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Improving Generalization and Stability of Generative Adversarial Networks | 126 | iclr | 7 | 0 | 2023-06-18 08:57:54.295000 | https://github.com/htt210/GeneralizationAndStabilityInGANs | 36 | Improving generalization and stability of generative adversarial networks | https://scholar.google.com/scholar?cluster=13499019185526283919&hl=en&as_sdt=0,15 | 3 | 2,019 |
Adaptive Input Representations for Neural Language Modeling | 317 | iclr | 5,883 | 1,031 | 2023-06-18 08:57:54.497000 | https://github.com/pytorch/fairseq | 26,500 | Adaptive input representations for neural language modeling | https://scholar.google.com/scholar?cluster=9932684582274973195&hl=en&as_sdt=0,32 | 411 | 2,019 |
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology | 102 | iclr | 6 | 0 | 2023-06-18 08:57:54.698000 | https://github.com/BorgwardtLab/Neural-Persistence | 24 | Neural persistence: A complexity measure for deep neural networks using algebraic topology | https://scholar.google.com/scholar?cluster=12286997751595249495&hl=en&as_sdt=0,44 | 9 | 2,019 |
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model | 10 | iclr | 6 | 1 | 2023-06-18 08:57:54.900000 | https://github.com/florianmai/word2mat | 20 | CBOW is not all you need: Combining CBOW with the compositional matrix space model | https://scholar.google.com/scholar?cluster=6038502138949255694&hl=en&as_sdt=0,43 | 1 | 2,019 |
Stochastic Optimization of Sorting Networks via Continuous Relaxations | 110 | iclr | 24 | 6 | 2023-06-18 08:57:55.101000 | https://github.com/ermongroup/neuralsort | 119 | Stochastic optimization of sorting networks via continuous relaxations | https://scholar.google.com/scholar?cluster=10619362619006891050&hl=en&as_sdt=0,44 | 10 | 2,019 |
Generating Multiple Objects at Spatially Distinct Locations | 105 | iclr | 14 | 7 | 2023-06-18 08:57:55.303000 | https://github.com/tohinz/multiple-objects-gan | 111 | Generating multiple objects at spatially distinct locations | https://scholar.google.com/scholar?cluster=13574885695794039292&hl=en&as_sdt=0,14 | 7 | 2,019 |
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning | 176 | iclr | 46,278 | 1,207 | 2023-06-18 08:57:55.504000 | https://github.com/tensorflow/models | 75,928 | Near-optimal representation learning for hierarchical reinforcement learning | https://scholar.google.com/scholar?cluster=17682749665983906973&hl=en&as_sdt=0,14 | 2,774 | 2,019 |
Understanding Composition of Word Embeddings via Tensor Decomposition | 6 | iclr | 1 | 0 | 2023-06-18 08:57:55.708000 | https://github.com/abefrandsen/syntactic-rand-walk | 5 | Understanding composition of word embeddings via tensor decomposition | https://scholar.google.com/scholar?cluster=9072436238425463642&hl=en&as_sdt=0,34 | 4 | 2,019 |
Structured Neural Summarization | 204 | iclr | 26 | 11 | 2023-06-18 08:57:55.910000 | https://github.com/CoderPat/structured-neural-summarization | 74 | Structured neural summarization | https://scholar.google.com/scholar?cluster=5961913139611201410&hl=en&as_sdt=0,5 | 3 | 2,019 |
Supervised Community Detection with Line Graph Neural Networks | 271 | iclr | 18 | 0 | 2023-06-18 08:57:56.111000 | https://github.com/zhengdao-chen/GNN4CD | 76 | Supervised community detection with line graph neural networks | https://scholar.google.com/scholar?cluster=5008209229610559765&hl=en&as_sdt=0,5 | 3 | 2,019 |
code2seq: Generating Sequences from Structured Representations of Code | 605 | iclr | 152 | 11 | 2023-06-18 08:57:56.312000 | https://github.com/tech-srl/code2seq | 494 | code2seq: Generating sequences from structured representations of code | https://scholar.google.com/scholar?cluster=14844338714783082531&hl=en&as_sdt=0,5 | 16 | 2,019 |
Predict then Propagate: Graph Neural Networks meet Personalized PageRank | 1,085 | iclr | 53 | 0 | 2023-06-18 08:57:56.513000 | https://github.com/klicperajo/ppnp | 298 | Predict then propagate: Graph neural networks meet personalized pagerank | https://scholar.google.com/scholar?cluster=12842465886565513517&hl=en&as_sdt=0,4 | 9 | 2,019 |
