| 6.16.22 |
Sharpness-Aware Minimization for Efficiently Improving Generalization |
google-research/sam |
| 5.26.22 |
Neural Tangent Kernel: Convergence and Generalization in Neural Networks |
|
| 4.28.22 |
A Modern Self-Referential Weight Matrix That Learns to Modify Itself |
IDSIA/modern-srwm |
| 4.14.22 |
Hierarchical Perceiver |
|
| 3.24.22 |
Dual Diffusion Implicit Bridges for Image-to-Image Translation |
|
| 3.10.22 |
Understanding Generalization through Visualizations |
wronnyhuang/gen-viz |
| 2.17.22 |
Divide and Contrast: Self-supervised Learning from Uncurated Data |
|
| 2.10.22 |
Investigating Human Priors for Playing Video Games |
rach0012/humanRL_prior_games |
| 1.27.22 |
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language |
pytorch/data2vec |
| 1.20.22 |
Consistent Video Depth Estimation |
facebookresearch/consistent_depth |
| 1.13.22 |
Masked Autoencoders Are Scalable Vision Learners |
|
| 12.02.21 |
Training Verifiers to Solve Math Word Problems |
|
| 11.18.21 |
(StyleGan3) Alias-Free Generative Adversarial Networks |
NVlabs/stylegan3 |
| 11.04.21 |
Do Vision Transformers See Like Convolutional Neural Networks? |
|
| 10.21.21 |
CoBERL: Contrastive BERT for Reinforcement Learning |
|
| 10.14.21 |
WarpedGANSpace: Finding non-linear RBF paths in GAN latent space |
chi0tzp/WarpedGANSpace |
| 10.06.21 |
RAFT: Recurrent All-Pairs Field Transforms for Optical Flow |
princeton-vl/RAFT |
| 9.16.21 |
Bootstrapped Meta-Learning |
|
| 9.09.21 |
Program Synthesis with Large Language Models |
|
| 8.19.21 |
Perceiver IO: A General Architecture for Structured Inputs & Outputs |
deepmind/perceiver |
| 8.12.21 |
Reward is enough |
|
| 8.05.21 |
Learning Compositional Rules via Neural Program Synthesis |
mtensor/rulesynthesis |
| 6.24.21 |
Thinking Like Transformers |
|
| 6.17.21 |
Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation |
|
| 6.10.21 |
Unsupervised Learning by Competing Hidden Units |
|
| 5.27.21 |
Pay Attention to MLPs |
|
| 5.20.21 |
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards |
|
| 5.13.21 |
Emerging Properties in Self-Supervised Vision Transformers |
|
| 5.06.21 |
Implicit Neural Representations with Periodic Activation Functions |
vsitzmann/siren |
| 4.29.21 |
How to represent part-whole hierarchies in a neural network |
lucidrains/glom-pytorch RedRyan111/GLOM ArneBinder/GlomImpl |
| 4.15.21 |
Perceiver: General Perception with Iterative Attention |
|
| 4.01.21 |
Synthetic Returns for Long-Term Credit Assignment |
|
| 3.25.21 |
The Pitfalls of Simplicity Bias in Neural Networks |
|
| 3.18.21 |
Bootstrap your own latent: A new approach to self-supervised Learning |
|
| 3.11.21 |
Meta Learning Backpropagation And Improving It |
|
| 3.04.21 |
Taming Transformers for High-Resolution Image Synthesis |
CompVis/taming-transformers |
| 2.18.21 |
Pre-training without Natural Images |
hirokatsukataoka16/FractalDB-Pretrained-ResNet-PyTorch |
| 2.11.21 |
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training |
blackfeather-wang/InfoPro-Pytorch |
| 2.04.21 |
Neural Power Units |
|
| 1.28.21 |
Representation Learning via Invariant Causal Mechanisms |
|
| 1.21.21 |
γ-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction |
JannerM/gamma-models |
| 1.14.21 |
Improving Generalisation for Temporal Difference Learning: The Successor Representation |
|
| 12.17.20 |
Learning Associative Inference Using Fast Weight Memory |
|
|
Hopfield Networks cycle ends |
|
| 12.10.20 |
Hopfield Networks is All You Need |
