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Learning Implicitly Recurrent CNNs Through Parameter Sharing | 57 | iclr | 13 | 1 | 2023-06-18 08:58:14.472000 | https://github.com/lolemacs/soft-sharing | 65 | Learning implicitly recurrent CNNs through parameter sharing | https://scholar.google.com/scholar?cluster=15123734257747528548&hl=en&as_sdt=0,33 | 4 | 2,019 |
Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs | 67 | iclr | 18 | 2 | 2023-06-18 08:58:14.673000 | https://github.com/Sachin19/seq2seq-con | 76 | Von mises-fisher loss for training sequence to sequence models with continuous outputs | https://scholar.google.com/scholar?cluster=1822338940984352644&hl=en&as_sdt=0,5 | 9 | 2,019 |
Rethinking the Value of Network Pruning | 1,281 | iclr | 307 | 23 | 2023-06-18 08:58:14.874000 | https://github.com/Eric-mingjie/rethinking-network-pruning | 1,452 | Rethinking the value of network pruning | https://scholar.google.com/scholar?cluster=3601827758437367761&hl=en&as_sdt=0,33 | 35 | 2,019 |
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network | 158 | iclr | 13 | 3 | 2023-06-18 08:58:15.075000 | https://github.com/xuanqing94/BayesianDefense | 61 | Adv-bnn: Improved adversarial defense through robust bayesian neural network | https://scholar.google.com/scholar?cluster=16111397550296660225&hl=en&as_sdt=0,10 | 5 | 2,019 |
Caveats for information bottleneck in deterministic scenarios | 62 | iclr | 1 | 0 | 2023-06-18 08:58:15.276000 | https://github.com/artemyk/ibcurve | 9 | Caveats for information bottleneck in deterministic scenarios | https://scholar.google.com/scholar?cluster=8561375002982335569&hl=en&as_sdt=0,23 | 5 | 2,019 |
Preferences Implicit in the State of the World | 54 | iclr | 7 | 0 | 2023-06-18 08:58:15.477000 | https://github.com/HumanCompatibleAI/rlsp | 40 | Preferences implicit in the state of the world | https://scholar.google.com/scholar?cluster=9659325123261489202&hl=en&as_sdt=0,10 | 8 | 2,019 |
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average | 227 | iclr | 34 | 5 | 2023-06-18 08:58:15.677000 | https://github.com/benathi/fastswa-semi-sup | 180 | There are many consistent explanations of unlabeled data: Why you should average | https://scholar.google.com/scholar?cluster=16133183473908875555&hl=en&as_sdt=0,47 | 11 | 2,019 |
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation | 12 | iclr | 22 | 1 | 2023-06-18 08:58:15.878000 | https://github.com/naver/aqm-plus | 50 | Large-scale answerer in questioner's mind for visual dialog question generation | https://scholar.google.com/scholar?cluster=7353352535802475325&hl=en&as_sdt=0,43 | 8 | 2,019 |
Delta: Deep Learning Transfer using Feature Map with Attention for Convolutional Networks | 126 | iclr | 12 | 2 | 2023-06-18 08:58:16.079000 | https://github.com/lixingjian/DELTA | 63 | Delta: Deep learning transfer using feature map with attention for convolutional networks | https://scholar.google.com/scholar?cluster=1065820725505324380&hl=en&as_sdt=0,3 | 1 | 2,019 |
Texttovec: Deep Contextualized Neural autoregressive Topic Models of Language with Distributed Compositional Prior | 10 | iclr | 5 | 3 | 2023-06-18 08:58:16.280000 | https://github.com/pgcool/textTOvec | 24 | Texttovec: Deep contextualized neural autoregressive topic models of language with distributed compositional prior | https://scholar.google.com/scholar?cluster=16604775897027080889&hl=en&as_sdt=0,14 | 3 | 2,019 |
Deep Graph Infomax | 1,328 | iclr | 128 | 10 | 2023-06-18 08:58:16.482000 | https://github.com/PetarV-/DGI | 533 | Deep graph infomax. | https://scholar.google.com/scholar?cluster=6675561854020696633&hl=en&as_sdt=0,11 | 11 | 2,019 |
Practical lossless compression with latent variables using bits back coding | 105 | iclr | 20 | 2 | 2023-06-18 08:58:16.684000 | https://github.com/bits-back/bits-back | 131 | Practical lossless compression with latent variables using bits back coding | https://scholar.google.com/scholar?cluster=1443052248345328520&hl=en&as_sdt=0,22 | 5 | 2,019 |
Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks | 214 | iclr | 50 | 10 | 2023-06-18 08:58:16.885000 | https://github.com/IC3Net/IC3Net | 185 | Learning when to communicate at scale in multiagent cooperative and competitive tasks | https://scholar.google.com/scholar?cluster=12298395236200633957&hl=en&as_sdt=0,5 | 4 | 2,019 |
GO Gradient for Expectation-Based Objectives | 22 | iclr | 0 | 0 | 2023-06-18 08:58:17.085000 | https://github.com/YulaiCong/GOgradient | 4 | GO gradient for expectation-based objectives | https://scholar.google.com/scholar?cluster=13295613950307692271&hl=en&as_sdt=0,23 | 1 | 2,019 |
h-detach: Modifying the LSTM Gradient Towards Better Optimization | 40 | iclr | 3 | 0 | 2023-06-18 08:58:17.286000 | https://github.com/bhargav104/h-detach | 11 | h-detach: Modifying the LSTM gradient towards better optimization | https://scholar.google.com/scholar?cluster=762520068872474914&hl=en&as_sdt=0,14 | 3 | 2,019 |
SOM-VAE: Interpretable Discrete Representation Learning on Time Series | 136 | iclr | 33 | 2 | 2023-06-18 08:58:17.488000 | https://github.com/ratschlab/SOM-VAE | 179 | Som-vae: Interpretable discrete representation learning on time series | https://scholar.google.com/scholar?cluster=9836294528958312436&hl=en&as_sdt=0,5 | 11 | 2,019 |
Learning Factorized Multimodal Representations | 273 | iclr | 9 | 4 | 2023-06-18 08:58:17.689000 | https://github.com/pliang279/factorized | 56 | Learning factorized multimodal representations | https://scholar.google.com/scholar?cluster=2626823666054989533&hl=en&as_sdt=0,5 | 7 | 2,019 |
Human-level Protein Localization with Convolutional Neural Networks | 21 | iclr | 2 | 1 | 2023-06-18 08:58:17.890000 | https://github.com/ml-jku/gapnet-pl | 8 | Human-level protein localization with convolutional neural networks | https://scholar.google.com/scholar?cluster=9993156504734443423&hl=en&as_sdt=0,5 | 6 | 2,019 |
Environment Probing Interaction Policies | 54 | iclr | 1 | 1 | 2023-06-18 08:58:18.091000 | https://github.com/Wenxuan-Zhou/EPI | 27 | Environment probing interaction policies | https://scholar.google.com/scholar?cluster=2903789960714905866&hl=en&as_sdt=0,47 | 2 | 2,019 |
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders | 278 | iclr | 33 | 2 | 2023-06-18 08:58:18.292000 | https://github.com/jxhe/vae-lagging-encoder | 183 | Lagging inference networks and posterior collapse in variational autoencoders | https://scholar.google.com/scholar?cluster=5286759698670808442&hl=en&as_sdt=0,5 | 4 | 2,019 |
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks | 235 | iclr | 26 | 0 | 2023-06-18 08:58:18.492000 | https://github.com/reinhardh/supplement_deep_decoder | 81 | Deep decoder: Concise image representations from untrained non-convolutional networks | https://scholar.google.com/scholar?cluster=5031846359818705791&hl=en&as_sdt=0,5 | 6 | 2,019 |
SNAS: stochastic neural architecture search | 877 | iclr | 24 | 3 | 2023-06-18 08:58:18.693000 | https://github.com/SNAS-Series/SNAS-Series | 133 | SNAS: stochastic neural architecture search | https://scholar.google.com/scholar?cluster=13328811299154907405&hl=en&as_sdt=0,33 | 5 | 2,019 |
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue | 151 | iclr | 25 | 1 | 2023-06-18 08:58:18.894000 | https://github.com/jasonwu0731/GLMP | 159 | Global-to-local memory pointer networks for task-oriented dialogue | https://scholar.google.com/scholar?cluster=8042905846859720405&hl=en&as_sdt=0,5 | 14 | 2,019 |
InstaGAN: Instance-aware Image-to-Image Translation | 172 | iclr | 161 | 11 | 2023-06-18 08:58:19.096000 | https://github.com/sangwoomo/instagan | 836 | Instagan: Instance-aware image-to-image translation | https://scholar.google.com/scholar?cluster=14041898124180765737&hl=en&as_sdt=0,5 | 34 | 2,019 |
Learning Multi-Level Hierarchies with Hindsight | 199 | iclr | 59 | 0 | 2023-06-18 08:58:19.297000 | https://github.com/andrew-j-levy/Hierarchical-Actor-Critc-HAC- | 239 | Learning multi-level hierarchies with hindsight | https://scholar.google.com/scholar?cluster=11558193958091287134&hl=en&as_sdt=0,33 | 12 | 2,019 |