Slimmable Neural Networks | 477 | iclr | 131 | 11 | 2023-06-18 08:57:56.716000 | https://github.com/JiahuiYu/slimmable_networks | 883 | Slimmable neural networks | https://scholar.google.com/scholar?cluster=15212173000600372424&hl=en&as_sdt=0,14 | 30 | 2,019 |
Exploration by random network distillation | 982 | iclr | 153 | 17 | 2023-06-18 08:57:56.917000 | https://github.com/openai/random-network-distillation | 811 | Exploration by random network distillation | https://scholar.google.com/scholar?cluster=126098205768710278&hl=en&as_sdt=0,10 | 26 | 2,019 |
Latent Convolutional Models | 30 | iclr | 5 | 2 | 2023-06-18 08:57:57.118000 | https://github.com/srxdev0619/Latent_Convolutional_Models | 17 | Latent convolutional models | https://scholar.google.com/scholar?cluster=1201013501878383620&hl=en&as_sdt=0,5 | 5 | 2,019 |
A Universal Music Translation Network | 137 | iclr | 73 | 9 | 2023-06-18 08:57:57.318000 | https://github.com/facebookresearch/music-translation | 446 | A universal music translation network | https://scholar.google.com/scholar?cluster=6168332349111008894&hl=en&as_sdt=0,3 | 21 | 2,019 |
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition | 79 | iclr | 13 | 1 | 2023-06-18 08:57:57.519000 | https://github.com/IBM/BigLittleNet | 55 | Big-little net: An efficient multi-scale feature representation for visual and speech recognition | https://scholar.google.com/scholar?cluster=555905086227832192&hl=en&as_sdt=0,38 | 9 | 2,019 |
Active Learning with Partial Feedback | 55 | iclr | 4 | 1 | 2023-06-18 08:57:57.720000 | https://github.com/peiyunh/alpf | 11 | Active learning with partial feedback | https://scholar.google.com/scholar?cluster=2828167692054854631&hl=en&as_sdt=0,34 | 2 | 2,019 |
DOM-Q-NET: Grounded RL on Structured Language | 20 | iclr | 10 | 1 | 2023-06-18 08:57:57.922000 | https://github.com/Sheng-J/DOM-Q-NET | 44 | Dom-q-net: Grounded rl on structured language | https://scholar.google.com/scholar?cluster=10126688324952353090&hl=en&as_sdt=0,47 | 2 | 2,019 |
Predicting the Generalization Gap in Deep Networks with Margin Distributions | 169 | iclr | 7,332 | 1,026 | 2023-06-18 08:57:58.123000 | https://github.com/google-research/google-research | 29,803 | Predicting the generalization gap in deep networks with margin distributions | https://scholar.google.com/scholar?cluster=13633337648471293543&hl=en&as_sdt=0,14 | 728 | 2,019 |
Measuring Compositionality in Representation Learning | 119 | iclr | 6 | 3 | 2023-06-18 08:57:58.324000 | https://github.com/jacobandreas/tre | 67 | Measuring compositionality in representation learning | https://scholar.google.com/scholar?cluster=36884338001216785&hl=en&as_sdt=0,5 | 2 | 2,019 |
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations | 2,199 | iclr | 138 | 9 | 2023-06-18 08:57:58.526000 | https://github.com/hendrycks/robustness | 846 | Benchmarking neural network robustness to common corruptions and perturbations | https://scholar.google.com/scholar?cluster=4440880036617273374&hl=en&as_sdt=0,24 | 12 | 2,019 |
Learning Recurrent Binary/Ternary Weights | 30 | iclr | 2 | 2 | 2023-06-18 08:57:58.727000 | https://github.com/arashardakani/Learning-Recurrent-Binary-Ternary-Weights | 12 | Learning recurrent binary/ternary weights | https://scholar.google.com/scholar?cluster=14324986620118227094&hl=en&as_sdt=0,5 | 1 | 2,019 |
Residual Non-local Attention Networks for Image Restoration | 573 | iclr | 55 | 17 | 2023-06-18 08:57:58.929000 | https://github.com/yulunzhang/RNAN | 327 | Residual non-local attention networks for image restoration | https://scholar.google.com/scholar?cluster=5425381515618577679&hl=en&as_sdt=0,10 | 15 | 2,019 |
Meta-Learning For Stochastic Gradient MCMC | 43 | iclr | 4 | 0 | 2023-06-18 08:57:59.130000 | https://github.com/WenboGong/MetaSGMCMC | 23 | Meta-learning for stochastic gradient MCMC | https://scholar.google.com/scholar?cluster=5266885862075190072&hl=en&as_sdt=0,11 | 7 | 2,019 |
