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Chunked Autoregressive GAN for Conditional Waveform Synthesis | 33 | iclr | 30 | 4 | 2023-06-18 09:43:37.678000 | https://github.com/descriptinc/cargan | 161 | Chunked autoregressive gan for conditional waveform synthesis | https://scholar.google.com/scholar?cluster=12411331012561904832&hl=en&as_sdt=0,31 | 23 | 2,022 |
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks | 7 | iclr | 1 | 0 | 2023-06-18 09:43:37.888000 | https://github.com/ai-secure/copa | 7 | COPA: Certifying robust policies for offline reinforcement learning against poisoning attacks | https://scholar.google.com/scholar?cluster=11901953356085311316&hl=en&as_sdt=0,5 | 2 | 2,022 |
Multi-Agent MDP Homomorphic Networks | 11 | iclr | 0 | 0 | 2023-06-18 09:43:38.091000 | https://github.com/elisevanderpol/marl_homomorphic_networks | 4 | Multi-agent MDP homomorphic networks | https://scholar.google.com/scholar?cluster=7742088366120766374&hl=en&as_sdt=0,20 | 2 | 2,022 |
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields | 21 | iclr | 6 | 6 | 2023-06-18 09:43:38.294000 | https://github.com/yifita/idf | 108 | Geometry-consistent neural shape representation with implicit displacement fields | https://scholar.google.com/scholar?cluster=1893838131986981154&hl=en&as_sdt=0,6 | 5 | 2,022 |
Modeling Label Space Interactions in Multi-label Classification using Box Embeddings | 17 | iclr | 0 | 0 | 2023-06-18 09:43:38.498000 | https://github.com/iesl/box-mlc-iclr-2022 | 9 | Modeling label space interactions in multi-label classification using box embeddings | https://scholar.google.com/scholar?cluster=10529771024100862700&hl=en&as_sdt=0,33 | 17 | 2,022 |
It Takes Two to Tango: Mixup for Deep Metric Learning | 13 | iclr | 4 | 1 | 2023-06-18 09:43:38.701000 | https://github.com/billpsomas/Metrix_ICLR22 | 25 | It takes two to tango: Mixup for deep metric learning | https://scholar.google.com/scholar?cluster=11528364689956817661&hl=en&as_sdt=0,11 | 6 | 2,022 |
Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation | 18 | iclr | 3 | 1 | 2023-06-18 09:43:38.905000 | https://github.com/facebookresearch/otter | 51 | Data efficient language-supervised zero-shot recognition with optimal transport distillation | https://scholar.google.com/scholar?cluster=16240113248211357205&hl=en&as_sdt=0,5 | 4 | 2,022 |
Learning State Representations via Retracing in Reinforcement Learning | 5 | iclr | 1 | 0 | 2023-06-18 09:43:39.108000 | https://github.com/changmin-yu/ccwm_code | 4 | Learning state representations via retracing in reinforcement learning | https://scholar.google.com/scholar?cluster=5497480692580123615&hl=en&as_sdt=0,5 | 1 | 2,022 |
Open-World Semi-Supervised Learning | 58 | iclr | 9 | 6 | 2023-06-18 09:43:39.311000 | https://github.com/snap-stanford/orca | 62 | Open-world semi-supervised learning | https://scholar.google.com/scholar?cluster=13685131570461746231&hl=en&as_sdt=0,22 | 4 | 2,022 |
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent | 23 | iclr | 3 | 3 | 2023-06-18 09:43:39.513000 | https://github.com/v-wangg/OrthogonalPGD | 17 | Evading adversarial example detection defenses with orthogonal projected gradient descent | https://scholar.google.com/scholar?cluster=6627043113889326245&hl=en&as_sdt=0,26 | 4 | 2,022 |
Fast AdvProp | 6 | iclr | 0 | 1 | 2023-06-18 09:43:39.716000 | https://github.com/meijieru/fast_advprop | 33 | Fast advprop | https://scholar.google.com/scholar?cluster=17518006235660748268&hl=en&as_sdt=0,10 | 5 | 2,022 |
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs | 29 | iclr | 18 | 0 | 2023-06-18 09:43:39.919000 | https://github.com/migalkin/NodePiece | 124 | Nodepiece: Compositional and parameter-efficient representations of large knowledge graphs | https://scholar.google.com/scholar?cluster=4956010200873018529&hl=en&as_sdt=0,5 | 7 | 2,022 |
Pix2seq: A Language Modeling Framework for Object Detection | 120 | iclr | 55 | 21 | 2023-06-18 09:43:40.122000 | https://github.com/google-research/pix2seq | 652 | Pix2seq: A language modeling framework for object detection | https://scholar.google.com/scholar?cluster=17102558257176551695&hl=en&as_sdt=0,5 | 17 | 2,022 |