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation | 143 | iclr | 19 | 4 | 2023-06-18 09:09:50.832000 | https://github.com/hugochan/RL-based-Graph2Seq-for-NQG | 114 | Reinforcement learning based graph-to-sequence model for natural question generation | https://scholar.google.com/scholar?cluster=5519507630710292821&hl=en&as_sdt=0,5 | 7 | 2,020 |
Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification | 475 | iclr | 76 | 4 | 2023-06-18 09:09:51.035000 | https://github.com/yxgeee/MMT | 450 | Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification | https://scholar.google.com/scholar?cluster=5921437976740591026&hl=en&as_sdt=0,47 | 10 | 2,020 |
Automatically Discovering and Learning New Visual Categories with Ranking Statistics | 112 | iclr | 20 | 4 | 2023-06-18 09:09:51.251000 | https://github.com/k-han/AutoNovel | 211 | Automatically discovering and learning new visual categories with ranking statistics | https://scholar.google.com/scholar?cluster=6046841849136229502&hl=en&as_sdt=0,5 | 7 | 2,020 |
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning | 104 | iclr | 11 | 1 | 2023-06-18 09:09:51.455000 | https://github.com/qlan3/Explorer | 72 | Maxmin q-learning: Controlling the estimation bias of q-learning | https://scholar.google.com/scholar?cluster=7792637153572320374&hl=en&as_sdt=0,5 | 4 | 2,020 |
DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures | 89 | iclr | 3 | 2 | 2023-06-18 09:09:51.657000 | https://github.com/yanghr/DeepHoyer | 29 | Deephoyer: Learning sparser neural network with differentiable scale-invariant sparsity measures | https://scholar.google.com/scholar?cluster=9357831330077087953&hl=en&as_sdt=0,33 | 3 | 2,020 |
Evaluating The Search Phase of Neural Architecture Search | 226 | iclr | 8 | 2 | 2023-06-18 09:09:51.860000 | https://github.com/kcyu2014/eval-nas | 49 | Evaluating the search phase of neural architecture search | https://scholar.google.com/scholar?cluster=14035367419965317698&hl=en&as_sdt=0,10 | 4 | 2,020 |
LAMOL: LAnguage MOdeling for Lifelong Language Learning | 122 | iclr | 10 | 1 | 2023-06-18 09:09:52.074000 | https://github.com/jojotenya/LAMOL | 82 | Lamol: Language modeling for lifelong language learning | https://scholar.google.com/scholar?cluster=16454938344621096337&hl=en&as_sdt=0,5 | 7 | 2,020 |
Automated Relational Meta-learning | 83 | iclr | 5 | 4 | 2023-06-18 09:09:52.280000 | https://github.com/huaxiuyao/ARML | 41 | Automated relational meta-learning | https://scholar.google.com/scholar?cluster=12701522525812856519&hl=en&as_sdt=0,33 | 5 | 2,020 |
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition | 98 | iclr | 2 | 0 | 2023-06-18 09:09:52.483000 | https://github.com/iclr2020-apd/anonymous_iclr2020_apd_code | 7 | Scalable and order-robust continual learning with additive parameter decomposition | https://scholar.google.com/scholar?cluster=1824460160917131841&hl=en&as_sdt=0,34 | 1 | 2,020 |
A Learning-based Iterative Method for Solving Vehicle Routing Problems | 169 | iclr | 23 | 5 | 2023-06-18 09:09:52.685000 | https://github.com/rlopt/l2i | 84 | A learning-based iterative method for solving vehicle routing problems | https://scholar.google.com/scholar?cluster=17783279286650305146&hl=en&as_sdt=0,47 | 11 | 2,020 |
Ranking Policy Gradient | 12 | iclr | 1 | 0 | 2023-06-18 09:09:52.888000 | https://github.com/illidanlab/rpg | 22 | Ranking policy gradient | https://scholar.google.com/scholar?cluster=15054324663691805917&hl=en&as_sdt=0,36 | 3 | 2,020 |
On Mutual Information Maximization for Representation Learning | 390 | iclr | 7,332 | 1,026 | 2023-06-18 09:09:53.091000 | https://github.com/google-research/google-research | 29,803 | On mutual information maximization for representation learning | https://scholar.google.com/scholar?cluster=13497843317340085742&hl=en&as_sdt=0,24 | 728 | 2,020 |
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks | 198 | iclr | 48 | 17 | 2023-06-18 09:09:53.295000 | https://github.com/yhhhli/APoT_Quantization | 217 | Additive powers-of-two quantization: An efficient non-uniform discretization for neural networks | https://scholar.google.com/scholar?cluster=15761551970233038392&hl=en&as_sdt=0,5 | 5 | 2,020 |