ml-jku/hopfield-layers |
| 12.03.20 |
On a model of associative memory with huge storage capacity |
|
| 11.19.20 |
Dense Associative Memory for Pattern Recognition |
|
| 11.12.20 |
Neural Networks and Physical Systems with Emergent Collective Computational Abilities (= "the Hopfield Networks paper") |
|
|
Hopfield Networks cycle of papers - from the original paper on Hopfield networks to "Hopfield Networks is All You Need" |
|
| 11.05.20 |
Training Generative Adversarial Networks with Limited Data |
NVlabs/stylegan2-ada |
| 10.29.20 |
Memories from patterns: Attractor and integrator networks in the brain |
|
| 10.15.20 |
Entities as Experts: Sparse Memory Access with Entity Supervision |
|
| 10.08.20 |
A Primer in BERTology: What we know about how BERT works |
|
| 10.01.20 |
It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners |
timoschick/pet |
| 9.24.20 |
End-to-End Object Detection with Transformers |
facebookresearch/detr |
| 9.17.20 |
Gated Linear Networks |
|
| 7.23.20 |
A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning |
|
| 7.02.20 |
DreamCoder: Building interpretable hierarchical knowledge representations with wake-sleep Bayesian program learning |
ellisk42/ec |
| 6.18.20 |
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver |
locuslab/SATNet |
| 6.4.20 |
Adaptive Attention Span in Transformers |
|
| 5.28.20 |
Complexity control by gradient descent in deep networks |
|
| 5.21.20 |
What Can Learned Intrinsic Rewards Capture? |
|
| 5.14.20 |
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction |
|
| 5.7.20 |
Write, Execute, Assess: Program Synthesis With a REPL |
flxsosa/ProgramSearch |
| 4.23.20 |
Graph Representations for Higher-Order Logic and Theorem Proving |
|
| 4.16.20 |
Mathematical Reasoning in Latent Space |
|
| 4.9.20 |
MEMO: A Deep Network for Flexible Combination of Episodic Memories |
|
| 4.2.20 |
Creating High Resolution Images with a Latent Adversarial Generator |
|
| 3.26.20 |
Invertible Residual Networks |
|
| 3.5.20 |
Value-driven Hindsight Modelling |
|
| 2.27.20 |
Analyzing and Improving the Image Quality of StyleGAN |
|
| 2.13.20 |
Axiomatic Attribution for Deep Networks |
|
| 2.6.20 |
Automated curricula through setter-solver interactions |
|
| 1.30.20 |
Protein structure prediction ... |
deepmind |
| 1.23.20 |
Putting An End to End-to-End: Gradient-Isolated Learning of Representations |
|
| 1.16.20 |
Normalizing Flows: An Introduction and Review of Current Methods |
|
| 12.19.19 |
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model |
|
| 12.5.19 |
On the Measure of Intelligence |
|
| 11.21.19 |
Understanding the Neural Tangent Kernel |
rajatvd |
| 11.14.19 |
XLNet: Generalized Autoregressive Pretraining for Language Understanding |
|
| 11.7.19 |
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction |
|
| 10.31.19 |
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context |
|
| 10.24.19 |
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting |
|
| 10.17.19 |
Unsupervised Doodling and Painting with Improved SPIRAL |
|
| 10.10.19 |
Adversarial Robustness as a Prior for Learned Representations |
MadryLab |
| 10.3.19 |
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks |
|
| 9.26.19 |
Image Transformer |
|
| 9.19.19 |
Generating Diverse High-Fidelity Images with VQ-VAE-2 |
|
| 9.12.19 |
Neural Discrete Representation Learning |
|
| 9.5.19 |
Neural Text Generation with Unlikelihood Training |
|
| 8.29.19 |
Learning Representations by Maximizing Mutual Information Across Views |