Learning Curves for SGD on Structured Features | 8 | iclr | 0 | 0 | 2023-06-18 09:43:40.326000 | https://github.com/Pehlevan-Group/sgd_structured_features | 0 | Learning curves for sgd on structured features | https://scholar.google.com/scholar?cluster=16931573474353829992&hl=en&as_sdt=0,33 | 2 | 2,022 |
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training | 17 | iclr | 4 | 3 | 2023-06-18 09:43:40.546000 | https://github.com/facebookresearch/NASViT | 57 | Nasvit: Neural architecture search for efficient vision transformers with gradient conflict aware supernet training | https://scholar.google.com/scholar?cluster=12012622546749628874&hl=en&as_sdt=0,41 | 5 | 2,022 |
Graphon based Clustering and Testing of Networks: Algorithms and Theory | 3 | iclr | 2 | 0 | 2023-06-18 09:43:40.750000 | https://github.com/maha-93/Clustering-Testing-Networks | 3 | Graphon based Clustering and Testing of Networks: Algorithms and Theory | https://scholar.google.com/scholar?cluster=11291859558104381886&hl=en&as_sdt=0,33 | 1 | 2,022 |
Augmented Sliced Wasserstein Distances | 13 | iclr | 4 | 1 | 2023-06-18 09:43:40.953000 | https://github.com/xiongjiechen/ASWD | 8 | Augmented sliced Wasserstein distances | https://scholar.google.com/scholar?cluster=955715037092022915&hl=en&as_sdt=0,33 | 2 | 2,022 |
Joint Shapley values: a measure of joint feature importance | 7 | iclr | 0 | 0 | 2023-06-18 09:43:41.155000 | https://github.com/harris-chris/joint-shapley-values | 13 | Joint Shapley values: a measure of joint feature importance | https://scholar.google.com/scholar?cluster=4894614344420722159&hl=en&as_sdt=0,33 | 1 | 2,022 |
Efficient Self-supervised Vision Transformers for Representation Learning | 129 | iclr | 42 | 14 | 2023-06-18 09:43:41.358000 | https://github.com/microsoft/esvit | 378 | Efficient self-supervised vision transformers for representation learning | https://scholar.google.com/scholar?cluster=15469437604545198809&hl=en&as_sdt=0,39 | 12 | 2,022 |
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain | 29 | iclr | 6 | 0 | 2023-06-18 09:43:41.561000 | https://github.com/bethgelab/InDomainGeneralizationBenchmark | 32 | Visual representation learning does not generalize strongly within the same domain | https://scholar.google.com/scholar?cluster=827943787586075996&hl=en&as_sdt=0,33 | 2 | 2,022 |
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions | 12 | iclr | 1 | 0 | 2023-06-18 09:43:41.764000 | https://github.com/ardasahiner/ProCoGAN | 5 | Hidden convexity of wasserstein gans: Interpretable generative models with closed-form solutions | https://scholar.google.com/scholar?cluster=9526825653845388729&hl=en&as_sdt=0,29 | 1 | 2,022 |
Memory Augmented Optimizers for Deep Learning | 1 | iclr | 1 | 0 | 2023-06-18 09:43:41.967000 | https://github.com/chandar-lab/CGOptimizer | 6 | Memory Augmented Optimizers for Deep Learning | https://scholar.google.com/scholar?cluster=11073351928197752868&hl=en&as_sdt=0,5 | 5 | 2,022 |
Orchestrated Value Mapping for Reinforcement Learning | 3 | iclr | 4 | 0 | 2023-06-18 09:43:42.170000 | https://github.com/microsoft/orchestrated-value-mapping | 3 | Orchestrated value mapping for reinforcement learning | https://scholar.google.com/scholar?cluster=11063352245318082342&hl=en&as_sdt=0,5 | 4 | 2,022 |
Learning to Generalize across Domains on Single Test Samples | 15 | iclr | 1 | 1 | 2023-06-18 09:43:42.373000 | https://github.com/zzzx1224/singlesamplegeneralization-iclr2022 | 22 | Learning to generalize across domains on single test samples | https://scholar.google.com/scholar?cluster=10799367073706985191&hl=en&as_sdt=0,47 | 4 | 2,022 |
How Attentive are Graph Attention Networks? | 334 | iclr | 29 | 2 | 2023-06-18 09:43:42.577000 | https://github.com/tech-srl/how_attentive_are_gats | 223 | How attentive are graph attention networks? | https://scholar.google.com/scholar?cluster=5656297883023258429&hl=en&as_sdt=0,36 | 11 | 2,022 |