TabFact: A Large-scale Dataset for Table-based Fact Verification | 228 | iclr | 51 | 0 | 2023-06-18 09:09:53.497000 | https://github.com/wenhuchen/Table-Fact-Checking | 324 | Tabfact: A large-scale dataset for table-based fact verification | https://scholar.google.com/scholar?cluster=17043210713635846770&hl=en&as_sdt=0,5 | 10 | 2,020 |
Neural Stored-program Memory | 29 | iclr | 3 | 23 | 2023-06-18 09:09:53.700000 | https://github.com/thaihungle/NSM | 14 | Neural stored-program memory | https://scholar.google.com/scholar?cluster=15969516798219653164&hl=en&as_sdt=0,11 | 3 | 2,020 |
Multi-agent Reinforcement Learning for Networked System Control | 71 | iclr | 82 | 3 | 2023-06-18 09:09:53.902000 | https://github.com/cts198859/deeprl_network | 309 | Multi-agent reinforcement learning for networked system control | https://scholar.google.com/scholar?cluster=8406297615890251928&hl=en&as_sdt=0,33 | 9 | 2,020 |
FSPool: Learning Set Representations with Featurewise Sort Pooling | 66 | iclr | 8 | 1 | 2023-06-18 09:09:54.105000 | https://github.com/Cyanogenoid/fspool | 42 | Fspool: Learning set representations with featurewise sort pooling | https://scholar.google.com/scholar?cluster=3929630154366081815&hl=en&as_sdt=0,5 | 4 | 2,020 |
Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction | 77 | iclr | 5 | 3 | 2023-06-18 09:09:54.308000 | https://github.com/galsang/trees_from_transformers | 28 | Are pre-trained language models aware of phrases? simple but strong baselines for grammar induction | https://scholar.google.com/scholar?cluster=12987326770571285349&hl=en&as_sdt=0,6 | 3 | 2,020 |
Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning | 54 | iclr | 3 | 1 | 2023-06-18 09:09:54.512000 | https://github.com/netpaladinx/DPMPN | 20 | Dynamically pruned message passing networks for large-scale knowledge graph reasoning | https://scholar.google.com/scholar?cluster=6314488797301074088&hl=en&as_sdt=0,5 | 3 | 2,020 |
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks | 101 | iclr | 10 | 1 | 2023-06-18 09:09:54.714000 | https://github.com/P2333/Mixup-Inference | 58 | Mixup inference: Better exploiting mixup to defend adversarial attacks | https://scholar.google.com/scholar?cluster=17489632663330060721&hl=en&as_sdt=0,34 | 3 | 2,020 |
Theory and Evaluation Metrics for Learning Disentangled Representations | 66 | iclr | 2 | 0 | 2023-06-18 09:09:54.917000 | https://github.com/clarken92/DisentanglementMetrics | 15 | Theory and evaluation metrics for learning disentangled representations | https://scholar.google.com/scholar?cluster=7456690520633127745&hl=en&as_sdt=0,41 | 2 | 2,020 |
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness | 133 | iclr | 19 | 1 | 2023-06-18 09:09:55.119000 | https://github.com/P2333/Max-Mahalanobis-Training | 87 | Rethinking softmax cross-entropy loss for adversarial robustness | https://scholar.google.com/scholar?cluster=12978417581755318851&hl=en&as_sdt=0,5 | 4 | 2,020 |
The Implicit Bias of Depth: How Incremental Learning Drives Generalization | 38 | iclr | 0 | 0 | 2023-06-18 09:09:55.322000 | https://github.com/dsgissin/Incremental-Learning | 7 | The implicit bias of depth: How incremental learning drives generalization | https://scholar.google.com/scholar?cluster=13677656727804857978&hl=en&as_sdt=0,5 | 3 | 2,020 |
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget | 20 | iclr | 530 | 16 | 2023-06-18 09:09:55.524000 | https://github.com/maximecb/gym-minigrid | 1,810 | The variational bandwidth bottleneck: Stochastic evaluation on an information budget | https://scholar.google.com/scholar?cluster=5182568436909686711&hl=en&as_sdt=0,8 | 39 | 2,020 |
Robust Local Features for Improving the Generalization of Adversarial Training | 66 | iclr | 3 | 1 | 2023-06-18 09:09:55.727000 | https://github.com/JHL-HUST/RLFAT | 13 | Robust local features for improving the generalization of adversarial training | https://scholar.google.com/scholar?cluster=11695646506050122270&hl=en&as_sdt=0,5 | 3 | 2,020 |
Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification | 5 | iclr | 2 | 0 | 2023-06-18 09:09:55.930000 | https://github.com/Benji4/zebrafish-learning | 7 | Analysis of video feature learning in two-stream CNNs on the example of zebrafish swim bout classification | https://scholar.google.com/scholar?cluster=7291111967926344032&hl=en&as_sdt=0,14 | 1 | 2,020 |
Logic and the 2-Simplicial Transformer | 2 | iclr | 4 | 0 | 2023-06-18 09:09:56.134000 | https://github.com/dmurfet/2simplicialtransformer | 15 | Logic and the -Simplicial Transformer | https://scholar.google.com/scholar?cluster=3081517893804157897&hl=en&as_sdt=0,33 | 2 | 2,020 |
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking | 59 | iclr | 9 | 2 | 2023-06-18 09:09:56.337000 | https://github.com/anonymousjack/hijacking | 42 | Fooling detection alone is not enough: Adversarial attack against multiple object tracking | https://scholar.google.com/scholar?cluster=15515055522275518315&hl=en&as_sdt=0,47 | 2 | 2,020 |
DivideMix: Learning with Noisy Labels as Semi-supervised Learning | 599 | iclr | 76 | 8 | 2023-06-18 09:09:56.540000 | https://github.com/LiJunnan1992/DivideMix | 456 | Dividemix: Learning with noisy labels as semi-supervised learning | https://scholar.google.com/scholar?cluster=11967955085227307195&hl=en&as_sdt=0,33 | 10 | 2,020 |
Accelerating SGD with momentum for over-parameterized learning | 62 | iclr | 1 | 0 | 2023-06-18 09:09:56.744000 | https://github.com/ts66395/MaSS | 5 | Accelerating sgd with momentum for over-parameterized learning | https://scholar.google.com/scholar?cluster=15634725943352892277&hl=en&as_sdt=0,36 | 1 | 2,020 |
A critical analysis of self-supervision, or what we can learn from a single image | 129 | iclr | 8 | 3 | 2023-06-18 09:09:56.947000 | https://github.com/yukimasano/linear-probes | 36 | A critical analysis of self-supervision, or what we can learn from a single image | https://scholar.google.com/scholar?cluster=1196793253523325509&hl=en&as_sdt=0,31 | 2 | 2,020 |
Progressive Memory Banks for Incremental Domain Adaptation | 23 | iclr | 2 | 0 | 2023-06-18 09:09:57.151000 | https://github.com/nabihach/IDA | 13 | Progressive memory banks for incremental domain adaptation | https://scholar.google.com/scholar?cluster=16171132848868692146&hl=en&as_sdt=0,33 | 2 | 2,020 |
Exploring Model-based Planning with Policy Networks | 124 | iclr | 12 | 4 | 2023-06-18 09:09:57.356000 | https://github.com/WilsonWangTHU/POPLIN | 93 | Exploring model-based planning with policy networks | https://scholar.google.com/scholar?cluster=5788425518026701179&hl=en&as_sdt=0,34 | 4 | 2,020 |
Few-shot Text Classification with Distributional Signatures | 141 | iclr | 57 | 0 | 2023-06-18 09:09:57.559000 | https://github.com/YujiaBao/Distributional-Signatures | 248 | Few-shot text classification with distributional signatures | https://scholar.google.com/scholar?cluster=4872590605106254296&hl=en&as_sdt=0,14 | 6 | 2,020 |
Adversarial Policies: Attacking Deep Reinforcement Learning | 299 | iclr | 41 | 7 | 2023-06-18 09:09:57.762000 | https://github.com/HumanCompatibleAI/adversarial-policies | 241 | Adversarial policies: Attacking deep reinforcement learning | https://scholar.google.com/scholar?cluster=1203868559900085227&hl=en&as_sdt=0,11 | 15 | 2,020 |
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation | 81 | iclr | 3,290 | 589 | 2023-06-18 09:09:57.965000 | https://github.com/tensorflow/tensor2tensor | 13,768 | Videoflow: A conditional flow-based model for stochastic video generation | https://scholar.google.com/scholar?cluster=13005087974871140727&hl=en&as_sdt=0,14 | 461 | 2,020 |
GLAD: Learning Sparse Graph Recovery | 23 | iclr | 5 | 0 | 2023-06-18 09:09:58.168000 | https://github.com/Harshs27/GLAD | 13 | GLAD: Learning sparse graph recovery | https://scholar.google.com/scholar?cluster=17323993038772593390&hl=en&as_sdt=0,47 | 2 | 2,020 |
FasterSeg: Searching for Faster Real-time Semantic Segmentation | 150 | iclr | 110 | 11 | 2023-06-18 09:09:58.370000 | https://github.com/TAMU-VITA/FasterSeg | 515 | Fasterseg: Searching for faster real-time semantic segmentation | https://scholar.google.com/scholar?cluster=11587095836376020772&hl=en&as_sdt=0,5 | 27 | 2,020 |
