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|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
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