Learning Transferable Reward for Query Object Localization with Policy Adaptation | 0 | iclr | 0 | 0 | 2023-06-18 09:43:42.780000 | https://github.com/litingfeng/localization-by-ordembed | 1 | Learning Transferable Reward for Query Object Localization with Policy Adaptation | https://scholar.google.com/scholar?cluster=6915912044091990536&hl=en&as_sdt=0,31 | 3 | 2,022 |
CKConv: Continuous Kernel Convolution For Sequential Data | 54 | iclr | 12 | 2 | 2023-06-18 09:43:42.982000 | https://github.com/dwromero/ckconv | 100 | Ckconv: Continuous kernel convolution for sequential data | https://scholar.google.com/scholar?cluster=13572212513025696836&hl=en&as_sdt=0,14 | 4 | 2,022 |
Towards Empirical Sandwich Bounds on the Rate-Distortion Function | 8 | iclr | 2 | 0 | 2023-06-18 09:43:43.185000 | https://github.com/mandt-lab/RD-sandwich | 9 | Towards empirical sandwich bounds on the rate-distortion function | https://scholar.google.com/scholar?cluster=3922055311859946203&hl=en&as_sdt=0,36 | 3 | 2,022 |
Fair Normalizing Flows | 6 | iclr | 2 | 0 | 2023-06-18 09:43:43.387000 | https://github.com/eth-sri/fnf | 16 | Fair normalizing flows | https://scholar.google.com/scholar?cluster=12495034483324120127&hl=en&as_sdt=0,11 | 6 | 2,022 |
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory | 38 | iclr | 10 | 0 | 2023-06-18 09:43:43.590000 | https://github.com/ghliu/sb-fbsde | 55 | Likelihood training of schr\" odinger bridge using forward-backward sdes theory | https://scholar.google.com/scholar?cluster=17490002779543160036&hl=en&as_sdt=0,23 | 2 | 2,022 |
Imitation Learning from Observations under Transition Model Disparity | 2 | iclr | 0 | 1 | 2023-06-18 09:43:43.793000 | https://github.com/tgangwani/ailo | 1 | Imitation Learning from Observations under Transition Model Disparity | https://scholar.google.com/scholar?cluster=2183334358740050451&hl=en&as_sdt=0,48 | 3 | 2,022 |
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks | 58 | iclr | 0 | 0 | 2023-06-18 09:43:43.996000 | https://github.com/rahimentezari/permutationinvariance | 19 | The role of permutation invariance in linear mode connectivity of neural networks | https://scholar.google.com/scholar?cluster=18352541695309676918&hl=en&as_sdt=0,5 | 1 | 2,022 |
Data Poisoning Won't Save You From Facial Recognition | 28 | iclr | 3 | 0 | 2023-06-18 09:43:44.203000 | https://github.com/ftramer/facecure | 9 | Data poisoning won't save you from facial recognition | https://scholar.google.com/scholar?cluster=12334665611277654156&hl=en&as_sdt=0,31 | 1 | 2,022 |
MetaMorph: Learning Universal Controllers with Transformers | 17 | iclr | 9 | 4 | 2023-06-18 09:43:44.406000 | https://github.com/agrimgupta92/metamorph | 65 | Metamorph: Learning universal controllers with transformers | https://scholar.google.com/scholar?cluster=5095019871599200934&hl=en&as_sdt=0,44 | 4 | 2,022 |
Illiterate DALL-E Learns to Compose | 44 | iclr | 10 | 4 | 2023-06-18 09:43:44.610000 | https://github.com/singhgautam/slate | 77 | Illiterate dall-e learns to compose | https://scholar.google.com/scholar?cluster=4019676252892800886&hl=en&as_sdt=0,21 | 1 | 2,022 |
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models | 24 | iclr | 2 | 0 | 2023-06-18 09:43:44.812000 | https://github.com/aypan17/reward-misspecification | 3 | The effects of reward misspecification: Mapping and mitigating misaligned models | https://scholar.google.com/scholar?cluster=13629255034936383162&hl=en&as_sdt=0,5 | 1 | 2,022 |
Counterfactual Plans under Distributional Ambiguity | 9 | iclr | 0 | 0 | 2023-06-18 09:43:45.015000 | https://github.com/ngocbh/copa | 3 | Counterfactual plans under distributional ambiguity | https://scholar.google.com/scholar?cluster=16318179024765381236&hl=en&as_sdt=0,33 | 2 | 2,022 |
Neural Parameter Allocation Search | 9 | iclr | 3 | 0 | 2023-06-18 09:43:45.218000 | https://github.com/bryanplummer/ssn | 4 | Neural parameter allocation search | https://scholar.google.com/scholar?cluster=15625823340904525164&hl=en&as_sdt=0,5 | 2 | 2,022 |
