title
stringlengths 8
155
| citations_google_scholar
int64 0
28.9k
| conference
stringclasses 5
values | forks
int64 0
46.3k
| issues
int64 0
12.2k
| lastModified
stringlengths 19
26
| repo_url
stringlengths 26
130
| stars
int64 0
75.9k
| title_google_scholar
stringlengths 8
155
| url_google_scholar
stringlengths 75
206
| watchers
int64 0
2.77k
| year
int64 2.02k
2.02k
|
|---|---|---|---|---|---|---|---|---|---|---|---|
Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning | 26 | icml | 1 | 0 | 2023-06-17 04:55:59.798000 | https://github.com/zhaoyang-0204/gnp | 21 | Penalizing gradient norm for efficiently improving generalization in deep learning | https://scholar.google.com/scholar?cluster=9350049289748522587&hl=en&as_sdt=0,3 | 1 | 2,022 |
Online Decision Transformer | 59 | icml | 18 | 0 | 2023-06-17 04:56:00.005000 | https://github.com/facebookresearch/online-dt | 127 | Online decision transformer | https://scholar.google.com/scholar?cluster=11549184825048973545&hl=en&as_sdt=0,34 | 4 | 2,022 |
Describing Differences between Text Distributions with Natural Language | 6 | icml | 3 | 0 | 2023-06-17 04:56:00.212000 | https://github.com/ruiqi-zhong/describedistributionaldifferences | 32 | Describing differences between text distributions with natural language | https://scholar.google.com/scholar?cluster=12276789524717856994&hl=en&as_sdt=0,36 | 3 | 2,022 |
Model Agnostic Sample Reweighting for Out-of-Distribution Learning | 15 | icml | 2 | 0 | 2023-06-17 04:56:00.430000 | https://github.com/x-zho14/maple | 30 | Model agnostic sample reweighting for out-of-distribution learning | https://scholar.google.com/scholar?cluster=4328634809674273852&hl=en&as_sdt=0,48 | 3 | 2,022 |
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting | 151 | icml | 78 | 1 | 2023-06-17 04:56:00.639000 | https://github.com/MAZiqing/FEDformer | 371 | Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting | https://scholar.google.com/scholar?cluster=447506194635826863&hl=en&as_sdt=0,36 | 4 | 2,022 |
Improving Adversarial Robustness via Mutual Information Estimation | 1 | icml | 0 | 0 | 2023-06-17 04:56:00.845000 | https://github.com/dwdavidxd/miat | 0 | Improving Adversarial Robustness via Mutual Information Estimation | https://scholar.google.com/scholar?cluster=18303472399739418322&hl=en&as_sdt=0,3 | 1 | 2,022 |
Modeling Adversarial Noise for Adversarial Training | 0 | icml | 0 | 0 | 2023-06-17 04:56:01.052000 | https://github.com/dwdavidxd/man | 2 | Modeling Adversarial Noise for Adversarial Training | https://scholar.google.com/scholar?cluster=6688229047921425158&hl=en&as_sdt=0,33 | 1 | 2,022 |
Contrastive Learning with Boosted Memorization | 5 | icml | 7 | 0 | 2023-06-17 04:56:01.270000 | https://github.com/MediaBrain-SJTU/BCL | 106 | Contrastive learning with boosted memorization | https://scholar.google.com/scholar?cluster=1426610895759607761&hl=en&as_sdt=0,33 | 4 | 2,022 |
Understanding The Robustness in Vision Transformers | 66 | icml | 24 | 11 | 2023-06-17 04:56:01.481000 | https://github.com/nvlabs/fan | 421 | Understanding the robustness in vision transformers | https://scholar.google.com/scholar?cluster=3041067607452518927&hl=en&as_sdt=0,5 | 22 | 2,022 |
Contextual Bandits with Large Action Spaces: Made Practical | 8 | icml | 0 | 0 | 2023-06-17 04:56:01.687000 | https://github.com/pmineiro/linrepcb | 1 | Contextual bandits with large action spaces: Made practical | https://scholar.google.com/scholar?cluster=5763648014002570810&hl=en&as_sdt=0,44 | 0 | 2,022 |
Neural-Symbolic Models for Logical Queries on Knowledge Graphs | 21 | icml | 5 | 4 | 2023-06-17 04:56:01.894000 | https://github.com/DeepGraphLearning/GNN-QE | 70 | Neural-symbolic models for logical queries on knowledge graphs | https://scholar.google.com/scholar?cluster=2755509975751664011&hl=en&as_sdt=0,5 | 4 | 2,022 |
