title
string | citations_google_scholar
int64 | conference
string | forks
int64 | issues
int64 | lastModified
string | repo_url
string | stars
int64 | title_google_scholar
string | url_google_scholar
string | watchers
int64 | year
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks | 9 | iclr | 1 | 0 | 2023-06-18 09:44:59.579000 | https://github.com/anneharrington/adversarially-robust-periphery | 2 | Finding biological plausibility for adversarially robust features via metameric tasks | https://scholar.google.com/scholar?cluster=16970067038193259370&hl=en&as_sdt=0,11 | 2 | 2,022 |
Omni-Dimensional Dynamic Convolution | 38 | iclr | 22 | 1 | 2023-06-18 09:44:59.782000 | https://github.com/osvai/odconv | 184 | Omni-dimensional dynamic convolution | https://scholar.google.com/scholar?cluster=3010782089276051732&hl=en&as_sdt=0,5 | 2 | 2,022 |
EViT: Expediting Vision Transformers via Token Reorganizations | 94 | iclr | 15 | 13 | 2023-06-18 09:44:59.986000 | https://github.com/youweiliang/evit | 122 | Not all patches are what you need: Expediting vision transformers via token reorganizations | https://scholar.google.com/scholar?cluster=13367059770507522630&hl=en&as_sdt=0,5 | 3 | 2,022 |
Policy improvement by planning with Gumbel | 14 | iclr | 152 | 0 | 2023-06-18 09:45:00.188000 | https://github.com/deepmind/mctx | 1,877 | Policy improvement by planning with Gumbel | https://scholar.google.com/scholar?cluster=7251499641538462070&hl=en&as_sdt=0,5 | 27 | 2,022 |
Learning Optimal Conformal Classifiers | 24 | iclr | 6 | 2 | 2023-06-18 09:45:00.392000 | https://github.com/deepmind/conformal_training | 69 | Learning optimal conformal classifiers | https://scholar.google.com/scholar?cluster=5366968417529245684&hl=en&as_sdt=0,23 | 4 | 2,022 |
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations | 186 | iclr | 979 | 108 | 2023-06-18 09:45:00.601000 | https://github.com/google-research/vision_transformer | 7,393 | When vision transformers outperform resnets without pre-training or strong data augmentations | https://scholar.google.com/scholar?cluster=4049796223449388186&hl=en&as_sdt=0,5 | 83 | 2,022 |
Long Expressive Memory for Sequence Modeling | 16 | iclr | 11 | 0 | 2023-06-18 09:45:00.804000 | https://github.com/tk-rusch/lem | 59 | Long expressive memory for sequence modeling | https://scholar.google.com/scholar?cluster=10849000047191483143&hl=en&as_sdt=0,5 | 2 | 2,022 |
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy | 94 | iclr | 88 | 10 | 2023-06-18 09:45:01.008000 | https://github.com/thuml/Anomaly-Transformer | 361 | Anomaly transformer: Time series anomaly detection with association discrepancy | https://scholar.google.com/scholar?cluster=12471325118803603403&hl=en&as_sdt=0,47 | 7 | 2,022 |
Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning | 4 | iclr | 0 | 0 | 2023-06-18 09:45:01.211000 | https://github.com/Haichao-Zhang/generative-planning | 6 | Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning | https://scholar.google.com/scholar?cluster=14730527943022398215&hl=en&as_sdt=0,5 | 3 | 2,022 |
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning | 46 | iclr | 3 | 0 | 2023-06-18 09:45:01.415000 | https://github.com/baichenjia/pbrl | 24 | Pessimistic bootstrapping for uncertainty-driven offline reinforcement learning | https://scholar.google.com/scholar?cluster=8122293342821829012&hl=en&as_sdt=0,19 | 2 | 2,022 |
Equivariant Subgraph Aggregation Networks | 74 | iclr | 8 | 1 | 2023-06-18 09:45:01.619000 | https://github.com/beabevi/esan | 68 | Equivariant subgraph aggregation networks | https://scholar.google.com/scholar?cluster=6011099715044788714&hl=en&as_sdt=0,5 | 5 | 2,022 |