Systematic Generalization: What Is Required and Can It Be Learned? | 169 | iclr | 11 | 2 | 2023-06-18 08:57:59.331000 | https://github.com/rizar/systematic-generalization-sqoop | 38 | Systematic generalization: What is required and can it be learned? | https://scholar.google.com/scholar?cluster=376953749686735892&hl=en&as_sdt=0,5 | 6 | 2,019 |
Efficient Lifelong Learning with A-GEM | 920 | iclr | 41 | 6 | 2023-06-18 08:57:59.531000 | https://github.com/facebookresearch/agem | 188 | Efficient lifelong learning with a-gem | https://scholar.google.com/scholar?cluster=14191909055509326948&hl=en&as_sdt=0,33 | 11 | 2,019 |
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering | 175 | iclr | 15 | 5 | 2023-06-18 08:57:59.733000 | https://github.com/rajarshd/Multi-Step-Reasoning | 118 | Multi-step retriever-reader interaction for scalable open-domain question answering | https://scholar.google.com/scholar?cluster=17865791345794061973&hl=en&as_sdt=0,5 | 7 | 2,019 |
Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision | 27 | iclr | 0 | 1 | 2023-06-18 08:57:59.934000 | https://github.com/jlezama/disentangling-jacobian | 24 | Overcoming the disentanglement vs reconstruction trade-off via Jacobian supervision | https://scholar.google.com/scholar?cluster=72617481773116679&hl=en&as_sdt=0,23 | 3 | 2,019 |
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space | 1,517 | iclr | 250 | 7 | 2023-06-18 08:58:00.136000 | https://github.com/DeepGraphLearning/KnowledgeGraphEmbedding | 1,051 | Rotate: Knowledge graph embedding by relational rotation in complex space | https://scholar.google.com/scholar?cluster=9820389801132772086&hl=en&as_sdt=0,5 | 24 | 2,019 |
Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications | 11 | iclr | 0 | 0 | 2023-06-18 08:58:00.336000 | https://github.com/ceisenach/MPG | 3 | Marginal policy gradients: A unified family of estimators for bounded action spaces with applications | https://scholar.google.com/scholar?cluster=14825352687327812567&hl=en&as_sdt=0,36 | 4 | 2,019 |
On Self Modulation for Generative Adversarial Networks | 104 | iclr | 322 | 16 | 2023-06-18 08:58:00.537000 | https://github.com/google/compare_gan | 1,814 | On self modulation for generative adversarial networks | https://scholar.google.com/scholar?cluster=14481067201346722037&hl=en&as_sdt=0,44 | 52 | 2,019 |
Subgradient Descent Learns Orthogonal Dictionaries | 49 | iclr | 1 | 0 | 2023-06-18 08:58:00.738000 | https://github.com/sunju/ODL_L1 | 1 | Subgradient descent learns orthogonal dictionaries | https://scholar.google.com/scholar?cluster=3757427846147866582&hl=en&as_sdt=0,26 | 4 | 2,019 |
A Closer Look at Few-shot Classification | 1,513 | iclr | 271 | 60 | 2023-06-18 08:58:00.939000 | https://github.com/wyharveychen/CloserLookFewShot | 1,064 | A closer look at few-shot classification | https://scholar.google.com/scholar?cluster=10436738309048088927&hl=en&as_sdt=0,4 | 20 | 2,019 |
Meta-Learning Probabilistic Inference for Prediction | 230 | iclr | 13 | 0 | 2023-06-18 08:58:01.140000 | https://github.com/Gordonjo/versa | 68 | Meta-learning probabilistic inference for prediction | https://scholar.google.com/scholar?cluster=18291407046711557858&hl=en&as_sdt=0,5 | 7 | 2,019 |
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling | 65 | iclr | 6 | 0 | 2023-06-18 08:58:01.341000 | https://github.com/catniplab/tree_structured_rslds | 30 | Tree-structured recurrent switching linear dynamical systems for multi-scale modeling | https://scholar.google.com/scholar?cluster=10945679458649765039&hl=en&as_sdt=0,6 | 4 | 2,019 |
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control | 35 | iclr | 7 | 1 | 2023-06-18 08:58:01.542000 | https://github.com/robertcsordas/dnc | 25 | Improving differentiable neural computers through memory masking, de-allocation, and link distribution sharpness control | https://scholar.google.com/scholar?cluster=9465849868631633208&hl=en&as_sdt=0,45 | 1 | 2,019 |