|
| break |
switch from Tuesdays to Thursdays after the break |
|
| 6.11.19 |
BERT Rediscovers the Classical NLP Pipeline |
|
| 6.4.19 |
Semantic Visual Localization |
|
| 5.28.19 |
AlgoNet: C^∞ Smooth Algorithmic Neural Networks |
|
| 5.14.19 |
Unsupervised Data Augmentation for Consistency Training |
|
| 4.30.19 |
Augmented Neural ODEs |
|
| 4.9.19 |
Wasserstein Dependency Measure for Representation Learning |
|
| 4.2.19 |
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading |
|
| 3.26.19 |
Meta Particle Flow for Sequential Bayesian Inference |
|
| 3.19.19 |
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms |
|
| 3.12.19 |
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks |
|
| 2.26.19 |
Language Models are Unsupervised Multitask Learners |
openai |
| 2.19.19 |
Learning to Understand Goal Specifications by Modelling Reward |
|
| 1.29.19 |
GamePad: A Learning Environment for Theorem Proving |
|
| 1.15.19 |
Matrix capsules with EM routing |
|
| 12.4.18 |
Optimizing Agent Behavior over Long Time Scales by Transporting Value |
|
| 11.27.18 |
Embedding Logical Queries on Knowledge Graphs |
williamleif |
| 11.20.18 |
Large-Scale Study of Curiosity-Driven Learning |
openai |
| 11.13.18 |
Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding |
nke001 |
| 11.6.18 |
Generalizing Hamiltonian Monte Carlo with Neural Networks |
brain-research |
| 10.23.18 |
A Conceptual Introduction to Hamiltonian Monte Carlo |
|
| 10.16.18 |
MaskGAN: Better Text Generation via Filling in the ... |
|
| 10.9.18 |
Large Scale GAN Training for High Fidelity Natural Image Synthesis |
|
| 10.2.18 |
Improving Variational Inference with Inverse Autoregressive Flow |
|
| 9.25.18 |
Artificial Intelligence - The Revolution Hasn’t Happened Yet |
|
| 9.18.18 |
Learning deep representations by mutual information estimation and maximization |
|
| 9.11.18 |
The Variational Homoencoder: Learning to learn high capacity generative models from few examples |
insperatum |
| 9.4.18 |
Towards Conceptual Compression |
geosada |
| 8.28.18 |
Vector-based navigation using grid-like representations in artificial agents |
deepmind |
|
break in maintaining this file; filled on April 10, 2020 |
|
| ------ |
------------- |
------------- |
| 8.21.18 |
Universal Transformers |
tensorflow |
| 8.14.18 |
Neural Arithmetic Logic Units |
gautam1858 |
| 8.7.18 |
Neural Scene Representation and Rendering |
|
| 7.31.18 |
Measuring Abstract Reasoning in Neural Networks |
|
| 6.26.18 |
Improving Language Understanding by Generative Pre-Training |
openai |
| 6.19.18 |
Associative Compression Networks for Representation Learning |
|
| 6.12.18 |
On Characterizing the Capacity of Neural Networks using Algebraic Topology |
|
| 6.5.18 |
Causal Effect Inference with Deep Latent-Variable Models |
AMLab |
| 5.29.18 |
ML beyond Curve Fitting |
|
| 5.22.18 |
Synthesizing Programs for Images using Reinforced Adversarial Learning |
|
| 5.15.18 |
TensorFlow Overview |
r1.8 |
| 5.8.18 |
Compositional Attention Networks for Machine Reasoning |
stanfordnlp |
| 4.24.18 |
The Annotated Transformer |
|
| 4.3.18 |
How Developers Iterate on Machine Learning Workflows |
|
| 3.27.18 |
Faster R-CNN: Towards Real-Time Object,Detection with Region Proposal Networks |
|
| 3.20.18 |
Attention Is All You Need |
tensor2tensor |
| 3.6.18 |
Generating Wikipedia by Summarizing Long Sequences |
wikisum, per this gist |
| 2.27.18 |
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks |
StackGAN-v2 |
| 2.20.18 |
