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Evaluating Robustness to Dataset Shift via Parametric Robustness Sets | 3 | neurips | 2 | 0 | 2023-06-16 22:58:36.748000 | https://github.com/clinicalml/parametric-robustness-evaluation | 4 | Evaluating robustness to dataset shift via parametric robustness sets | https://scholar.google.com/scholar?cluster=13183637754887103370&hl=en&as_sdt=0,44 | 8 | 2,022 |
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers | 78 | neurips | 68 | 21 | 2023-06-16 22:58:36.959000 | https://github.com/thudm/cogview2 | 862 | Cogview2: Faster and better text-to-image generation via hierarchical transformers | https://scholar.google.com/scholar?cluster=13690046467918196748&hl=en&as_sdt=0,24 | 36 | 2,022 |
Recursive Reasoning in Minimax Games: A Level $k$ Gradient Play Method | 1 | neurips | 0 | 0 | 2023-06-16 22:58:37.171000 | https://github.com/zichuliu/submission | 3 | Recursive Reasoning in Minimax Games: A Level Gradient Play Method | https://scholar.google.com/scholar?cluster=9230671350422718821&hl=en&as_sdt=0,5 | 1 | 2,022 |
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning | 4 | neurips | 2 | 0 | 2023-06-16 22:58:37.383000 | https://github.com/tajwarfahim/proactive_interventions | 6 | When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning | https://scholar.google.com/scholar?cluster=552685687177516453&hl=en&as_sdt=0,33 | 4 | 2,022 |
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering | 6 | neurips | 2 | 0 | 2023-06-16 22:58:37.594000 | https://github.com/kepsail/SHGP | 17 | Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering | https://scholar.google.com/scholar?cluster=11543677254444809912&hl=en&as_sdt=0,47 | 1 | 2,022 |
coVariance Neural Networks | 3 | neurips | 0 | 0 | 2023-06-16 22:58:37.809000 | https://github.com/pennbindlab/vnn | 2 | coVariance Neural Networks | https://scholar.google.com/scholar?cluster=5746884455895587002&hl=en&as_sdt=0,48 | 0 | 2,022 |
Two-Stream Network for Sign Language Recognition and Translation | 8 | neurips | 9 | 10 | 2023-06-16 22:58:38.020000 | https://github.com/FangyunWei/SLRT | 89 | Two-Stream Network for Sign Language Recognition and Translation | https://scholar.google.com/scholar?cluster=18038872806670059767&hl=en&as_sdt=0,5 | 3 | 2,022 |
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives | 3 | neurips | 1 | 1 | 2023-06-16 22:58:38.232000 | https://github.com/zfying/visfis | 4 | VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives | https://scholar.google.com/scholar?cluster=11221935189799088705&hl=en&as_sdt=0,34 | 1 | 2,022 |
Batch size-invariance for policy optimization | 6 | neurips | 14 | 0 | 2023-06-16 22:58:38.443000 | https://github.com/openai/ppo-ewma | 42 | Batch size-invariance for policy optimization | https://scholar.google.com/scholar?cluster=2296025407370141358&hl=en&as_sdt=0,5 | 2 | 2,022 |
Variational Model Perturbation for Source-Free Domain Adaptation | 4 | neurips | 1 | 0 | 2023-06-16 22:58:38.654000 | https://github.com/mmjing/variational_model_perturbation | 4 | Variational model perturbation for source-free domain adaptation | https://scholar.google.com/scholar?cluster=11797225835673378824&hl=en&as_sdt=0,18 | 1 | 2,022 |
A Unified Framework for Alternating Offline Model Training and Policy Learning | 3 | neurips | 1 | 0 | 2023-06-16 22:58:38.865000 | https://github.com/shentao-yang/ampl_neurips2022 | 7 | A Unified Framework for Alternating Offline Model Training and Policy Learning | https://scholar.google.com/scholar?cluster=1237354038205563544&hl=en&as_sdt=0,49 | 1 | 2,022 |
Peer Prediction for Learning Agents | 2 | neurips | 0 | 0 | 2023-06-16 22:58:39.076000 | https://github.com/fengtony686/peer-prediction-convergence | 2 | Peer Prediction for Learning Agents | https://scholar.google.com/scholar?cluster=6943061375108468617&hl=en&as_sdt=0,5 | 2 | 2,022 |
ShuffleMixer: An Efficient ConvNet for Image Super-Resolution | 8 | neurips | 7 | 5 | 2023-06-16 22:58:39.288000 | https://github.com/sunny2109/mobilesr-ntire2022 | 56 | ShuffleMixer: An Efficient ConvNet for Image Super-Resolution | https://scholar.google.com/scholar?cluster=15307398465334207013&hl=en&as_sdt=0,5 | 4 | 2,022 |