Evaluating Robustness of Neural Networks with Mixed Integer Programming | 665 | iclr | 30 | 7 | 2023-06-18 08:58:01.743000 | https://github.com/vtjeng/MIPVerify.jl | 106 | Evaluating robustness of neural networks with mixed integer programming | https://scholar.google.com/scholar?cluster=18154476008132424293&hl=en&as_sdt=0,48 | 4 | 2,019 |
Random mesh projectors for inverse problems | 7 | iclr | 4 | 0 | 2023-06-18 08:58:01.945000 | https://github.com/swing-research/deepmesh | 23 | Random mesh projectors for inverse problems | https://scholar.google.com/scholar?cluster=1149610136001098856&hl=en&as_sdt=0,5 | 9 | 2,019 |
Complement Objective Training | 49 | iclr | 9 | 2 | 2023-06-18 08:58:02.146000 | https://github.com/henry8527/COT | 74 | Complement objective training | https://scholar.google.com/scholar?cluster=63949908447902569&hl=en&as_sdt=0,2 | 7 | 2,019 |
Trellis Networks for Sequence Modeling | 125 | iclr | 63 | 1 | 2023-06-18 08:58:02.347000 | https://github.com/locuslab/trellisnet | 464 | Trellis networks for sequence modeling | https://scholar.google.com/scholar?cluster=13782940196634240151&hl=en&as_sdt=0,5 | 24 | 2,019 |
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks | 49 | iclr | 0 | 0 | 2023-06-18 08:58:02.549000 | https://github.com/uhlerlab/unbalanced_ot | 1 | Scalable unbalanced optimal transport using generative adversarial networks | https://scholar.google.com/scholar?cluster=14112773597586866494&hl=en&as_sdt=0,21 | 3 | 2,019 |
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic | 118 | iclr | 56 | 6 | 2023-06-18 08:58:02.750000 | https://github.com/Atcold/pytorch-PPUU | 188 | Model-predictive policy learning with uncertainty regularization for driving in dense traffic | https://scholar.google.com/scholar?cluster=5048415252406845644&hl=en&as_sdt=0,5 | 25 | 2,019 |
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks | 472 | iclr | 286 | 15 | 2023-06-18 08:58:02.952000 | https://github.com/CSAILVision/gandissect | 1,749 | Gan dissection: Visualizing and understanding generative adversarial networks | https://scholar.google.com/scholar?cluster=197925763027882731&hl=en&as_sdt=0,5 | 75 | 2,019 |
Improving MMD-GAN Training with Repulsive Loss Function | 60 | iclr | 19 | 2 | 2023-06-18 08:58:03.154000 | https://github.com/richardwth/MMD-GAN | 82 | Improving MMD-GAN training with repulsive loss function | https://scholar.google.com/scholar?cluster=5981776109708607840&hl=en&as_sdt=0,49 | 5 | 2,019 |
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware | 1,665 | iclr | 281 | 2 | 2023-06-18 08:58:03.354000 | https://github.com/MIT-HAN-LAB/ProxylessNAS | 1,379 | Proxylessnas: Direct neural architecture search on target task and hardware | https://scholar.google.com/scholar?cluster=18033301425061747520&hl=en&as_sdt=0,5 | 73 | 2,019 |
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization | 34 | iclr | 5 | 0 | 2023-06-18 08:58:03.555000 | https://github.com/TakaOsa/adInfoHRL | 6 | Hierarchical reinforcement learning via advantage-weighted information maximization | https://scholar.google.com/scholar?cluster=8371143208721459013&hl=en&as_sdt=0,15 | 2 | 2,019 |
Generalizable Adversarial Training via Spectral Normalization | 122 | iclr | 4 | 0 | 2023-06-18 08:58:03.757000 | https://github.com/jessemzhang/dl_spectral_normalization | 13 | Generalizable adversarial training via spectral normalization | https://scholar.google.com/scholar?cluster=16959420457208400665&hl=en&as_sdt=0,14 | 3 | 2,019 |
Deep Anomaly Detection with Outlier Exposure | 1,070 | iclr | 102 | 3 | 2023-06-18 08:58:03.960000 | https://github.com/hendrycks/outlier-exposure | 498 | Deep anomaly detection with outlier exposure | https://scholar.google.com/scholar?cluster=13915279318347653817&hl=en&as_sdt=0,5 | 19 | 2,019 |
Context-adaptive Entropy Model for End-to-end Optimized Image Compression | 318 | iclr | 29 | 1 | 2023-06-18 08:58:04.161000 | https://github.com/JooyoungLeeETRI/CA_Entropy_Model | 134 | Context-adaptive entropy model for end-to-end optimized image compression | https://scholar.google.com/scholar?cluster=17458297235582784877&hl=en&as_sdt=0,5 | 3 | 2,019 |