Information Dropout |
InformationDropout, official implementation |
| 2.13.18 |
Nested LSTMs |
Nested-LSTM |
| 2.6.18 |
Deep vs. Shallow Networks: An Approximation Theory Perspective |
|
| 1.30.18 |
The Case for Learned Index Structures |
|
| 1.23.18 |
Visualizing The Loss Landscape Of Neural Nets |
|
| 1.16.18 |
Go for a Walk and Arrive at the Answer, RelNet: End-to-End Modeling of Entities & Relations |
|
| 1.9.18 |
Intro to Coq |
|
| 12.12.17 |
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks |
(ChainsofReasoning) |
| 12.5.17 |
Stochastic Neural Networks for Hierarchical Reinforcement Learning |
snn4hrl |
| 11.28.17 |
Emergent Complexity via Multi-Agent Competition (blog post) |
multiagent-competition |
| 11.14.17 |
Mastering the game of Go without human knowledge |
|
| 11.7.17 |
Meta-Learning with Memory-Augmented Neural Networks |
ntm-meta-learning |
| 10.24.17 |
Poincaré Embeddings for Learning Hierarchical Representations |
poincare_embeddings |
| 10.17.17 |
What does Attention in Neural Machine Translation Pay Attention to? |
|
| 10.10.17 |
Zero-Shot Learning Through Cross-Modal Transfer |
zslearning |
| 9.26.17 |
Variational Boosting: Iteratively Refining Posterior Approximations |
vboost |
| 9.19.17 |
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks |
cbfinn |
| 9.12.17 |
Neuroscience-inspired AI |
|
| 9.5.17 |
Recurrent Dropout Without Memory Loss |
rnn_cell_mulint_modern.py |
| 8.29.17 |
Deep Transfer Learning with Joint Adaptation Networks |
jmmd.{cpp,hpp} |
| 8.22.17 |
Designing Neural Network Architectures using Reinforcement Learning |
metaqnn |
| 8.15.17 |
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences |
plstm |
| 8.8.17 |
Hyper Networks |
otoro blog |
| 8.1.17 |
Full-Capacity Unitary Recurrent Neural Networks |
complex_RNN, urnn |
| 7.25.17 |
Decoupled Neural Interfaces using Synthetic Gradients & follow-up |
dni.pytorch |
| 7.18.17 |
A simple neural network module for relational reasoning |
relation-network |
| 7.11.17 |
Speaker diarization using deep neural network embeddings |
|
| 6.20.17 |
Neural Episodic Control |
PFCM |
| 6.13.17 |
Lie-Access Neural Turing Machines |
harvardnlp |
| 6.6.17 |
Artistic style transfer for videos |
artistic video |
| 5.30.17 |
High-Dimensional Continuous Control Using Generalized Advantage Estimation |
modular_rl |
| 5.23.17 |
Emergence of Grounded Compositional Language in Multi-Agent Populations |
|
| 5.16.17 |
Trust Region Policy Optimization |
modular_rl |
| 5.9.17 |
Improved Training of Wasserstein GANs |
code |
| 5.4.17 |
Using Fast Weights to Attend to the Recent Past |
|
| 4.25.17 |
Strategic Attentive Writer for Learning Macro-Actions |
|
| 4.18.17 |
Massive Exploration of Neural Machine Translation Architectures |
|
| 4.4.17 |
End to End Learning for Self-Driving Cars |
|
| 3.28.17 |
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning |
|
| 3.21.17 |
Image-to-Image Translation with Conditional Adversarial Networks |
|
| 3.7.17 |
Neural Programmer Interpreters |
|
| 2.14.17 |
Wasserstein GAN |
|
| 2.7.17 |
Towards Principled Methods for Training GANs |
|
| 1.31.17 |
Mastering the Game of Go with Deep Networks |
|
| 1.24.17 |
Understanding Deep Learning Requires Rethinking Generalization |
|
| 1.17.17 |
Neural Semantic Encoders |
|
| 12.21.16 |
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks |
|
| 12.14.16 |
Key-Value Memory Networks for Directly Reading Documents |
|
| 12.7.16 |
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets |
|