Topology-aware Generalization of Decentralized SGD | 8 | icml | 2 | 0 | 2023-06-17 04:56:02.100000 | https://github.com/raiden-zhu/generalization-of-dsgd | 24 | Topology-aware generalization of decentralized sgd | https://scholar.google.com/scholar?cluster=17709285400263398599&hl=en&as_sdt=0,10 | 3 | 2,022 |
On Numerical Integration in Neural Ordinary Differential Equations | 7 | icml | 0 | 0 | 2023-06-17 04:56:02.306000 | https://github.com/aiqing-zhu/imde | 2 | On numerical integration in neural ordinary differential equations | https://scholar.google.com/scholar?cluster=1480049561976484832&hl=en&as_sdt=0,47 | 1 | 2,022 |
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces | 5 | icml | 0 | 0 | 2023-06-17 04:56:02.513000 | https://github.com/pmineiro/smoothcb | 2 | Contextual bandits with smooth regret: Efficient learning in continuous action spaces | https://scholar.google.com/scholar?cluster=2237234303144765537&hl=en&as_sdt=0,39 | 0 | 2,022 |
Region-Based Semantic Factorization in GANs | 14 | icml | 3 | 6 | 2023-06-17 04:56:02.718000 | https://github.com/zhujiapeng/resefa | 67 | Region-based semantic factorization in GANs | https://scholar.google.com/scholar?cluster=15967827822215112166&hl=en&as_sdt=0,15 | 5 | 2,022 |
Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm | 4 | icml | 1 | 0 | 2023-06-17 04:56:02.925000 | https://github.com/pizilber/IMC | 1 | Inductive matrix completion: No bad local minima and a fast algorithm | https://scholar.google.com/scholar?cluster=1576217126267485656&hl=en&as_sdt=0,3 | 1 | 2,022 |
Synthetic and Natural Noise Both Break Neural Machine Translation | 633 | iclr | 8 | 2 | 2023-06-18 08:50:41.202000 | https://github.com/ybisk/charNMT-noise | 28 | Synthetic and natural noise both break neural machine translation | https://scholar.google.com/scholar?cluster=10493132199224079445&hl=en&as_sdt=0,5 | 4 | 2,018 |
Training and Inference with Integers in Deep Neural Networks | 413 | iclr | 38 | 4 | 2023-06-18 08:50:41.409000 | https://github.com/boluoweifenda/WAGE | 143 | Training and inference with integers in deep neural networks | https://scholar.google.com/scholar?cluster=15215054387477750278&hl=en&as_sdt=0,44 | 10 | 2,018 |
Spherical CNNs | 888 | iclr | 170 | 17 | 2023-06-18 08:50:41.611000 | https://github.com/jonas-koehler/s2cnn | 908 | Spherical cnns | https://scholar.google.com/scholar?cluster=6361332838540502667&hl=en&as_sdt=0,36 | 28 | 2,018 |
On the insufficiency of existing momentum schemes for Stochastic Optimization | 103 | iclr | 27 | 1 | 2023-06-18 08:50:41.814000 | https://github.com/rahulkidambi/AccSGD | 207 | On the insufficiency of existing momentum schemes for stochastic optimization | https://scholar.google.com/scholar?cluster=6907311906014063619&hl=en&as_sdt=0,3 | 5 | 2,018 |
Wasserstein Auto-Encoders | 1,044 | iclr | 92 | 7 | 2023-06-18 08:50:42.021000 | https://github.com/tolstikhin/wae | 490 | Wasserstein auto-encoders | https://scholar.google.com/scholar?cluster=1669877132293977025&hl=en&as_sdt=0,5 | 21 | 2,018 |
Spectral Normalization for Generative Adversarial Networks | 4,106 | iclr | 200 | 26 | 2023-06-18 08:50:42.223000 | https://github.com/pfnet-research/sngan_projection | 1,045 | Spectral normalization for generative adversarial networks | https://scholar.google.com/scholar?cluster=973410365172845184&hl=en&as_sdt=0,5 | 34 | 2,018 |
Learning to Represent Programs with Graphs | 764 | iclr | 37 | 4 | 2023-06-18 08:50:42.514000 | https://github.com/Microsoft/graph-based-code-modelling | 157 | Learning to represent programs with graphs | https://scholar.google.com/scholar?cluster=9342740598325165289&hl=en&as_sdt=0,5 | 13 | 2,018 |
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality | 633 | iclr | 38 | 0 | 2023-06-18 08:50:42.773000 | https://github.com/xingjunm/lid_adversarial_subspace_detection | 112 | Characterizing adversarial subspaces using local intrinsic dimensionality | https://scholar.google.com/scholar?cluster=17134144151462669065&hl=en&as_sdt=0,23 | 4 | 2,018 |