ProxQuant: Quantized Neural Networks via Proximal Operators | 96 | iclr | 3 | 3 | 2023-06-18 08:58:04.363000 | https://github.com/allenbai01/ProxQuant | 23 | Proxquant: Quantized neural networks via proximal operators | https://scholar.google.com/scholar?cluster=13740367040689029941&hl=en&as_sdt=0,47 | 3 | 2,019 |
Universal Transformers | 698 | iclr | 3,290 | 589 | 2023-06-18 08:58:04.564000 | https://github.com/tensorflow/tensor2tensor | 13,768 | Universal transformers | https://scholar.google.com/scholar?cluster=8443376534582904234&hl=en&as_sdt=0,44 | 461 | 2,019 |
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data | 170 | iclr | 4 | 1 | 2023-06-18 08:58:04.766000 | https://github.com/Jianbo-Lab/LCShapley | 15 | L-shapley and c-shapley: Efficient model interpretation for structured data | https://scholar.google.com/scholar?cluster=13478206087371335896&hl=en&as_sdt=0,14 | 8 | 2,019 |
Discovery of Natural Language Concepts in Individual Units of CNNs | 19 | iclr | 1 | 0 | 2023-06-18 08:58:04.971000 | https://github.com/seilna/CNN-Units-in-NLP | 27 | Discovery of natural language concepts in individual units of cnns | https://scholar.google.com/scholar?cluster=16647657304104807726&hl=en&as_sdt=0,10 | 3 | 2,019 |
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams | 31 | iclr | 16 | 0 | 2023-06-18 08:58:05.172000 | https://github.com/mkachuee/Opportunistic | 10 | Opportunistic learning: Budgeted cost-sensitive learning from data streams | https://scholar.google.com/scholar?cluster=926797319762361897&hl=en&as_sdt=0,5 | 3 | 2,019 |
DARTS: Differentiable Architecture Search | 3,734 | iclr | 831 | 92 | 2023-06-18 08:58:05.374000 | https://github.com/quark0/darts | 3,757 | Darts: Differentiable architecture search | https://scholar.google.com/scholar?cluster=895422516420751823&hl=en&as_sdt=0,22 | 92 | 2,019 |
The relativistic discriminator: a key element missing from standard GAN | 966 | iclr | 106 | 1 | 2023-06-18 08:58:05.575000 | https://github.com/AlexiaJM/RelativisticGAN | 706 | The relativistic discriminator: a key element missing from standard GAN | https://scholar.google.com/scholar?cluster=9348243398459465041&hl=en&as_sdt=0,6 | 26 | 2,019 |
Quasi-hyperbolic momentum and Adam for deep learning | 118 | iclr | 15 | 2 | 2023-06-18 08:58:05.776000 | https://github.com/facebookresearch/qhoptim | 99 | Quasi-hyperbolic momentum and Adam for deep learning | https://scholar.google.com/scholar?cluster=4018448922538302075&hl=en&as_sdt=0,5 | 10 | 2,019 |
Multilingual Neural Machine Translation with Knowledge Distillation | 205 | iclr | 18 | 5 | 2023-06-18 08:58:05.978000 | https://github.com/RayeRen/multilingual-kd-pytorch | 69 | Multilingual neural machine translation with knowledge distillation | https://scholar.google.com/scholar?cluster=5753623392275205285&hl=en&as_sdt=0,33 | 4 | 2,019 |
MisGAN: Learning from Incomplete Data with Generative Adversarial Networks | 177 | iclr | 18 | 2 | 2023-06-18 08:58:06.180000 | https://github.com/steveli/misgan | 77 | Misgan: Learning from incomplete data with generative adversarial networks | https://scholar.google.com/scholar?cluster=4415656656646533426&hl=en&as_sdt=0,47 | 3 | 2,019 |
A Direct Approach to Robust Deep Learning Using Adversarial Networks | 62 | iclr | 3 | 3 | 2023-06-18 08:58:06.380000 | https://github.com/whxbergkamp/RobustDL_GAN | 20 | A direct approach to robust deep learning using adversarial networks | https://scholar.google.com/scholar?cluster=2332293430655643076&hl=en&as_sdt=0,5 | 2 | 2,019 |
ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks | 60 | iclr | 9 | 0 | 2023-06-18 08:58:06.582000 | https://github.com/mingzhang-yin/ARM-gradient | 28 | ARM: Augment-REINFORCE-merge gradient for stochastic binary networks | https://scholar.google.com/scholar?cluster=1199474822347449770&hl=en&as_sdt=0,34 | 2 | 2,019 |