Locating and Editing Factual Associations in GPT | 77 | neurips | 52 | 10 | 2023-06-16 22:58:39.500000 | https://github.com/kmeng01/rome | 237 | Locating and editing factual associations in GPT | https://scholar.google.com/scholar?cluster=6676170860106418721&hl=en&as_sdt=0,45 | 6 | 2,022 |
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models | 11 | neurips | 2 | 0 | 2023-06-16 22:58:39.712000 | https://github.com/wimh966/outlier_suppression | 28 | Outlier suppression: Pushing the limit of low-bit transformer language models | https://scholar.google.com/scholar?cluster=10349903029841353318&hl=en&as_sdt=0,10 | 1 | 2,022 |
DataMUX: Data Multiplexing for Neural Networks | 4 | neurips | 8 | 0 | 2023-06-16 22:58:39.923000 | https://github.com/princeton-nlp/datamux | 53 | Datamux: Data multiplexing for neural networks | https://scholar.google.com/scholar?cluster=3955638905484690082&hl=en&as_sdt=0,33 | 7 | 2,022 |
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators | 2 | neurips | 0 | 0 | 2023-06-16 22:58:40.134000 | https://github.com/helena-yuhan-liu/modprop | 3 | Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators | https://scholar.google.com/scholar?cluster=2884524613792294582&hl=en&as_sdt=0,41 | 1 | 2,022 |
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering | 8 | neurips | 2 | 0 | 2023-06-16 22:58:40.346000 | https://github.com/gorilla-lab-scut/ttac | 32 | Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering | https://scholar.google.com/scholar?cluster=15662895642331219475&hl=en&as_sdt=0,36 | 1 | 2,022 |
Active Labeling: Streaming Stochastic Gradients | 1 | neurips | 0 | 0 | 2023-06-16 22:58:40.557000 | https://github.com/viviencabannes/active-labeling | 1 | Active Labeling: Streaming Stochastic Gradients | https://scholar.google.com/scholar?cluster=15951285451586696904&hl=en&as_sdt=0,44 | 2 | 2,022 |
TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation | 3 | neurips | 2 | 0 | 2023-06-16 22:58:40.769000 | https://github.com/air-discover/toist | 117 | TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation | https://scholar.google.com/scholar?cluster=12198126632106334540&hl=en&as_sdt=0,44 | 5 | 2,022 |
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning | 50 | neurips | 5 | 1 | 2023-06-16 22:58:40.980000 | https://github.com/weixin-liang/modality-gap | 46 | Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning | https://scholar.google.com/scholar?cluster=9899703375781547991&hl=en&as_sdt=0,5 | 3 | 2,022 |
Sequence Model Imitation Learning with Unobserved Contexts | 3 | neurips | 0 | 0 | 2023-06-16 22:58:41.198000 | https://github.com/gkswamy98/sequence_model_il | 3 | Sequence model imitation learning with unobserved contexts | https://scholar.google.com/scholar?cluster=2920440114291350523&hl=en&as_sdt=0,5 | 2 | 2,022 |
Merging Models with Fisher-Weighted Averaging | 35 | neurips | 2 | 0 | 2023-06-16 22:58:41.417000 | https://github.com/mmatena/model_merging | 28 | Merging models with fisher-weighted averaging | https://scholar.google.com/scholar?cluster=6334185910733231827&hl=en&as_sdt=0,38 | 1 | 2,022 |
FasterRisk: Fast and Accurate Interpretable Risk Scores | 2 | neurips | 1 | 0 | 2023-06-16 22:58:41.628000 | https://github.com/jiachangliu/fasterrisk | 17 | FasterRisk: Fast and Accurate Interpretable Risk Scores | https://scholar.google.com/scholar?cluster=16531707730202339054&hl=en&as_sdt=0,33 | 4 | 2,022 |
Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution | 12 | neurips | 1 | 0 | 2023-06-16 22:58:41.840000 | https://github.com/ut-austin-data-science-group/csw | 4 | Revisiting sliced Wasserstein on images: From vectorization to convolution | https://scholar.google.com/scholar?cluster=16632120304055085115&hl=en&as_sdt=0,5 | 0 | 2,022 |
A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning | 3 | neurips | 1 | 0 | 2023-06-16 22:58:42.052000 | https://github.com/ml-postech/rown | 18 | A rotated hyperbolic wrapped normal distribution for hierarchical representation learning | https://scholar.google.com/scholar?cluster=12794077703223787887&hl=en&as_sdt=0,5 | 7 | 2,022 |