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model | 349 | iclr | 84 | 6 | 2023-06-18 08:50:42.979000 | https://github.com/zihangdai/mos | 391 | Breaking the softmax bottleneck: A high-rank RNN language model | https://scholar.google.com/scholar?cluster=15538946355362697879&hl=en&as_sdt=0,23 | 14 | 2,018 |
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments | 348 | iclr | 73 | 5 | 2023-06-18 08:50:43.181000 | https://github.com/openai/robosumo | 283 | Continuous adaptation via meta-learning in nonstationary and competitive environments | https://scholar.google.com/scholar?cluster=10800934967753473866&hl=en&as_sdt=0,5 | 20 | 2,018 |
Neural Sketch Learning for Conditional Program Generation | 137 | iclr | 83 | 33 | 2023-06-18 08:50:43.381000 | https://github.com/capergroup/bayou | 276 | Neural sketch learning for conditional program generation | https://scholar.google.com/scholar?cluster=11134234129920472875&hl=en&as_sdt=0,5 | 43 | 2,018 |
Progressive Growing of GANs for Improved Quality, Stability, and Variation | 6,379 | iclr | 1,102 | 11 | 2023-06-18 08:50:43.589000 | https://github.com/tkarras/progressive_growing_of_gans | 5,932 | Progressive growing of gans for improved quality, stability, and variation | https://scholar.google.com/scholar?cluster=11486098150916361186&hl=en&as_sdt=0,5 | 273 | 2,018 |
Zero-Shot Visual Imitation | 264 | iclr | 43 | 6 | 2023-06-18 08:50:43.790000 | https://github.com/pathak22/zeroshot-imitation | 201 | Zero-shot visual imitation | https://scholar.google.com/scholar?cluster=15276541363750863723&hl=en&as_sdt=0,5 | 15 | 2,018 |
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs | 205 | iclr | 11 | 0 | 2023-06-18 08:50:43.991000 | https://github.com/jamie-murdoch/ContextualDecomposition | 56 | Beyond word importance: Contextual decomposition to extract interactions from lstms | https://scholar.google.com/scholar?cluster=9223539489272553209&hl=en&as_sdt=0,5 | 10 | 2,018 |
Model-Ensemble Trust-Region Policy Optimization | 422 | iclr | 26 | 1 | 2023-06-18 08:50:44.192000 | https://github.com/thanard/me-trpo | 85 | Model-ensemble trust-region policy optimization | https://scholar.google.com/scholar?cluster=5763230631763342838&hl=en&as_sdt=0,22 | 4 | 2,018 |
Learning Latent Permutations with Gumbel-Sinkhorn Networks | 199 | iclr | 21 | 0 | 2023-06-18 08:50:44.394000 | https://github.com/google/gumbel_sinkhorn | 69 | Learning latent permutations with gumbel-sinkhorn networks | https://scholar.google.com/scholar?cluster=17995429437153045101&hl=en&as_sdt=0,29 | 4 | 2,018 |
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration | 88 | iclr | 1 | 1 | 2023-06-18 08:50:44.596000 | https://github.com/flowersteam/Unsupervised_Goal_Space_Learning | 19 | Unsupervised learning of goal spaces for intrinsically motivated goal exploration | https://scholar.google.com/scholar?cluster=17844977813077230695&hl=en&as_sdt=0,5 | 16 | 2,018 |
Multi-View Data Generation Without View Supervision | 22 | iclr | 1 | 0 | 2023-06-18 08:50:44.799000 | https://github.com/mickaelChen/GMV | 12 | Multi-view data generation without view supervision | https://scholar.google.com/scholar?cluster=15286827840377806140&hl=en&as_sdt=0,5 | 3 | 2,018 |
Hyperparameter optimization: a spectral approach | 131 | iclr | 32 | 2 | 2023-06-18 08:50:45.001000 | https://github.com/callowbird/Harmonica | 173 | Hyperparameter optimization: A spectral approach | https://scholar.google.com/scholar?cluster=11236398750787903780&hl=en&as_sdt=0,3 | 8 | 2,018 |
Efficient Sparse-Winograd Convolutional Neural Networks | 140 | iclr | 50 | 1 | 2023-06-18 08:50:45.203000 | https://github.com/xingyul/Sparse-Winograd-CNN | 179 | Efficient sparse-winograd convolutional neural networks | https://scholar.google.com/scholar?cluster=5437414522331578688&hl=en&as_sdt=0,33 | 13 | 2,018 |
Polar Transformer Networks | 174 | iclr | 19 | 3 | 2023-06-18 08:50:45.407000 | https://github.com/daniilidis-group/polar-transformer-networks | 54 | Polar transformer networks | https://scholar.google.com/scholar?cluster=15618354521274654533&hl=en&as_sdt=0,5 | 7 | 2,018 |