Collapse by Conditioning: Training Class-conditional GANs with Limited Data | 16 | iclr | 7 | 4 | 2023-06-18 09:43:45.422000 | https://github.com/mshahbazi72/transitional-cgan | 35 | Collapse by conditioning: Training class-conditional GANs with limited data | https://scholar.google.com/scholar?cluster=2177449249574403992&hl=en&as_sdt=0,5 | 2 | 2,022 |
Map Induction: Compositional spatial submap learning for efficient exploration in novel environments | 2 | iclr | 0 | 0 | 2023-06-18 09:43:45.625000 | https://github.com/s72sue/map-induction | 0 | Map Induction: Compositional spatial submap learning for efficient exploration in novel environments | https://scholar.google.com/scholar?cluster=15462260189293500047&hl=en&as_sdt=0,33 | 2 | 2,022 |
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? | 49 | iclr | 1 | 1 | 2023-06-18 09:43:45.833000 | https://github.com/inspire-group/proxy-distributions | 26 | Robust learning meets generative models: Can proxy distributions improve adversarial robustness? | https://scholar.google.com/scholar?cluster=15097099690109904849&hl=en&as_sdt=0,44 | 2 | 2,022 |
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap | 53 | iclr | 3 | 0 | 2023-06-18 09:43:46.036000 | https://github.com/zhangq327/arc | 22 | Chaos is a ladder: A new theoretical understanding of contrastive learning via augmentation overlap | https://scholar.google.com/scholar?cluster=7197581293948710911&hl=en&as_sdt=0,33 | 2 | 2,022 |
Language-biased image classification: evaluation based on semantic representations | 4 | iclr | 1 | 0 | 2023-06-18 09:43:46.239000 | https://github.com/flowersteam/picture-word-interference | 4 | Language-biased image classification: evaluation based on semantic representations | https://scholar.google.com/scholar?cluster=7894245425840424018&hl=en&as_sdt=0,43 | 7 | 2,022 |
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models | 40 | iclr | 37 | 0 | 2023-06-18 09:43:46.443000 | https://github.com/JonasGeiping/breaching | 178 | Robbing the fed: Directly obtaining private data in federated learning with modified models | https://scholar.google.com/scholar?cluster=15885116748368204506&hl=en&as_sdt=0,36 | 3 | 2,022 |
Permutation-Based SGD: Is Random Optimal? | 8 | iclr | 0 | 0 | 2023-06-18 09:43:46.646000 | https://github.com/shashankrajput/flipflop | 0 | Permutation-Based SGD: Is Random Optimal? | https://scholar.google.com/scholar?cluster=9197780273484525148&hl=en&as_sdt=0,19 | 1 | 2,022 |
Graph-less Neural Networks: Teaching Old MLPs New Tricks Via Distillation | 57 | iclr | 16 | 1 | 2023-06-18 09:43:46.848000 | https://github.com/snap-research/graphless-neural-networks | 64 | Graph-less neural networks: Teaching old mlps new tricks via distillation | https://scholar.google.com/scholar?cluster=14166973652994088038&hl=en&as_sdt=0,33 | 7 | 2,022 |
How many degrees of freedom do we need to train deep networks: a loss landscape perspective | 7 | iclr | 2 | 0 | 2023-06-18 09:43:47.052000 | https://github.com/ganguli-lab/degrees-of-freedom | 32 | How many degrees of freedom do we need to train deep networks: a loss landscape perspective | https://scholar.google.com/scholar?cluster=11943963795167204430&hl=en&as_sdt=0,5 | 2 | 2,022 |
Is Importance Weighting Incompatible with Interpolating Classifiers? | 14 | iclr | 3 | 1 | 2023-06-18 09:43:47.254000 | https://github.com/keawang/importance-weighting-interpolating-classifiers | 4 | Is importance weighting incompatible with interpolating classifiers? | https://scholar.google.com/scholar?cluster=5476028930081234281&hl=en&as_sdt=0,14 | 3 | 2,022 |
Mirror Descent Policy Optimization | 39 | iclr | 3 | 0 | 2023-06-18 09:43:47.458000 | https://github.com/manantomar/Mirror-Descent-Policy-Optimization | 28 | Mirror descent policy optimization | https://scholar.google.com/scholar?cluster=2587999722409846316&hl=en&as_sdt=0,11 | 2 | 2,022 |
Large-Scale Representation Learning on Graphs via Bootstrapping | 64 | iclr | 16 | 3 | 2023-06-18 09:43:47.662000 | https://github.com/nerdslab/bgrl | 68 | Large-scale representation learning on graphs via bootstrapping | https://scholar.google.com/scholar?cluster=3168526433938319234&hl=en&as_sdt=0,39 | 3 | 2,022 |