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer | 100 | iclr | 0 | 3 | 2023-06-18 08:58:06.783000 | https://github.com/huangsicong/TimbreTron | 43 | Timbretron: A wavenet (cyclegan (cqt (audio))) pipeline for musical timbre transfer | https://scholar.google.com/scholar?cluster=11196022310662002190&hl=en&as_sdt=0,33 | 19 | 2,019 |
Whitening and Coloring Batch Transform for GANs | 51 | iclr | 10 | 0 | 2023-06-18 08:58:06.985000 | https://github.com/AliaksandrSiarohin/wc-gan | 34 | Whitening and coloring batch transform for gans | https://scholar.google.com/scholar?cluster=8343777033924906329&hl=en&as_sdt=0,39 | 5 | 2,019 |
Learnable Embedding Space for Efficient Neural Architecture Compression | 46 | iclr | 3 | 1 | 2023-06-18 08:58:07.189000 | https://github.com/Friedrich1006/ESNAC | 28 | Learnable embedding space for efficient neural architecture compression | https://scholar.google.com/scholar?cluster=117198627951999316&hl=en&as_sdt=0,33 | 4 | 2,019 |
A Statistical Approach to Assessing Neural Network Robustness | 68 | iclr | 9 | 0 | 2023-06-18 08:58:07.390000 | https://github.com/oval-group/statistical-robustness | 8 | A statistical approach to assessing neural network robustness | https://scholar.google.com/scholar?cluster=7897732150648450452&hl=en&as_sdt=0,5 | 12 | 2,019 |
Supervised Policy Update for Deep Reinforcement Learning | 20 | iclr | 2 | 21 | 2023-06-18 08:58:07.592000 | https://github.com/quanvuong/Supervised_Policy_Update | 17 | Supervised policy update for deep reinforcement learning | https://scholar.google.com/scholar?cluster=9669638111330201224&hl=en&as_sdt=0,3 | 3 | 2,019 |
Learning to Schedule Communication in Multi-agent Reinforcement Learning | 154 | iclr | 27 | 2 | 2023-06-18 08:58:07.794000 | https://github.com/rhoowd/sched_net | 68 | Learning to schedule communication in multi-agent reinforcement learning | https://scholar.google.com/scholar?cluster=2430706253185717368&hl=en&as_sdt=0,10 | 5 | 2,019 |
Multi-class classification without multi-class labels | 118 | iclr | 49 | 3 | 2023-06-18 08:58:07.994000 | https://github.com/GT-RIPL/L2C | 306 | Multi-class classification without multi-class labels | https://scholar.google.com/scholar?cluster=15660059153270341215&hl=en&as_sdt=0,18 | 20 | 2,019 |
Spectral Inference Networks: Unifying Deep and Spectral Learning | 30 | iclr | 27 | 2 | 2023-06-18 08:58:08.195000 | https://github.com/deepmind/spectral_inference_networks | 165 | Spectral inference networks: Unifying deep and spectral learning | https://scholar.google.com/scholar?cluster=16660579419089969631&hl=en&as_sdt=0,10 | 14 | 2,019 |
Attentive Neural Processes | 312 | iclr | 146 | 8 | 2023-06-18 08:58:08.397000 | https://github.com/deepmind/neural-processes | 929 | Attentive neural processes | https://scholar.google.com/scholar?cluster=6519833436864425356&hl=en&as_sdt=0,5 | 42 | 2,019 |
Hierarchical interpretations for neural network predictions | 126 | iclr | 21 | 2 | 2023-06-18 08:58:08.598000 | https://github.com/csinva/hierarchical-dnn-interpretations | 114 | Hierarchical interpretations for neural network predictions | https://scholar.google.com/scholar?cluster=14523630218994203463&hl=en&as_sdt=0,33 | 10 | 2,019 |
Spreading vectors for similarity search | 70 | iclr | 37 | 0 | 2023-06-18 08:58:08.799000 | https://github.com/facebookresearch/spreadingvectors | 308 | Spreading vectors for similarity search | https://scholar.google.com/scholar?cluster=7912762574684423820&hl=en&as_sdt=0,5 | 15 | 2,019 |
Episodic Curiosity through Reachability | 249 | iclr | 34 | 6 | 2023-06-18 08:58:09.001000 | https://github.com/google-research/episodic-curiosity | 188 | Episodic curiosity through reachability | https://scholar.google.com/scholar?cluster=3202653392377789217&hl=en&as_sdt=0,34 | 12 | 2,019 |
Multilingual Neural Machine Translation With Soft Decoupled Encoding | 53 | iclr | 3 | 0 | 2023-06-18 08:58:09.202000 | https://github.com/cindyxinyiwang/SDE | 28 | Multilingual neural machine translation with soft decoupled encoding | https://scholar.google.com/scholar?cluster=1841872742547049658&hl=en&as_sdt=0,5 | 2 | 2,019 |