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training | 4 | neurips | 4 | 1 | 2023-06-16 22:58:42.263000 | https://github.com/dem123456789/semifl-semi-supervised-federated-learning-for-unlabeled-clients-with-alternate-training | 13 | SemiFL: Semi-supervised federated learning for unlabeled clients with alternate training | https://scholar.google.com/scholar?cluster=15626144916318485438&hl=en&as_sdt=0,47 | 3 | 2,022 |
RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection | 2 | neurips | 1 | 0 | 2023-06-16 22:58:42.474000 | https://github.com/kingjamessong/rankfeat | 14 | RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection | https://scholar.google.com/scholar?cluster=15686388667832765832&hl=en&as_sdt=0,5 | 1 | 2,022 |
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model | 3 | neurips | 2 | 1 | 2023-06-16 22:58:42.686000 | https://github.com/srishtigautam/protovae | 12 | Protovae: A trustworthy self-explainable prototypical variational model | https://scholar.google.com/scholar?cluster=16989445926776575392&hl=en&as_sdt=0,47 | 1 | 2,022 |
If Influence Functions are the Answer, Then What is the Question? | 14 | neurips | 0 | 0 | 2023-06-16 22:58:42.897000 | https://github.com/pomonam/jax-influence | 7 | If Influence Functions are the Answer, Then What is the Question? | https://scholar.google.com/scholar?cluster=17591064813348027664&hl=en&as_sdt=0,23 | 1 | 2,022 |
Hierarchical classification at multiple operating points | 1 | neurips | 1 | 0 | 2023-06-16 22:58:43.107000 | https://github.com/jvlmdr/hiercls | 11 | Hierarchical classification at multiple operating points | https://scholar.google.com/scholar?cluster=6696040702671773446&hl=en&as_sdt=0,14 | 2 | 2,022 |
CARD: Classification and Regression Diffusion Models | 10 | neurips | 16 | 1 | 2023-06-16 22:58:43.319000 | https://github.com/xzwhan/card | 108 | CARD: Classification and regression diffusion models | https://scholar.google.com/scholar?cluster=13161498921981862309&hl=en&as_sdt=0,5 | 5 | 2,022 |
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness? | 2 | neurips | 0 | 0 | 2023-06-16 22:58:43.531000 | https://github.com/Tsili42/adv-ntk | 0 | What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness? | https://scholar.google.com/scholar?cluster=765440786974281242&hl=en&as_sdt=0,33 | 1 | 2,022 |
MoCoDA: Model-based Counterfactual Data Augmentation | 3 | neurips | 1 | 0 | 2023-06-16 22:58:43.742000 | https://github.com/spitis/mocoda | 8 | MoCoDA: Model-based Counterfactual Data Augmentation | https://scholar.google.com/scholar?cluster=7948314758864851403&hl=en&as_sdt=0,34 | 1 | 2,022 |
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification | 5 | neurips | 1 | 0 | 2023-06-16 22:58:43.954000 | https://github.com/activatedgeek/bayesian-classification | 18 | On uncertainty, tempering, and data augmentation in bayesian classification | https://scholar.google.com/scholar?cluster=5049318542021404538&hl=en&as_sdt=0,33 | 2 | 2,022 |
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters | 18 | neurips | 7,321 | 1,026 | 2023-06-16 22:58:44.165000 | https://github.com/google-research/google-research | 29,788 | Why so pessimistic? estimating uncertainties for offline rl through ensembles, and why their independence matters | https://scholar.google.com/scholar?cluster=6972415736332431556&hl=en&as_sdt=0,44 | 727 | 2,022 |
Advancing Model Pruning via Bi-level Optimization | 7 | neurips | 34 | 1 | 2023-06-16 22:58:44.377000 | https://github.com/optml-group/bip | 130 | Advancing Model Pruning via Bi-level Optimization | https://scholar.google.com/scholar?cluster=13543295038180870418&hl=en&as_sdt=0,43 | 24 | 2,022 |
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge | 61 | neurips | 93 | 24 | 2023-06-16 22:58:44.588000 | https://github.com/MineDojo/MineDojo | 1,310 | Minedojo: Building open-ended embodied agents with internet-scale knowledge | https://scholar.google.com/scholar?cluster=231281729668967714&hl=en&as_sdt=0,11 | 27 | 2,022 |
Truncated Matrix Power Iteration for Differentiable DAG Learning | 1 | neurips | 1 | 0 | 2023-06-16 22:58:44.800000 | https://github.com/zzhang1987/truncated-matrix-power-iteration-for-differentiable-dag-learning | 1 | Truncated Matrix Power Iteration for Differentiable DAG Learning | https://scholar.google.com/scholar?cluster=9166467047019565651&hl=en&as_sdt=0,5 | 2 | 2,022 |