Neural Processes with Stochastic Attention: Paying more attention to the context dataset | 6 | iclr | 1 | 0 | 2023-06-18 09:43:47.865000 | https://github.com/mingyukim87/npwsa | 7 | Neural processes with stochastic attention: Paying more attention to the context dataset | https://scholar.google.com/scholar?cluster=4366830755369002835&hl=en&as_sdt=0,25 | 1 | 2,022 |
Geometric Transformers for Protein Interface Contact Prediction | 13 | iclr | 11 | 2 | 2023-06-18 09:43:48.069000 | https://github.com/bioinfomachinelearning/deepinteract | 47 | Geometric transformers for protein interface contact prediction | https://scholar.google.com/scholar?cluster=11431746960941491092&hl=en&as_sdt=0,5 | 1 | 2,022 |
IGLU: Efficient GCN Training via Lazy Updates | 5 | iclr | 0 | 0 | 2023-06-18 09:43:48.273000 | https://github.com/sdeepaknarayanan/iglu | 3 | IGLU: Efficient GCN Training via Lazy Updates | https://scholar.google.com/scholar?cluster=16548699335588161367&hl=en&as_sdt=0,5 | 3 | 2,022 |
Top-N: Equivariant Set and Graph Generation without Exchangeability | 11 | iclr | 0 | 1 | 2023-06-18 09:43:48.492000 | https://github.com/cvignac/top-n | 6 | Top-n: Equivariant set and graph generation without exchangeability | https://scholar.google.com/scholar?cluster=10023385156817268910&hl=en&as_sdt=0,43 | 4 | 2,022 |
LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 | 29 | iclr | 6 | 2 | 2023-06-18 09:43:48.700000 | https://github.com/qcwthu/lifelong-fewshot-language-learning | 49 | LFPT5: A unified framework for lifelong few-shot language learning based on prompt tuning of t5 | https://scholar.google.com/scholar?cluster=7716940912154178619&hl=en&as_sdt=0,1 | 3 | 2,022 |
On Non-Random Missing Labels in Semi-Supervised Learning | 6 | iclr | 0 | 0 | 2023-06-18 09:43:48.904000 | https://github.com/joyhuyy1412/cadr-fixmatch | 12 | On non-random missing labels in semi-supervised learning | https://scholar.google.com/scholar?cluster=2589366795376584946&hl=en&as_sdt=0,5 | 3 | 2,022 |
Mapping conditional distributions for domain adaptation under generalized target shift | 7 | iclr | 0 | 0 | 2023-06-18 09:43:49.107000 | https://github.com/mkirchmeyer/ostar | 5 | Mapping conditional distributions for domain adaptation under generalized target shift | https://scholar.google.com/scholar?cluster=3768189705171035948&hl=en&as_sdt=0,33 | 1 | 2,022 |
Adversarial Retriever-Ranker for Dense Text Retrieval | 47 | iclr | 6 | 5 | 2023-06-18 09:43:49.327000 | https://github.com/microsoft/ar2 | 56 | Adversarial retriever-ranker for dense text retrieval | https://scholar.google.com/scholar?cluster=9069461514425266804&hl=en&as_sdt=0,5 | 9 | 2,022 |
Normalization of Language Embeddings for Cross-Lingual Alignment | 4 | iclr | 1 | 0 | 2023-06-18 09:43:49.553000 | https://github.com/poaboagye/SpecNorm | 6 | Normalization of Language Embeddings for Cross-Lingual Alignment | https://scholar.google.com/scholar?cluster=10286218373304313543&hl=en&as_sdt=0,44 | 1 | 2,022 |
Boosting the Certified Robustness of L-infinity Distance Nets | 19 | iclr | 3 | 0 | 2023-06-18 09:43:49.755000 | https://github.com/zbh2047/L_inf-dist-net-v2 | 16 | Boosting the certified robustness of l-infinity distance nets | https://scholar.google.com/scholar?cluster=7903222136558927992&hl=en&as_sdt=0,33 | 1 | 2,022 |
Stochastic Training is Not Necessary for Generalization | 41 | iclr | 5 | 0 | 2023-06-18 09:43:49.958000 | https://github.com/jonasgeiping/fullbatchtraining | 36 | Stochastic training is not necessary for generalization | https://scholar.google.com/scholar?cluster=16676804811575846883&hl=en&as_sdt=0,5 | 2 | 2,022 |
GATSBI: Generative Adversarial Training for Simulation-Based Inference | 6 | iclr | 2 | 1 | 2023-06-18 09:43:50.162000 | https://github.com/mackelab/gatsbi | 11 | GATSBI: Generative adversarial training for simulation-based inference | https://scholar.google.com/scholar?cluster=15349002435008264502&hl=en&as_sdt=0,5 | 8 | 2,022 |