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks | 1,398 | iclr | 101 | 15 | 2023-06-18 08:50:45.613000 | https://github.com/facebookresearch/odin | 485 | Enhancing the reliability of out-of-distribution image detection in neural networks | https://scholar.google.com/scholar?cluster=7536099354022278878&hl=en&as_sdt=0,7 | 14 | 2,018 |
Stabilizing Adversarial Nets with Prediction Methods | 106 | iclr | 0 | 1 | 2023-06-18 08:50:45.821000 | https://github.com/jaiabhayk/stableGAN | 1 | Stabilizing adversarial nets with prediction methods | https://scholar.google.com/scholar?cluster=1304972437215881711&hl=en&as_sdt=0,5 | 3 | 2,018 |
Graph Attention Networks | 6,447 | iclr | 618 | 31 | 2023-06-18 08:50:46.032000 | https://github.com/PetarV-/GAT | 2,775 | Graph attention networks | https://scholar.google.com/scholar?cluster=5609128480281463225&hl=en&as_sdt=0,5 | 47 | 2,018 |
Generalizing Hamiltonian Monte Carlo with Neural Networks | 127 | iclr | 42 | 2 | 2023-06-18 08:50:46.235000 | https://github.com/brain-research/l2hmc | 179 | Generalizing hamiltonian monte carlo with neural networks | https://scholar.google.com/scholar?cluster=6189563132756829558&hl=en&as_sdt=0,10 | 20 | 2,018 |
Divide and Conquer Networks | 16 | iclr | 8 | 1 | 2023-06-18 08:50:46.436000 | https://github.com/alexnowakvila/DiCoNet | 11 | Divide and conquer networks | https://scholar.google.com/scholar?cluster=13506472853038229205&hl=en&as_sdt=0,5 | 3 | 2,018 |
Meta Learning Shared Hierarchies | 361 | iclr | 164 | 16 | 2023-06-18 08:50:46.644000 | https://github.com/openai/mlsh | 588 | Meta learning shared hierarchies | https://scholar.google.com/scholar?cluster=8366113293045727240&hl=en&as_sdt=0,5 | 44 | 2,018 |
Deep Neural Networks as Gaussian Processes | 914 | iclr | 52 | 2 | 2023-06-18 08:50:46.845000 | https://github.com/brain-research/nngp | 178 | Deep neural networks as gaussian processes | https://scholar.google.com/scholar?cluster=6709509064500094656&hl=en&as_sdt=0,18 | 12 | 2,018 |
Syntax-Directed Variational Autoencoder for Structured Data | 315 | iclr | 19 | 2 | 2023-06-18 08:50:47.049000 | https://github.com/Hanjun-Dai/sdvae | 75 | Syntax-directed variational autoencoder for structured data | https://scholar.google.com/scholar?cluster=7991796845235005593&hl=en&as_sdt=0,14 | 9 | 2,018 |
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering | 181 | iclr | 19 | 2 | 2023-06-18 08:50:47.252000 | https://github.com/shuohangwang/mprc | 83 | Evidence aggregation for answer re-ranking in open-domain question answering | https://scholar.google.com/scholar?cluster=5917321946590508860&hl=en&as_sdt=0,5 | 14 | 2,018 |
MGAN: Training Generative Adversarial Nets with Multiple Generators | 221 | iclr | 19 | 2 | 2023-06-18 08:50:47.453000 | https://github.com/qhoangdl/MGAN | 38 | MGAN: Training generative adversarial nets with multiple generators | https://scholar.google.com/scholar?cluster=15083973924521420990&hl=en&as_sdt=0,47 | 8 | 2,018 |
SEARNN: Training RNNs with global-local losses | 42 | iclr | 9 | 1 | 2023-06-18 08:50:47.656000 | https://github.com/RemiLeblond/SeaRNN-open | 50 | SEARNN: Training RNNs with global-local losses | https://scholar.google.com/scholar?cluster=10552754146488713829&hl=en&as_sdt=0,22 | 6 | 2,018 |
Unsupervised Representation Learning by Predicting Image Rotations | 2,632 | iclr | 123 | 14 | 2023-06-18 08:50:47.859000 | https://github.com/gidariss/FeatureLearningRotNet | 490 | Unsupervised representation learning by predicting image rotations | https://scholar.google.com/scholar?cluster=12748509220929577948&hl=en&as_sdt=0,44 | 14 | 2,018 |
Emergent Communication in a Multi-Modal, Multi-Step Referential Game | 113 | iclr | 21 | 1 | 2023-06-18 08:50:48.059000 | https://github.com/nyu-dl/MultimodalGame | 55 | Emergent communication in a multi-modal, multi-step referential game | https://scholar.google.com/scholar?cluster=6581857213563474520&hl=en&as_sdt=0,19 | 9 | 2,018 |
