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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 |
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