Learning Debiased Classifier with Biased Committee | 8 | neurips | 0 | 0 | 2023-06-16 22:58:45.011000 | https://github.com/nayeong-v-kim/lwbc | 12 | Learning debiased classifier with biased committee | https://scholar.google.com/scholar?cluster=2775898324803541021&hl=en&as_sdt=0,44 | 1 | 2,022 |
Unifying Voxel-based Representation with Transformer for 3D Object Detection | 53 | neurips | 12 | 8 | 2023-06-16 22:58:45.223000 | https://github.com/dvlab-research/uvtr | 187 | Unifying voxel-based representation with transformer for 3d object detection | https://scholar.google.com/scholar?cluster=2319515305755204659&hl=en&as_sdt=0,5 | 6 | 2,022 |
On Scrambling Phenomena for Randomly Initialized Recurrent Networks | 0 | neurips | 1 | 0 | 2023-06-16 22:58:45.435000 | https://github.com/steliostavroulakis/chaos_rnns | 2 | On Scrambling Phenomena for Randomly Initialized Recurrent Networks | https://scholar.google.com/scholar?cluster=7078078811342818102&hl=en&as_sdt=0,43 | 1 | 2,022 |
Learning to Branch with Tree MDPs | 14 | neurips | 9 | 2 | 2023-06-16 22:58:45.645000 | https://github.com/lascavana/rl2branch | 7 | Learning to branch with tree mdps | https://scholar.google.com/scholar?cluster=5953866441971807828&hl=en&as_sdt=0,47 | 1 | 2,022 |
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs | 48 | neurips | 9 | 0 | 2023-06-16 22:58:45.856000 | https://github.com/twitter-research/neural-sheaf-diffusion | 43 | Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in gnns | https://scholar.google.com/scholar?cluster=14875672783767429079&hl=en&as_sdt=0,50 | 5 | 2,022 |
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios | 1 | neurips | 0 | 1 | 2023-06-16 22:58:46.068000 | https://github.com/hendrycks/emodiversity | 7 | How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios | https://scholar.google.com/scholar?cluster=7719508504871552377&hl=en&as_sdt=0,33 | 4 | 2,022 |
On Elimination Strategies for Bandit Fixed-Confidence Identification | 0 | neurips | 0 | 0 | 2023-06-16 22:58:46.279000 | https://github.com/andreatirinzoni/bandit-elimination | 2 | On Elimination Strategies for Bandit Fixed-Confidence Identification | https://scholar.google.com/scholar?cluster=7723207511483790063&hl=en&as_sdt=0,5 | 1 | 2,022 |
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture | 7 | neurips | 3 | 0 | 2023-06-16 22:58:46.492000 | https://github.com/mo666666/when-adversarial-training-meets-vision-transformers | 13 | When adversarial training meets vision transformers: Recipes from training to architecture | https://scholar.google.com/scholar?cluster=4979980809128856359&hl=en&as_sdt=0,31 | 2 | 2,022 |
Private Estimation with Public Data | 48 | neurips | 0 | 0 | 2023-06-16 22:58:46.704000 | https://github.com/alexbie98/1pub-priv-mean-est | 0 | Sharing social network data: differentially private estimation of exponential family random-graph models | https://scholar.google.com/scholar?cluster=15510004526104950140&hl=en&as_sdt=0,5 | 1 | 2,022 |
Most Activation Functions Can Win the Lottery Without Excessive Depth | 2 | neurips | 0 | 0 | 2023-06-16 22:58:46.915000 | https://github.com/relationalml/lt-existence | 2 | Most activation functions can win the lottery without excessive depth | https://scholar.google.com/scholar?cluster=2762350726974066343&hl=en&as_sdt=0,47 | 0 | 2,022 |
Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition | 1 | neurips | 4 | 0 | 2023-06-16 22:58:47.127000 | https://github.com/kdhht2334/elim_fer | 24 | Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition | https://scholar.google.com/scholar?cluster=9348912629792592227&hl=en&as_sdt=0,32 | 1 | 2,022 |
SHINE: SubHypergraph Inductive Neural nEtwork | 1 | neurips | 2 | 0 | 2023-06-16 22:58:47.339000 | https://github.com/luoyuanlab/shine | 9 | SHINE: SubHypergraph Inductive Neural nEtwork | https://scholar.google.com/scholar?cluster=5043594054485770914&hl=en&as_sdt=0,44 | 2 | 2,022 |