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth | 161 | iclr | 242 | 79 | 2023-06-18 09:09:58.573000 | https://github.com/TRI-ML/packnet-sfm | 1,140 | Semantically-guided representation learning for self-supervised monocular depth | https://scholar.google.com/scholar?cluster=17082069917027724929&hl=en&as_sdt=0,5 | 56 | 2,020 |
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius | 123 | iclr | 7 | 3 | 2023-06-18 09:09:58.776000 | https://github.com/RuntianZ/macer | 27 | Macer: Attack-free and scalable robust training via maximizing certified radius | https://scholar.google.com/scholar?cluster=17692253363082747545&hl=en&as_sdt=0,5 | 4 | 2,020 |
GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification | 40 | iclr | 0 | 0 | 2023-06-18 09:09:58.979000 | https://github.com/xuwangyin/GAT-Generative-Adversarial-Training | 3 | Gat: Generative adversarial training for adversarial example detection and robust classification | https://scholar.google.com/scholar?cluster=11402188250493503654&hl=en&as_sdt=0,5 | 2 | 2,020 |
Variational Recurrent Models for Solving Partially Observable Control Tasks | 45 | iclr | 13 | 0 | 2023-06-18 09:09:59.181000 | https://github.com/oist-cnru/Variational-Recurrent-Models | 41 | Variational recurrent models for solving partially observable control tasks | https://scholar.google.com/scholar?cluster=10619641407453895242&hl=en&as_sdt=0,5 | 6 | 2,020 |
Population-Guided Parallel Policy Search for Reinforcement Learning | 36 | iclr | 6 | 1 | 2023-06-18 09:09:59.384000 | https://github.com/wyjung0625/p3s | 19 | Population-guided parallel policy search for reinforcement learning | https://scholar.google.com/scholar?cluster=13101828686651859537&hl=en&as_sdt=0,44 | 1 | 2,020 |
Compositional languages emerge in a neural iterated learning model | 55 | iclr | 2 | 4 | 2023-06-18 09:09:59.586000 | https://github.com/Joshua-Ren/Neural_Iterated_Learning | 11 | Compositional languages emerge in a neural iterated learning model | https://scholar.google.com/scholar?cluster=12260597755376568294&hl=en&as_sdt=0,5 | 3 | 2,020 |
Black-Box Adversarial Attack with Transferable Model-based Embedding | 85 | iclr | 16 | 2 | 2023-06-18 09:09:59.788000 | https://github.com/TransEmbedBA/TREMBA | 52 | Black-box adversarial attack with transferable model-based embedding | https://scholar.google.com/scholar?cluster=2817331092772484407&hl=en&as_sdt=0,5 | 4 | 2,020 |
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively | 18 | iclr | 3 | 0 | 2023-06-18 09:09:59.991000 | https://github.com/TAMU-VITA/MAD | 19 | I am going MAD: Maximum discrepancy competition for comparing classifiers adaptively | https://scholar.google.com/scholar?cluster=7857243656260190793&hl=en&as_sdt=0,5 | 12 | 2,020 |
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models | 122 | iclr | 6 | 0 | 2023-06-18 09:10:00.194000 | https://github.com/bloodwass/mixout | 70 | Mixout: Effective regularization to finetune large-scale pretrained language models | https://scholar.google.com/scholar?cluster=476449558403052711&hl=en&as_sdt=0,33 | 3 | 2,020 |
Deep Network Classification by Scattering and Homotopy Dictionary Learning | 37 | iclr | 7 | 1 | 2023-06-18 09:10:00.397000 | https://github.com/j-zarka/SparseScatNet | 22 | Deep network classification by scattering and homotopy dictionary learning | https://scholar.google.com/scholar?cluster=8953532076769179699&hl=en&as_sdt=0,5 | 2 | 2,020 |
Action Semantics Network: Considering the Effects of Actions in Multiagent Systems | 27 | iclr | 7 | 8 | 2023-06-18 09:10:00.599000 | https://github.com/MAS-anony/ASN | 26 | Action semantics network: Considering the effects of actions in multiagent systems | https://scholar.google.com/scholar?cluster=12922359203743384074&hl=en&as_sdt=0,47 | 2 | 2,020 |
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing | 66 | iclr | 2 | 0 | 2023-06-18 09:10:00.802000 | https://github.com/jjy1994/Certify_Topk | 9 | Certified robustness for top-k predictions against adversarial perturbations via randomized smoothing | https://scholar.google.com/scholar?cluster=12562520033309681005&hl=en&as_sdt=0,1 | 2 | 2,020 |