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet | 523 | iclr | 45 | 6 | 2023-06-18 08:58:09.403000 | https://github.com/wielandbrendel/bag-of-local-features-models | 304 | Approximating cnns with bag-of-local-features models works surprisingly well on imagenet | https://scholar.google.com/scholar?cluster=13421262728275736184&hl=en&as_sdt=0,5 | 11 | 2,019 |
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length | 82 | iclr | 2 | 8 | 2023-06-18 08:58:09.612000 | https://github.com/kudkudak/dnn_sharpest_directions | 11 | On the relation between the sharpest directions of DNN loss and the SGD step length | https://scholar.google.com/scholar?cluster=3857357074541596262&hl=en&as_sdt=0,33 | 3 | 2,019 |
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos | 9 | iclr | 0 | 0 | 2023-06-18 08:58:09.813000 | https://github.com/EKirschbaum/LeMoNADe | 3 | LeMoNADe: Learned motif and neuronal assembly detection in calcium imaging videos | https://scholar.google.com/scholar?cluster=16794354699308703573&hl=en&as_sdt=0,36 | 2 | 2,019 |
Multi-Domain Adversarial Learning | 63 | iclr | 6 | 2 | 2023-06-18 08:58:10.015000 | https://github.com/AltschulerWu-Lab/MuLANN | 38 | Multi-domain adversarial learning | https://scholar.google.com/scholar?cluster=12918642192245741417&hl=en&as_sdt=0,10 | 5 | 2,019 |
ProMP: Proximal Meta-Policy Search | 193 | iclr | 50 | 8 | 2023-06-18 08:58:10.217000 | https://github.com/jonasrothfuss/promp | 222 | Promp: Proximal meta-policy search | https://scholar.google.com/scholar?cluster=5271959514847376578&hl=en&as_sdt=0,33 | 14 | 2,019 |
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors | 47 | iclr | 4 | 1 | 2023-06-18 08:58:10.418000 | https://github.com/Babylonpartners/fuzzymax | 43 | Don't settle for average, go for the max: fuzzy sets and max-pooled word vectors | https://scholar.google.com/scholar?cluster=17199150617564073243&hl=en&as_sdt=0,5 | 118 | 2,019 |
Learning Exploration Policies for Navigation | 177 | iclr | 17 | 2 | 2023-06-18 08:58:10.620000 | https://github.com/taochenshh/exp4nav | 83 | Learning exploration policies for navigation | https://scholar.google.com/scholar?cluster=1526633576375251578&hl=en&as_sdt=0,47 | 3 | 2,019 |
Deep Frank-Wolfe For Neural Network Optimization | 40 | iclr | 10 | 0 | 2023-06-18 08:58:10.821000 | https://github.com/oval-group/dfw | 57 | Deep Frank-Wolfe for neural network optimization | https://scholar.google.com/scholar?cluster=17584931574409094808&hl=en&as_sdt=0,33 | 12 | 2,019 |
Learning protein sequence embeddings using information from structure | 242 | iclr | 72 | 3 | 2023-06-18 08:58:11.022000 | https://github.com/tbepler/protein-sequence-embedding-iclr2019 | 239 | Learning protein sequence embeddings using information from structure | https://scholar.google.com/scholar?cluster=15164585032422536283&hl=en&as_sdt=0,5 | 11 | 2,019 |
Biologically-Plausible Learning Algorithms Can Scale to Large Datasets | 58 | iclr | 4 | 0 | 2023-06-18 08:58:11.224000 | https://github.com/willwx/sign-symmetry | 24 | Biologically-plausible learning algorithms can scale to large datasets | https://scholar.google.com/scholar?cluster=10952740218459903429&hl=en&as_sdt=0,36 | 0 | 2,019 |
Learning to Make Analogies by Contrasting Abstract Relational Structure | 78 | iclr | 36 | 5 | 2023-06-18 08:58:11.426000 | https://github.com/deepmind/abstract-reasoning-matrices | 162 | Learning to make analogies by contrasting abstract relational structure | https://scholar.google.com/scholar?cluster=15521573039503233138&hl=en&as_sdt=0,5 | 24 | 2,019 |
Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion | 28 | iclr | 2 | 0 | 2023-06-18 08:58:11.627000 | https://github.com/ruiqigao/GridCell | 16 | Learning grid cells as vector representation of self-position coupled with matrix representation of self-motion | https://scholar.google.com/scholar?cluster=1267366913161335013&hl=en&as_sdt=0,33 | 4 | 2,019 |
Feature Intertwiner for Object Detection | 19 | iclr | 15 | 5 | 2023-06-18 08:58:11.828000 | https://github.com/hli2020/feature_intertwiner | 106 | Feature intertwiner for object detection | https://scholar.google.com/scholar?cluster=1331733591833237522&hl=en&as_sdt=0,5 | 8 | 2,019 |
