Representation, Provably

  • Many Normalizations

  • Semantic Image Synthesis with SPADE

  • The importance of transparency and reproducibility in artificial intelligence research

  • Supervised autoencoders: Improving generalization performance with unsupervised regularizers

  • StyleGAN2 Distillation for Feed-forward Image Manipulation

  • Transformation-based Adversarial Video Prediction on Large-Scale Data

  • Google search: using GAN for representation learning

  • Object-based unsupervised scene representation learning

  • 基于对象的无监督场景表征学习

  • Object-based Representation Learning from Unlabeled Videos

  • Object-Level Representation Learning for Few-Shot Image Classification

  • BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

  • HoloGAN: Unsupervised learning of 3D representations from natural images

  • Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

  • GAN for representation learning

  • Object-Oriented Dynamics Predictor

  • Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations

  • Countering Language Drift with Seeded Iterated Learning

  • Out-of-Distribution Generalization via Risk Extrapolation (REx)

  • Stochastic Neural Network with Kronecker Flow

  • Planning in Dynamic Environments with Conditional Autoregressive Models

  • A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

  • Ordered Memory

  • Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

  • Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs

  • AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

  • Grid world

  • Neuroevolution of Self-Interpretable Agents

  • VQ-DRAW

  • Gauge Equivariant GCNN

  • SimCLR

  • Semi-supervised StyleGAN for disentanglement learning

  • On Catastrophic Interference in Atari 2600 Games

  • Get started with PyTorch, Cloud TPUs, and Colab

  • Deep Meditations: Controlled navigation of latent space

  • How to Pick your Grad School

  • Deep SDF

  • A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications

  • Representation Learning Through Latent Canonicalizations

  • Deep Sets for Generalization in RL

  • Disentangled Relational Representations for Explaining and Learning from Demonstration

  • language drift

  • Agent57

  • GNN

  • Indirectly Encoded Sodarace for Artificial Life

  • Blog example

  • Effective PyTorch

  • SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. (arXiv:2004.00353v1 [cs.LG]) https://ift.tt/2UWC9Ue

  • A theory of independent mechanisms for extrapolation in generative models. (arXiv:2004.00184v1 [cs.LG])

  • Latent space generative models

  • Use adversarial learning in the latent space. This way you don't need to produce perfect images.

  • Self-supervised Object-centric Representations for Reinforcement Learning

  • Construction as the Manipulation of Structure

  • Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning

  • Challenging common assumptions in the unsupervised learning of disentangled representations

  • Unsupervised Learning of Probably Symmetric Deformable 3D Objects in the Wild

  • Symmetry-Based Disentangled Representation Learning requires Interaction with Environments

  • CURL: Contrastive Unsupervised Representations for Reinforcement Learning

  • Contrastive Learning

  • The transformer family

  • Causal Relational Learning

  • SimCLR

  • Evolutionary algorithms

  • Convolution and attention (transformer)

  • [Social GAN](Social GAN)

  • Temporal Generalization, long term dependency

  • HoloGAN

  • The equivalence between Stein variational gradient descent and black-box variational inference

  • NLP tutorial

  • Learning from text

  • Deep unsupervised learning

  • Learning to Manipulate Individual Objects in an Image

  • pytorch-lightning

  • DeepSDF

  • NeRF

  • Positional encoding in Transformer

  • Deep (Neural) Volume Rendering

  • C++ Best Tutorial

  • RL Theory

  • Gan Training