Optimistic Exploration even with a Pessimistic Initialisation | 39 | iclr | 2 | 0 | 2023-06-18 09:10:01.008000 | https://github.com/oxwhirl/opiq | 13 | Optimistic exploration even with a pessimistic initialisation | https://scholar.google.com/scholar?cluster=3638632110441158229&hl=en&as_sdt=0,10 | 4 | 2,020 |
VL-BERT: Pre-training of Generic Visual-Linguistic Representations | 1,249 | iclr | 108 | 20 | 2023-06-18 09:10:01.210000 | https://github.com/jackroos/VL-BERT | 715 | Vl-bert: Pre-training of generic visual-linguistic representations | https://scholar.google.com/scholar?cluster=7768062511032572067&hl=en&as_sdt=0,43 | 14 | 2,020 |
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality | 16 | iclr | 3 | 2 | 2023-06-18 09:10:01.413000 | https://github.com/spitis/deepnorms | 10 | An inductive bias for distances: Neural nets that respect the triangle inequality | https://scholar.google.com/scholar?cluster=17260770554780214553&hl=en&as_sdt=0,23 | 5 | 2,020 |
NAS evaluation is frustratingly hard | 164 | iclr | 23 | 1 | 2023-06-18 09:10:01.617000 | https://github.com/antoyang/NAS-Benchmark | 146 | NAS evaluation is frustratingly hard | https://scholar.google.com/scholar?cluster=12471694483970544806&hl=en&as_sdt=0,5 | 4 | 2,020 |
Order Learning and Its Application to Age Estimation | 19 | iclr | 6 | 9 | 2023-06-18 09:10:01.820000 | https://github.com/changsukim-ku/order-learning | 18 | Order learning and its application to age estimation | https://scholar.google.com/scholar?cluster=11437601791215885877&hl=en&as_sdt=0,5 | 2 | 2,020 |
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning | 102 | iclr | 12 | 1 | 2023-06-18 09:10:02.022000 | https://github.com/yuweihao/reclor | 70 | Reclor: A reading comprehension dataset requiring logical reasoning | https://scholar.google.com/scholar?cluster=4598160843843301931&hl=en&as_sdt=0,22 | 2 | 2,020 |
From Variational to Deterministic Autoencoders | 238 | iclr | 14 | 13 | 2023-06-18 09:10:02.225000 | https://github.com/ParthaEth/Regularized_autoencoders-RAE- | 112 | From variational to deterministic autoencoders | https://scholar.google.com/scholar?cluster=10583740506297544895&hl=en&as_sdt=0,33 | 4 | 2,020 |
A Fair Comparison of Graph Neural Networks for Graph Classification | 336 | iclr | 48 | 6 | 2023-06-18 09:10:02.428000 | https://github.com/diningphil/gnn-comparison | 323 | A fair comparison of graph neural networks for graph classification | https://scholar.google.com/scholar?cluster=3840429300245249800&hl=en&as_sdt=0,46 | 9 | 2,020 |
SAdam: A Variant of Adam for Strongly Convex Functions | 37 | iclr | 1 | 0 | 2023-06-18 09:10:02.632000 | https://github.com/SAdam-ICLR2020/codes | 1 | Sadam: A variant of adam for strongly convex functions | https://scholar.google.com/scholar?cluster=4099818587284366739&hl=en&as_sdt=0,47 | 1 | 2,020 |
Few-Shot Learning on graphs via super-Classes based on Graph spectral Measures | 51 | iclr | 6 | 5 | 2023-06-18 09:10:02.835000 | https://github.com/chauhanjatin10/GraphsFewShot | 27 | Few-shot learning on graphs via super-classes based on graph spectral measures | https://scholar.google.com/scholar?cluster=14327533105664935166&hl=en&as_sdt=0,38 | 2 | 2,020 |
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning | 43 | iclr | 0 | 1 | 2023-06-18 09:10:03.038000 | https://github.com/shrezaei/Target-Agnostic-Attack | 8 | A target-agnostic attack on deep models: Exploiting security vulnerabilities of transfer learning | https://scholar.google.com/scholar?cluster=15887045580387501339&hl=en&as_sdt=0,5 | 1 | 2,020 |
Option Discovery using Deep Skill Chaining | 71 | iclr | 9 | 2 | 2023-06-18 09:10:03.240000 | https://github.com/deep-skill-chaining/deep-skill-chaining | 26 | Option discovery using deep skill chaining | https://scholar.google.com/scholar?cluster=3599079453056617566&hl=en&as_sdt=0,5 | 2 | 2,020 |
Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations | 1 | iclr | 30 | 4 | 2023-06-18 09:10:03.443000 | https://github.com/pkorus/neural-imaging | 139 | Quantifying the cost of reliable photo authentication via high-performance learned lossy representations | https://scholar.google.com/scholar?cluster=1043795359610865764&hl=en&as_sdt=0,5 | 11 | 2,020 |