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients | 12 | iclr | 2 | 1 | 2023-06-18 09:43:50.373000 | https://github.com/mil-ad/prospr | 26 | Prospect pruning: Finding trainable weights at initialization using meta-gradients | https://scholar.google.com/scholar?cluster=8783006285808358460&hl=en&as_sdt=0,5 | 2 | 2,022 |
Generalized rectifier wavelet covariance models for texture synthesis | 2 | iclr | 2 | 1 | 2023-06-18 09:43:50.577000 | https://github.com/abrochar/wavelet-texture-synthesis | 4 | Generalized rectifier wavelet covariance models for texture synthesis | https://scholar.google.com/scholar?cluster=1160036386380312390&hl=en&as_sdt=0,5 | 2 | 2,022 |
Towards Evaluating the Robustness of Neural Networks Learned by Transduction | 8 | iclr | 2 | 0 | 2023-06-18 09:43:50.781000 | https://github.com/jfc43/eval-transductive-robustness | 4 | Towards evaluating the robustness of neural networks learned by transduction | https://scholar.google.com/scholar?cluster=10802124604610826531&hl=en&as_sdt=0,5 | 1 | 2,022 |
Understanding Intrinsic Robustness Using Label Uncertainty | 0 | iclr | 0 | 0 | 2023-06-18 09:43:50.986000 | https://github.com/xiaozhanguva/intrinsic_rob_lu | 3 | Understanding Intrinsic Robustness Using Label Uncertainty | https://scholar.google.com/scholar?cluster=7215793248994812724&hl=en&as_sdt=0,45 | 2 | 2,022 |
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization | 16 | iclr | 8 | 0 | 2023-06-18 09:43:51.190000 | https://github.com/illidanlab/SplitMix | 22 | Efficient split-mix federated learning for on-demand and in-situ customization | https://scholar.google.com/scholar?cluster=1074497134544260795&hl=en&as_sdt=0,26 | 3 | 2,022 |
Relational Surrogate Loss Learning | 2 | iclr | 6 | 0 | 2023-06-18 09:43:51.395000 | https://github.com/hunto/reloss | 35 | Relational surrogate loss learning | https://scholar.google.com/scholar?cluster=10424444268949840679&hl=en&as_sdt=0,5 | 3 | 2,022 |
Knowledge Infused Decoding | 9 | iclr | 8 | 4 | 2023-06-18 09:43:51.603000 | https://github.com/microsoft/kid | 65 | Knowledge infused decoding | https://scholar.google.com/scholar?cluster=5121405141535448243&hl=en&as_sdt=0,6 | 6 | 2,022 |
Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences | 1 | iclr | 5 | 2 | 2023-06-18 09:43:51.808000 | https://github.com/Multilevel-NN/torchbraid | 5 | Parallel training of gru networks with a multi-grid solver for long sequences | https://scholar.google.com/scholar?cluster=546461240895153656&hl=en&as_sdt=0,10 | 10 | 2,022 |
Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models | 8 | iclr | 0 | 0 | 2023-06-18 09:43:52.012000 | https://github.com/vietvo89/SparseEvoAttack.github.io | 0 | Query efficient decision based sparse attacks against black-box deep learning models | https://scholar.google.com/scholar?cluster=10824573856366104653&hl=en&as_sdt=0,39 | 0 | 2,022 |
Rethinking Goal-Conditioned Supervised Learning and Its Connection to Offline RL | 19 | iclr | 0 | 0 | 2023-06-18 09:43:52.215000 | https://github.com/yangrui2015/awgcsl | 21 | Rethinking goal-conditioned supervised learning and its connection to offline rl | https://scholar.google.com/scholar?cluster=889787684792010852&hl=en&as_sdt=0,43 | 2 | 2,022 |
PF-GNN: Differentiable particle filtering based approximation of universal graph representations | 5 | iclr | 0 | 0 | 2023-06-18 09:43:52.418000 | https://github.com/pfgnn/pf-gnn | 8 | PF-GNN: Differentiable particle filtering based approximation of universal graph representations | https://scholar.google.com/scholar?cluster=3626161026171680219&hl=en&as_sdt=0,5 | 1 | 2,022 |
Continual Normalization: Rethinking Batch Normalization for Online Continual Learning | 27 | iclr | 2 | 0 | 2023-06-18 09:43:52.621000 | https://github.com/phquang/continual-normalization | 8 | Continual normalization: Rethinking batch normalization for online continual learning | https://scholar.google.com/scholar?cluster=5393032746032394321&hl=en&as_sdt=0,10 | 2 | 2,022 |