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling | 1,329 | iclr | 110 | 25 | 2023-06-18 08:50:48.261000 | https://github.com/matenure/FastGCN | 504 | Fastgcn: fast learning with graph convolutional networks via importance sampling | https://scholar.google.com/scholar?cluster=18054036108684442257&hl=en&as_sdt=0,14 | 12 | 2,018 |
Demystifying MMD GANs | 873 | iclr | 14 | 0 | 2023-06-18 08:50:48.462000 | https://github.com/mbinkowski/MMD-GAN | 79 | Demystifying mmd gans | https://scholar.google.com/scholar?cluster=10236052458128513824&hl=en&as_sdt=0,5 | 5 | 2,018 |
Smooth Loss Functions for Deep Top-k Classification | 97 | iclr | 31 | 1 | 2023-06-18 08:50:48.663000 | https://github.com/oval-group/smooth-topk | 237 | Smooth loss functions for deep top-k classification | https://scholar.google.com/scholar?cluster=2261810241418874442&hl=en&as_sdt=0,3 | 14 | 2,018 |
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem | 25 | iclr | 6 | 0 | 2023-06-18 08:50:48.865000 | https://github.com/afriesen/ftprop | 27 | Deep learning as a mixed convex-combinatorial optimization problem | https://scholar.google.com/scholar?cluster=14079107033151501838&hl=en&as_sdt=0,5 | 3 | 2,018 |
Model compression via distillation and quantization | 627 | iclr | 77 | 1 | 2023-06-18 08:50:49.066000 | https://github.com/antspy/quantized_distillation | 317 | Model compression via distillation and quantization | https://scholar.google.com/scholar?cluster=9862176539747361028&hl=en&as_sdt=0,5 | 10 | 2,018 |
Variational Message Passing with Structured Inference Networks | 44 | iclr | 16 | 3 | 2023-06-18 08:50:49.267000 | https://github.com/emtiyaz/vmp-for-svae | 41 | Variational message passing with structured inference networks | https://scholar.google.com/scholar?cluster=4788714492758509312&hl=en&as_sdt=0,5 | 6 | 2,018 |
Learning from Between-class Examples for Deep Sound Recognition | 240 | iclr | 23 | 8 | 2023-06-18 08:50:49.470000 | https://github.com/mil-tokyo/bc_learning_sound | 84 | Learning from between-class examples for deep sound recognition | https://scholar.google.com/scholar?cluster=13221046760066147561&hl=en&as_sdt=0,19 | 18 | 2,018 |
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples | 735 | iclr | 47 | 4 | 2023-06-18 08:50:49.672000 | https://github.com/alinlab/Confident_classifier | 173 | Training confidence-calibrated classifiers for detecting out-of-distribution samples | https://scholar.google.com/scholar?cluster=14294577348397503039&hl=en&as_sdt=0,5 | 11 | 2,018 |
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop | 167 | iclr | 162 | 18 | 2023-06-18 08:50:49.874000 | https://github.com/facebookresearch/loop | 874 | Voiceloop: Voice fitting and synthesis via a phonological loop | https://scholar.google.com/scholar?cluster=14159878382438547497&hl=en&as_sdt=0,34 | 68 | 2,018 |
Generating Wikipedia by Summarizing Long Sequences | 727 | iclr | 3,290 | 589 | 2023-06-18 08:50:50.075000 | https://github.com/tensorflow/tensor2tensor | 13,768 | Generating wikipedia by summarizing long sequences | https://scholar.google.com/scholar?cluster=9480555348664414627&hl=en&as_sdt=0,5 | 461 | 2,018 |
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training | 1,212 | iclr | 39 | 3 | 2023-06-18 08:50:50.276000 | https://github.com/synxlin/deep-gradient-compression | 186 | Deep gradient compression: Reducing the communication bandwidth for distributed training | https://scholar.google.com/scholar?cluster=2485379403852124678&hl=en&as_sdt=0,44 | 8 | 2,018 |
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models | 1,148 | iclr | 415 | 34 | 2023-06-18 08:50:50.478000 | https://github.com/bethgelab/foolbox | 2,503 | Decision-based adversarial attacks: Reliable attacks against black-box machine learning models | https://scholar.google.com/scholar?cluster=1222517566911879461&hl=en&as_sdt=0,47 | 46 | 2,018 |
Unbiased Online Recurrent Optimization | 80 | iclr | 1 | 0 | 2023-06-18 08:50:50.682000 | https://github.com/ctallec/uoro | 9 | Unbiased online recurrent optimization | https://scholar.google.com/scholar?cluster=3493841590728342658&hl=en&as_sdt=0,10 | 5 | 2,018 |
