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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
|
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