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation | 220 | iclr | 17 | 9 | 2023-06-18 08:58:12.029000 | https://github.com/chihyaoma/selfmonitoring-agent | 113 | Self-monitoring navigation agent via auxiliary progress estimation | https://scholar.google.com/scholar?cluster=5431855784757864150&hl=en&as_sdt=0,33 | 6 | 2,019 |
Kernel Change-point Detection with Auxiliary Deep Generative Models | 65 | iclr | 13 | 3 | 2023-06-18 08:58:12.230000 | https://github.com/OctoberChang/klcpd_code | 46 | Kernel change-point detection with auxiliary deep generative models | https://scholar.google.com/scholar?cluster=15362141737124631231&hl=en&as_sdt=0,46 | 2 | 2,019 |
Auxiliary Variational MCMC | 26 | iclr | 2 | 0 | 2023-06-18 08:58:12.431000 | https://github.com/AVMCMC/AuxiliaryVariationalMCMC | 17 | Auxiliary variational MCMC | https://scholar.google.com/scholar?cluster=16399175938915448128&hl=en&as_sdt=0,46 | 1 | 2,019 |
Interpolation-Prediction Networks for Irregularly Sampled Time Series | 114 | iclr | 14 | 5 | 2023-06-18 08:58:12.632000 | https://github.com/mlds-lab/interp-net | 75 | Interpolation-prediction networks for irregularly sampled time series | https://scholar.google.com/scholar?cluster=15477406781147246766&hl=en&as_sdt=0,5 | 9 | 2,019 |
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters | 49 | iclr | 4 | 0 | 2023-06-18 08:58:12.848000 | https://github.com/cambridge-mlg/miracle | 18 | Minimal random code learning: Getting bits back from compressed model parameters | https://scholar.google.com/scholar?cluster=17962712491875468296&hl=en&as_sdt=0,5 | 3 | 2,019 |
Equi-normalization of Neural Networks | 369 | iclr | 13 | 0 | 2023-06-18 08:58:13.050000 | https://github.com/facebookresearch/enorm | 114 | Data-free quantization through weight equalization and bias correction | https://scholar.google.com/scholar?cluster=7650143789920544723&hl=en&as_sdt=0,33 | 10 | 2,019 |
A Variational Inequality Perspective on Generative Adversarial Networks | 341 | iclr | 11 | 0 | 2023-06-18 08:58:13.251000 | https://github.com/GauthierGidel/Variational-Inequality-GAN | 36 | A variational inequality perspective on generative adversarial networks | https://scholar.google.com/scholar?cluster=6445881932716952872&hl=en&as_sdt=0,24 | 5 | 2,019 |
GamePad: A Learning Environment for Theorem Proving | 83 | iclr | 15 | 12 | 2023-06-18 08:58:13.453000 | https://github.com/ml4tp/gamepad | 66 | Gamepad: A learning environment for theorem proving | https://scholar.google.com/scholar?cluster=10460600857870546205&hl=en&as_sdt=0,5 | 9 | 2,019 |
Large-Scale Study of Curiosity-Driven Learning | 677 | iclr | 178 | 14 | 2023-06-18 08:58:13.661000 | https://github.com/openai/large-scale-curiosity | 783 | Large-scale study of curiosity-driven learning | https://scholar.google.com/scholar?cluster=6931272873542879959&hl=en&as_sdt=0,5 | 63 | 2,019 |
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning | 138 | iclr | 135 | 9 | 2023-06-18 08:58:13.866000 | https://github.com/mila-iqia/babyai | 609 | Babyai: A platform to study the sample efficiency of grounded language learning | https://scholar.google.com/scholar?cluster=16615836502291630253&hl=en&as_sdt=0,33 | 36 | 2,019 |
An Empirical study of Binary Neural Networks' Optimisation | 72 | iclr | 10 | 0 | 2023-06-18 08:58:14.067000 | https://github.com/mi-lad/studying-binary-neural-networks | 51 | An empirical study of binary neural networks' optimisation | https://scholar.google.com/scholar?cluster=9499204720789675846&hl=en&as_sdt=0,31 | 5 | 2,019 |
DeepOBS: A Deep Learning Optimizer Benchmark Suite | 47 | iclr | 34 | 16 | 2023-06-18 08:58:14.269000 | https://github.com/fsschneider/deepobs | 97 | DeepOBS: A deep learning optimizer benchmark suite | https://scholar.google.com/scholar?cluster=10657953635405668036&hl=en&as_sdt=0,5 | 4 | 2,019 |
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