Measuring the Intrinsic Dimension of Objective Landscapes | 234 | iclr | 36 | 4 | 2023-06-18 08:50:50.884000 | https://github.com/uber-research/intrinsic-dimension | 223 | Measuring the intrinsic dimension of objective landscapes | https://scholar.google.com/scholar?cluster=17182266159657033387&hl=en&as_sdt=0,5 | 12 | 2,018 |
Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks | 38 | iclr | 8 | 0 | 2023-06-18 08:50:51.086000 | https://github.com/whyjay/memoryGAN | 47 | Memorization precedes generation: Learning unsupervised gans with memory networks | https://scholar.google.com/scholar?cluster=7548592689214672445&hl=en&as_sdt=0,5 | 8 | 2,018 |
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control | 112 | iclr | 46,278 | 1,207 | 2023-06-18 08:50:51.287000 | https://github.com/tensorflow/models | 75,928 | Trust-pcl: An off-policy trust region method for continuous control | https://scholar.google.com/scholar?cluster=11034633680493566157&hl=en&as_sdt=0,47 | 2,774 | 2,018 |
Towards better understanding of gradient-based attribution methods for Deep Neural Networks | 852 | iclr | 158 | 41 | 2023-06-18 08:50:51.512000 | https://github.com/kundajelab/deeplift | 735 | Towards better understanding of gradient-based attribution methods for deep neural networks | https://scholar.google.com/scholar?cluster=7129422820232184089&hl=en&as_sdt=0,3 | 38 | 2,018 |
Countering Adversarial Images using Input Transformations | 1,237 | iclr | 74 | 0 | 2023-06-18 08:50:51.715000 | https://github.com/facebookresearch/adversarial_image_defenses | 478 | Countering adversarial images using input transformations | https://scholar.google.com/scholar?cluster=3375700876994648267&hl=en&as_sdt=0,26 | 19 | 2,018 |
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks | 230 | iclr | 41 | 1 | 2023-06-18 08:50:51.918000 | https://github.com/imatge-upc/skiprnn-2017-telecombcn | 123 | Skip rnn: Learning to skip state updates in recurrent neural networks | https://scholar.google.com/scholar?cluster=4452574796134429216&hl=en&as_sdt=0,31 | 10 | 2,018 |
Twin Networks: Matching the Future for Sequence Generation | 56 | iclr | 2 | 0 | 2023-06-18 08:50:52.122000 | https://github.com/dmitriy-serdyuk/twin-net | 14 | Twin networks: Matching the future for sequence generation | https://scholar.google.com/scholar?cluster=18040787837429694230&hl=en&as_sdt=0,7 | 3 | 2,018 |
Interactive Grounded Language Acquisition and Generalization in a 2D World | 78 | iclr | 31 | 1 | 2023-06-18 08:50:52.324000 | https://github.com/PaddlePaddle/XWorld | 84 | Interactive grounded language acquisition and generalization in a 2d world | https://scholar.google.com/scholar?cluster=4696587271474463712&hl=en&as_sdt=0,5 | 17 | 2,018 |
Emergent Complexity via Multi-Agent Competition | 396 | iclr | 151 | 12 | 2023-06-18 08:50:52.561000 | https://github.com/openai/multiagent-competition | 761 | Emergent complexity via multi-agent competition | https://scholar.google.com/scholar?cluster=12865596457557919071&hl=en&as_sdt=0,21 | 46 | 2,018 |
Learning to Count Objects in Natural Images for Visual Question Answering | 203 | iclr | 45 | 1 | 2023-06-18 08:50:52.771000 | https://github.com/Cyanogenoid/vqa-counting | 197 | Learning to count objects in natural images for visual question answering | https://scholar.google.com/scholar?cluster=5291501502665174038&hl=en&as_sdt=0,24 | 10 | 2,018 |
i-RevNet: Deep Invertible Networks | 304 | iclr | 46 | 3 | 2023-06-18 08:50:52.973000 | https://github.com/jhjacobsen/pytorch-i-revnet | 385 | i-revnet: Deep invertible networks | https://scholar.google.com/scholar?cluster=14608880224467079528&hl=en&as_sdt=0,5 | 20 | 2,018 |
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach | 411 | iclr | 19 | 8 | 2023-06-18 08:50:53.174000 | https://github.com/huanzhang12/CLEVER | 55 | Evaluating the robustness of neural networks: An extreme value theory approach | https://scholar.google.com/scholar?cluster=2078120094241692942&hl=en&as_sdt=0,5 | 6 | 2,018 |