Equivariant Graph Mechanics Networks with Constraints | 22 | iclr | 5 | 0 | 2023-06-18 09:43:52.824000 | https://github.com/hanjq17/gmn | 50 | Equivariant graph mechanics networks with constraints | https://scholar.google.com/scholar?cluster=3158185965758098235&hl=en&as_sdt=0,10 | 1 | 2,022 |
Convergent Graph Solvers | 9 | iclr | 2 | 0 | 2023-06-18 09:43:53.027000 | https://github.com/Junyoungpark/CGS | 22 | Convergent graph solvers | https://scholar.google.com/scholar?cluster=16292715563047713132&hl=en&as_sdt=0,47 | 1 | 2,022 |
Generalization Through the Lens of Leave-One-Out Error | 6 | iclr | 0 | 0 | 2023-06-18 09:43:53.231000 | https://github.com/gregorbachmann/leaveoneout | 2 | Generalization through the lens of leave-one-out error | https://scholar.google.com/scholar?cluster=17232289047191270815&hl=en&as_sdt=0,14 | 1 | 2,022 |
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks | 4 | iclr | 0 | 0 | 2023-06-18 09:43:53.434000 | https://github.com/StephanLorenzen/ExactIBAnalysisInQNNs | 4 | Information bottleneck: Exact analysis of (quantized) neural networks | https://scholar.google.com/scholar?cluster=14219492799643625897&hl=en&as_sdt=0,5 | 1 | 2,022 |
Attacking deep networks with surrogate-based adversarial black-box methods is easy | 5 | iclr | 1 | 0 | 2023-06-18 09:43:53.636000 | https://github.com/fiveai/gfcs | 6 | Attacking deep networks with surrogate-based adversarial black-box methods is easy | https://scholar.google.com/scholar?cluster=9504422673038646416&hl=en&as_sdt=0,5 | 5 | 2,022 |
Auto-scaling Vision Transformers without Training | 12 | iclr | 4 | 0 | 2023-06-18 09:43:53.840000 | https://github.com/vita-group/asvit | 72 | Auto-scaling vision transformers without training | https://scholar.google.com/scholar?cluster=10616211011095299898&hl=en&as_sdt=0,51 | 5 | 2,022 |
Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics | 2 | iclr | 1 | 0 | 2023-06-18 09:43:54.043000 | https://github.com/realcrane/fine-grained-differentiable-physics-a-yarn-level-model-for-fabrics | 4 | Fine-grained differentiable physics: a yarn-level model for fabrics | https://scholar.google.com/scholar?cluster=10505737509483577526&hl=en&as_sdt=0,15 | 2 | 2,022 |
Missingness Bias in Model Debugging | 13 | iclr | 0 | 0 | 2023-06-18 09:43:54.247000 | https://github.com/madrylab/missingness | 4 | Missingness bias in model debugging | https://scholar.google.com/scholar?cluster=2038886342850944148&hl=en&as_sdt=0,34 | 5 | 2,022 |
Conditional Object-Centric Learning from Video | 89 | iclr | 13 | 13 | 2023-06-18 09:43:54.450000 | https://github.com/google-research/slot-attention-video | 117 | Conditional object-centric learning from video | https://scholar.google.com/scholar?cluster=13987153077190983503&hl=en&as_sdt=0,10 | 7 | 2,022 |
Bayesian Neural Network Priors Revisited | 80 | iclr | 11 | 1 | 2023-06-18 09:43:54.653000 | https://github.com/ratschlab/bnn_priors | 51 | Bayesian neural network priors revisited | https://scholar.google.com/scholar?cluster=4553297460189369768&hl=en&as_sdt=0,37 | 6 | 2,022 |
Hybrid Random Features | 36 | iclr | 0 | 0 | 2023-06-18 09:43:54.856000 | https://github.com/arijitthegame/hybrid-sampling | 1 | Hybrid feature extraction and feature selection for improving recognition accuracy of handwritten numerals | https://scholar.google.com/scholar?cluster=15768792646769641893&hl=en&as_sdt=0,33 | 2 | 2,022 |
Salient ImageNet: How to discover spurious features in Deep Learning? | 33 | iclr | 3 | 0 | 2023-06-18 09:43:55.059000 | https://github.com/singlasahil14/salient_imagenet | 30 | Salient ImageNet: How to discover spurious features in Deep Learning? | https://scholar.google.com/scholar?cluster=14829986418742964472&hl=en&as_sdt=0,5 | 1 | 2,022 |
Differentiable DAG Sampling | 12 | iclr | 4 | 1 | 2023-06-18 09:43:55.262000 | https://github.com/sharpenb/Differentiable-DAG-Sampling | 25 | Differentiable DAG sampling | https://scholar.google.com/scholar?cluster=10667986307237653289&hl=en&as_sdt=0,22 | 2 | 2,022 |
Hierarchical Few-Shot Imitation with Skill Transition Models | 18 | iclr | 3 | 0 | 2023-06-18 09:43:55.465000 | https://github.com/kouroshhakha/fist | 8 | Hierarchical few-shot imitation with skill transition models | https://scholar.google.com/scholar?cluster=11314236649785473138&hl=en&as_sdt=0,14 | 2 | 2,022 |