How Do Vision Transformers Work? | 191 | iclr | 71 | 5 | 2023-06-18 09:45:01.823000 | https://github.com/xxxnell/how-do-vits-work | 712 | How do vision transformers work? | https://scholar.google.com/scholar?cluster=8029612233773990665&hl=en&as_sdt=0,5 | 6 | 2,022 |
Variational methods for simulation-based inference | 18 | iclr | 4 | 0 | 2023-06-18 09:45:02.028000 | https://github.com/mackelab/snvi_repo | 3 | Variational methods for simulation-based inference | https://scholar.google.com/scholar?cluster=16337891944937937425&hl=en&as_sdt=0,33 | 1 | 2,022 |
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs | 150 | iclr | 57 | 25 | 2023-06-18 09:45:02.231000 | https://github.com/NVlabs/denoising-diffusion-gan | 548 | Tackling the generative learning trilemma with denoising diffusion GANs | https://scholar.google.com/scholar?cluster=9436697539752906895&hl=en&as_sdt=0,32 | 40 | 2,022 |
Imbedding Deep Neural Networks | 0 | iclr | 0 | 0 | 2023-06-18 09:45:02.435000 | https://github.com/andrw3000/inimnet | 2 | Imbedding Deep Neural Networks | https://scholar.google.com/scholar?cluster=10680544455244654489&hl=en&as_sdt=0,11 | 2 | 2,022 |
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration | 25 | iclr | 3 | 0 | 2023-06-18 09:45:02.640000 | https://github.com/cianeastwood/bufr | 13 | Source-free adaptation to measurement shift via bottom-up feature restoration | https://scholar.google.com/scholar?cluster=13912921237099843796&hl=en&as_sdt=0,33 | 2 | 2,022 |
Emergent Communication at Scale | 19 | iclr | 3 | 1 | 2023-06-18 09:45:02.844000 | https://github.com/deepmind/emergent_communication_at_scale | 25 | Emergent communication at scale | https://scholar.google.com/scholar?cluster=4797610842429518149&hl=en&as_sdt=0,5 | 4 | 2,022 |
Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings | 4 | iclr | 1 | 0 | 2023-06-18 09:45:03.048000 | https://github.com/KnowledgeDiscovery/SCGM | 2 | Superclass-conditional Gaussian mixture model for learning fine-grained embeddings | https://scholar.google.com/scholar?cluster=16398991451441380752&hl=en&as_sdt=0,39 | 0 | 2,022 |
IntSGD: Adaptive Floatless Compression of Stochastic Gradients | 16 | iclr | 1 | 0 | 2023-06-18 09:45:03.251000 | https://github.com/bokunwang1/intsgd | 2 | IntSGD: Adaptive floatless compression of stochastic gradients | https://scholar.google.com/scholar?cluster=16969044896100418296&hl=en&as_sdt=0,5 | 1 | 2,022 |
PAC-Bayes Information Bottleneck | 9 | iclr | 1 | 0 | 2023-06-18 09:45:03.455000 | https://github.com/ryanwangzf/pac-bayes-ib | 36 | PAC-bayes information bottleneck | https://scholar.google.com/scholar?cluster=8594070314886177653&hl=en&as_sdt=0,33 | 3 | 2,022 |
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing | 37 | iclr | 3 | 0 | 2023-06-18 09:45:03.659000 | https://github.com/epfml/byzantine-robust-noniid-optimizer | 10 | Byzantine-robust learning on heterogeneous datasets via bucketing | https://scholar.google.com/scholar?cluster=10653774941778356470&hl=en&as_sdt=0,5 | 4 | 2,022 |
Label Encoding for Regression Networks | 2 | iclr | 2 | 0 | 2023-06-18 09:45:03.862000 | https://github.com/ubc-aamodt-group/bel_regression | 7 | Label encoding for regression networks | https://scholar.google.com/scholar?cluster=17134575941397611216&hl=en&as_sdt=0,5 | 2 | 2,022 |
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks | 16 | iclr | 5 | 0 | 2023-06-18 09:45:04.065000 | https://github.com/martenlienen/finite-element-networks | 55 | Learning the dynamics of physical systems from sparse observations with finite element networks | https://scholar.google.com/scholar?cluster=10753878238660840723&hl=en&as_sdt=0,1 | 2 | 2,022 |