HexaConv | 82 | iclr | 12 | 5 | 2023-06-18 08:50:53.375000 | https://github.com/ehoogeboom/hexaconv | 57 | Hexaconv | https://scholar.google.com/scholar?cluster=3503620825946735449&hl=en&as_sdt=0,14 | 7 | 2,018 |
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge | 266 | iclr | 17 | 3 | 2023-06-18 08:50:53.611000 | https://github.com/emited/flow | 51 | Deep learning for physical processes: Incorporating prior scientific knowledge | https://scholar.google.com/scholar?cluster=339008717685681020&hl=en&as_sdt=0,15 | 4 | 2,018 |
Communication Algorithms via Deep Learning | 213 | iclr | 48 | 0 | 2023-06-18 08:50:53.812000 | https://github.com/yihanjiang/Sequential-RNN-Decoder | 56 | Communication algorithms via deep learning | https://scholar.google.com/scholar?cluster=3745511757842142493&hl=en&as_sdt=0,6 | 7 | 2,018 |
Unsupervised Cipher Cracking Using Discrete GANs | 71 | iclr | 24 | 6 | 2023-06-18 08:50:54.014000 | https://github.com/for-ai/ciphergan | 122 | Unsupervised cipher cracking using discrete gans | https://scholar.google.com/scholar?cluster=3064134608179971225&hl=en&as_sdt=0,21 | 8 | 2,018 |
Towards Neural Phrase-based Machine Translation | 95 | iclr | 28 | 0 | 2023-06-18 08:50:54.214000 | https://github.com/posenhuang/NPMT | 175 | Towards neural phrase-based machine translation | https://scholar.google.com/scholar?cluster=14839462711165509564&hl=en&as_sdt=0,34 | 22 | 2,018 |
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples | 750 | iclr | 6 | 2 | 2023-06-18 08:50:54.415000 | https://github.com/Microsoft/PixelDefend | 19 | Pixeldefend: Leveraging generative models to understand and defend against adversarial examples | https://scholar.google.com/scholar?cluster=9269726813530152599&hl=en&as_sdt=0,10 | 5 | 2,018 |
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models | 1,154 | iclr | 62 | 13 | 2023-06-18 08:50:54.616000 | https://github.com/kabkabm/defensegan | 218 | Defense-gan: Protecting classifiers against adversarial attacks using generative models | https://scholar.google.com/scholar?cluster=4356922002684962280&hl=en&as_sdt=0,22 | 8 | 2,018 |
Fraternal Dropout | 48 | iclr | 12 | 1 | 2023-06-18 08:50:54.817000 | https://github.com/kondiz/fraternal-dropout | 65 | Fraternal dropout | https://scholar.google.com/scholar?cluster=4593127166702636404&hl=en&as_sdt=0,5 | 4 | 2,018 |
Attacking Binarized Neural Networks | 102 | iclr | 2 | 1 | 2023-06-18 08:50:55.018000 | https://github.com/AngusG/cleverhans-attacking-bnns | 21 | Attacking binarized neural networks | https://scholar.google.com/scholar?cluster=4964512256521124807&hl=en&as_sdt=0,24 | 3 | 2,018 |
Depthwise Separable Convolutions for Neural Machine Translation | 294 | iclr | 3,290 | 589 | 2023-06-18 08:50:55.219000 | https://github.com/tensorflow/tensor2tensor | 13,768 | Depthwise separable convolutions for neural machine translation | https://scholar.google.com/scholar?cluster=7520360878420709403&hl=en&as_sdt=0,10 | 461 | 2,018 |
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization | 19 | iclr | 4 | 0 | 2023-06-18 08:50:55.420000 | https://github.com/BorealisAI/bre-gan | 20 | Improving GAN training via binarized representation entropy (BRE) regularization | https://scholar.google.com/scholar?cluster=14467671840316463321&hl=en&as_sdt=0,3 | 6 | 2,018 |
Generative networks as inverse problems with Scattering transforms | 32 | iclr | 9 | 0 | 2023-06-18 08:50:55.622000 | https://github.com/tomas-angles/generative-scattering-networks | 25 | Generative networks as inverse problems with scattering transforms | https://scholar.google.com/scholar?cluster=2488553421180641259&hl=en&as_sdt=0,5 | 3 | 2,018 |
On the Expressive Power of Overlapping Architectures of Deep Learning | 40 | iclr | 0 | 0 | 2023-06-18 08:50:55.824000 | https://github.com/HUJI-Deep/OverlapsAndExpressiveness | 1 | On the expressive power of overlapping architectures of deep learning | https://scholar.google.com/scholar?cluster=17865700268037263115&hl=en&as_sdt=0,5 | 2 | 2,018 |