On the Variance of the Adaptive Learning Rate and Beyond | 1,557 | iclr | 340 | 12 | 2023-06-18 09:10:03.646000 | https://github.com/LiyuanLucasLiu/RAdam | 2,494 | On the variance of the adaptive learning rate and beyond | https://scholar.google.com/scholar?cluster=2176563085556003509&hl=en&as_sdt=0,14 | 58 | 2,020 |
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection | 48 | iclr | 11 | 7 | 2023-06-18 09:10:03.849000 | https://github.com/mtsang/interaction_interpretability | 34 | Feature interaction interpretability: A case for explaining ad-recommendation systems via neural interaction detection | https://scholar.google.com/scholar?cluster=3857662297580644261&hl=en&as_sdt=0,48 | 5 | 2,020 |
Understanding the Limitations of Variational Mutual Information Estimators | 138 | iclr | 7 | 0 | 2023-06-18 09:10:04.054000 | https://github.com/ermongroup/smile-mi-estimator | 58 | Understanding the limitations of variational mutual information estimators | https://scholar.google.com/scholar?cluster=4523141934967854838&hl=en&as_sdt=0,25 | 5 | 2,020 |
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations | 210 | iclr | 18 | 2 | 2023-06-18 09:10:04.273000 | https://github.com/applied-ai-lab/genesis | 87 | Genesis: Generative scene inference and sampling with object-centric latent representations | https://scholar.google.com/scholar?cluster=12595023313997791876&hl=en&as_sdt=0,5 | 4 | 2,020 |
Language GANs Falling Short | 185 | iclr | 11 | 0 | 2023-06-18 09:10:04.475000 | https://github.com/pclucas14/GansFallingShort | 57 | Language gans falling short | https://scholar.google.com/scholar?cluster=5625942263097164405&hl=en&as_sdt=0,5 | 6 | 2,020 |
Reinforced active learning for image segmentation | 72 | iclr | 19 | 1 | 2023-06-18 09:10:04.678000 | https://github.com/ArantxaCasanova/ralis | 83 | Reinforced active learning for image segmentation | https://scholar.google.com/scholar?cluster=1054013285080220526&hl=en&as_sdt=0,5 | 4 | 2,020 |
Sign Bits Are All You Need for Black-Box Attacks | 42 | iclr | 4 | 0 | 2023-06-18 09:10:04.882000 | https://github.com/ash-aldujaili/blackbox-adv-examples-signhunter | 19 | Sign bits are all you need for black-box attacks | https://scholar.google.com/scholar?cluster=7597354738321523797&hl=en&as_sdt=0,5 | 4 | 2,020 |
Deep Semi-Supervised Anomaly Detection | 407 | iclr | 88 | 17 | 2023-06-18 09:10:05.084000 | https://github.com/lukasruff/Deep-SAD-PyTorch | 303 | Deep semi-supervised anomaly detection | https://scholar.google.com/scholar?cluster=5100822312770479848&hl=en&as_sdt=0,5 | 9 | 2,020 |
Minimizing FLOPs to Learn Efficient Sparse Representations | 30 | iclr | 2 | 0 | 2023-06-18 09:10:05.288000 | https://github.com/biswajitsc/sparse-embed | 19 | Minimizing flops to learn efficient sparse representations | https://scholar.google.com/scholar?cluster=16391107852895136725&hl=en&as_sdt=0,31 | 4 | 2,020 |
Imitation Learning via Off-Policy Distribution Matching | 111 | iclr | 7,332 | 1,026 | 2023-06-18 09:10:05.491000 | https://github.com/google-research/google-research | 29,803 | Imitation learning via off-policy distribution matching | https://scholar.google.com/scholar?cluster=17232131883135762020&hl=en&as_sdt=0,32 | 728 | 2,020 |
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space | 104 | iclr | 33 | 0 | 2023-06-18 09:10:05.693000 | https://github.com/aspuru-guzik-group/GA | 80 | Augmenting genetic algorithms with deep neural networks for exploring the chemical space | https://scholar.google.com/scholar?cluster=4690781735136459726&hl=en&as_sdt=0,5 | 6 | 2,020 |
Neural Text Generation With Unlikelihood Training | 334 | iclr | 43 | 7 | 2023-06-18 09:10:05.897000 | https://github.com/facebookresearch/unlikelihood_training | 293 | Neural text generation with unlikelihood training | https://scholar.google.com/scholar?cluster=16638535268657480159&hl=en&as_sdt=0,5 | 16 | 2,020 |
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