GeneDisco: A Benchmark for Experimental Design in Drug Discovery | 10 | iclr | 10 | 5 | 2023-06-18 09:43:55.668000 | https://github.com/genedisco/genedisco | 30 | Genedisco: A benchmark for experimental design in drug discovery | https://scholar.google.com/scholar?cluster=10686323109700882145&hl=en&as_sdt=0,5 | 2 | 2,022 |
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting | 10 | iclr | 1 | 4 | 2023-06-18 09:43:55.871000 | https://github.com/hyunwookl/pm-memnet | 21 | Learning to remember patterns: Pattern matching memory networks for traffic forecasting | https://scholar.google.com/scholar?cluster=11909851991809109995&hl=en&as_sdt=0,39 | 2 | 2,022 |
Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks | 29 | iclr | 6 | 1 | 2023-06-18 09:43:56.074000 | https://github.com/graph-com/peg | 27 | Equivariant and stable positional encoding for more powerful graph neural networks | https://scholar.google.com/scholar?cluster=16446538441027140116&hl=en&as_sdt=0,33 | 1 | 2,022 |
A Deep Variational Approach to Clustering Survival Data | 19 | iclr | 12 | 0 | 2023-06-18 09:43:56.276000 | https://github.com/i6092467/vadesc | 22 | A deep variational approach to clustering survival data | https://scholar.google.com/scholar?cluster=12300997661063839145&hl=en&as_sdt=0,5 | 1 | 2,022 |
Charformer: Fast Character Transformers via Gradient-based Subword Tokenization | 69 | iclr | 7,332 | 1,026 | 2023-06-18 09:43:56.480000 | https://github.com/google-research/google-research | 29,803 | Charformer: Fast character transformers via gradient-based subword tokenization | https://scholar.google.com/scholar?cluster=13362289969236599063&hl=en&as_sdt=0,3 | 728 | 2,022 |
Knowledge Removal in Sampling-based Bayesian Inference | 9 | iclr | 1 | 0 | 2023-06-18 09:43:56.683000 | https://github.com/fshp971/mcmc-unlearning | 16 | Knowledge removal in sampling-based bayesian inference | https://scholar.google.com/scholar?cluster=3535045679170951379&hl=en&as_sdt=0,5 | 2 | 2,022 |
Igeood: An Information Geometry Approach to Out-of-Distribution Detection | 15 | iclr | 0 | 0 | 2023-06-18 09:43:56.888000 | https://github.com/edadaltocg/igeood | 6 | Igeood: An information geometry approach to out-of-distribution detection | https://scholar.google.com/scholar?cluster=14684067719933018833&hl=en&as_sdt=0,23 | 1 | 2,022 |
Bag of Instances Aggregation Boosts Self-supervised Distillation | 9 | iclr | 1 | 0 | 2023-06-18 09:43:57.090000 | https://github.com/haohang96/bingo | 29 | Bag of instances aggregation boosts self-supervised distillation | https://scholar.google.com/scholar?cluster=3290933725411237169&hl=en&as_sdt=0,5 | 7 | 2,022 |
Unrolling PALM for Sparse Semi-Blind Source Separation | 1 | iclr | 4 | 0 | 2023-06-18 09:43:57.294000 | https://github.com/mfahes/lpalm | 9 | Unrolling PALM for sparse semi-blind source separation | https://scholar.google.com/scholar?cluster=17855454750763330141&hl=en&as_sdt=0,47 | 2 | 2,022 |
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift | 29 | iclr | 7 | 5 | 2023-06-18 09:43:57.498000 | https://github.com/ts-kim/RevIN | 87 | Reversible instance normalization for accurate time-series forecasting against distribution shift | https://scholar.google.com/scholar?cluster=15726225809303254672&hl=en&as_sdt=0,36 | 6 | 2,022 |
Query Embedding on Hyper-Relational Knowledge Graphs | 12 | iclr | 4 | 0 | 2023-06-18 09:43:57.701000 | https://github.com/DimitrisAlivas/StarQE | 22 | Query embedding on hyper-relational knowledge graphs | https://scholar.google.com/scholar?cluster=4690980256531947393&hl=en&as_sdt=0,5 | 4 | 2,022 |
Neural Solvers for Fast and Accurate Numerical Optimal Control | 4 | iclr | 7 | 1 | 2023-06-18 09:43:57.904000 | https://github.com/diffeqml/diffeqml-research | 69 | Neural solvers for fast and accurate numerical optimal control | https://scholar.google.com/scholar?cluster=3860528662060774857&hl=en&as_sdt=0,47 | 4 | 2,022 |