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers | 401 | iclr | 9 | 2 | 2023-06-18 08:50:56.025000 | https://github.com/bobye/batchnorm_prune | 30 | Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers | https://scholar.google.com/scholar?cluster=17821725364773859726&hl=en&as_sdt=0,5 | 2 | 2,018 |
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting | 2,227 | iclr | 379 | 21 | 2023-06-18 08:50:56.226000 | https://github.com/liyaguang/DCRNN | 1,011 | Diffusion convolutional recurrent neural network: Data-driven traffic forecasting | https://scholar.google.com/scholar?cluster=6301301566407555232&hl=en&as_sdt=0,5 | 21 | 2,018 |
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions | 279 | iclr | 19 | 10 | 2023-06-18 08:50:56.427000 | https://github.com/sjoerdvansteenkiste/Relational-NEM | 70 | Relational neural expectation maximization: Unsupervised discovery of objects and their interactions | https://scholar.google.com/scholar?cluster=11323622217846680222&hl=en&as_sdt=0,5 | 3 | 2,018 |
Hierarchical Density Order Embeddings | 52 | iclr | 8 | 2 | 2023-06-18 08:50:56.628000 | https://github.com/benathi/density-order-emb | 32 | Hierarchical density order embeddings | https://scholar.google.com/scholar?cluster=12427920250451702495&hl=en&as_sdt=0,33 | 5 | 2,018 |
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks | 124 | iclr | 19 | 1 | 2023-06-18 08:50:56.829000 | https://github.com/chrisdonahue/sdgan | 94 | Semantically decomposing the latent spaces of generative adversarial networks | https://scholar.google.com/scholar?cluster=8664262583947148240&hl=en&as_sdt=0,44 | 8 | 2,018 |
A Framework for the Quantitative Evaluation of Disentangled Representations | 356 | iclr | 9 | 0 | 2023-06-18 08:50:57.030000 | https://github.com/cianeastwood/qedr | 56 | A framework for the quantitative evaluation of disentangled representations | https://scholar.google.com/scholar?cluster=3224087322020629595&hl=en&as_sdt=0,5 | 2 | 2,018 |
Meta-Learning for Semi-Supervised Few-Shot Classification | 1,205 | iclr | 99 | 12 | 2023-06-18 08:50:57.231000 | https://github.com/renmengye/few-shot-ssl-public | 514 | Meta-learning for semi-supervised few-shot classification | https://scholar.google.com/scholar?cluster=798380540199769906&hl=en&as_sdt=0,44 | 18 | 2,018 |
A DIRT-T Approach to Unsupervised Domain Adaptation | 541 | iclr | 35 | 1 | 2023-06-18 08:50:57.432000 | https://github.com/RuiShu/dirt-t | 171 | A dirt-t approach to unsupervised domain adaptation | https://scholar.google.com/scholar?cluster=8960716763873957731&hl=en&as_sdt=0,3 | 7 | 2,018 |
Generalizing Across Domains via Cross-Gradient Training | 394 | iclr | 5 | 1 | 2023-06-18 08:50:57.632000 | https://github.com/vihari/crossgrad | 21 | Generalizing across domains via cross-gradient training | https://scholar.google.com/scholar?cluster=4167124586655060881&hl=en&as_sdt=0,5 | 5 | 2,018 |
Deep Complex Networks | 327 | iclr | 268 | 22 | 2023-06-18 08:50:57.832000 | https://github.com/ChihebTrabelsi/deep_complex_networks | 655 | Deep complex networks | https://scholar.google.com/scholar?cluster=18218729763326747000&hl=en&as_sdt=0,48 | 40 | 2,018 |
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling | 157 | iclr | 27 | 0 | 2023-06-18 08:50:58.034000 | https://github.com/taoshen58/BiBloSA | 123 | Bi-directional block self-attention for fast and memory-efficient sequence modeling | https://scholar.google.com/scholar?cluster=7203374430207428965&hl=en&as_sdt=0,33 | 7 | 2,018 |
Training wide residual networks for deployment using a single bit for each weight | 74 | iclr | 10 | 0 | 2023-06-18 08:50:58.234000 | https://github.com/McDonnell-Lab/1-bit-per-weight | 35 | Training wide residual networks for deployment using a single bit for each weight | https://scholar.google.com/scholar?cluster=7686605623349914581&hl=en&as_sdt=0,33 | 7 | 2,018 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.