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics | 7 | neurips | 0 | 0 | 2023-06-16 22:58:47.553000 | https://github.com/antoninschrab/agginc-paper | 3 | Efficient Aggregated Kernel Tests using Incomplete -statistics | https://scholar.google.com/scholar?cluster=14498936236963978885&hl=en&as_sdt=0,5 | 1 | 2,022 |
Influencing Long-Term Behavior in Multiagent Reinforcement Learning | 7 | neurips | 5 | 0 | 2023-06-16 22:58:47.766000 | https://github.com/dkkim93/further | 16 | Influencing long-term behavior in multiagent reinforcement learning | https://scholar.google.com/scholar?cluster=12230303792245064491&hl=en&as_sdt=0,33 | 1 | 2,022 |
Quantized Training of Gradient Boosting Decision Trees | 0 | neurips | 1 | 0 | 2023-06-16 22:58:47.978000 | https://github.com/quantized-gbdt/quantized-gbdt | 9 | Quantized Training of Gradient Boosting Decision Trees | https://scholar.google.com/scholar?cluster=4058197876307352226&hl=en&as_sdt=0,10 | 2 | 2,022 |
Data Distributional Properties Drive Emergent In-Context Learning in Transformers | 37 | neurips | 10 | 3 | 2023-06-16 22:58:48.192000 | https://github.com/deepmind/emergent_in_context_learning | 53 | Data distributional properties drive emergent in-context learning in transformers | https://scholar.google.com/scholar?cluster=16209854431595052414&hl=en&as_sdt=0,33 | 3 | 2,022 |
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks | 5 | neurips | 1 | 1 | 2023-06-16 22:58:48.417000 | https://github.com/mansheej/lth_diet | 8 | Lottery tickets on a data diet: Finding initializations with sparse trainable networks | https://scholar.google.com/scholar?cluster=17203687298264030475&hl=en&as_sdt=0,5 | 3 | 2,022 |
Memory safe computations with XLA compiler | 1 | neurips | 2 | 5 | 2023-06-16 22:58:48.630000 | https://github.com/awav/tensorflow | 1 | Memory safe computations with XLA compiler | https://scholar.google.com/scholar?cluster=18390099303465948139&hl=en&as_sdt=0,47 | 1 | 2,022 |
Towards Theoretically Inspired Neural Initialization Optimization | 0 | neurips | 1 | 0 | 2023-06-16 22:58:48.841000 | https://github.com/HarborYuan/GradCosine | 8 | Towards Theoretically Inspired Neural Initialization Optimization | https://scholar.google.com/scholar?cluster=6350876524339921816&hl=en&as_sdt=0,14 | 2 | 2,022 |
AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies | 0 | neurips | 3 | 4 | 2023-06-16 22:58:49.052000 | https://github.com/lisiyao21/animerun | 69 | AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies | https://scholar.google.com/scholar?cluster=2206932835628309531&hl=en&as_sdt=0,5 | 11 | 2,022 |
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization | 6 | neurips | 0 | 0 | 2023-06-16 22:58:49.265000 | https://github.com/yuri-k111/neurips2022_code | 0 | Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization | https://scholar.google.com/scholar?cluster=14537579459449046280&hl=en&as_sdt=0,5 | 1 | 2,022 |
Efficient learning of nonlinear prediction models with time-series privileged information | 2 | neurips | 1 | 0 | 2023-06-16 22:58:49.477000 | https://github.com/healthy-ai/glupts | 0 | Efficient learning of nonlinear prediction models with time-series privileged information | https://scholar.google.com/scholar?cluster=18191800989177614120&hl=en&as_sdt=0,5 | 0 | 2,022 |
Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training | 0 | neurips | 1 | 0 | 2023-06-16 22:58:49.693000 | https://github.com/snap-research/spfde | 8 | Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training | https://scholar.google.com/scholar?cluster=8941325294447745327&hl=en&as_sdt=0,33 | 4 | 2,022 |
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity | 1 | neurips | 0 | 0 | 2023-06-16 22:58:49.904000 | https://github.com/andrew-cullen/doublebubble | 1 | Double bubble, toil and trouble: enhancing certified robustness through transitivity | https://scholar.google.com/scholar?cluster=15829183381578160837&hl=en&as_sdt=0,44 | 2 | 2,022 |
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch | 35 | neurips | 4 | 2 | 2023-06-16 22:58:50.117000 | https://github.com/hsouri/Sleeper-Agent | 45 | Sleeper agent: Scalable hidden trigger backdoors for neural networks trained from scratch | https://scholar.google.com/scholar?cluster=9248176712796866973&hl=en&as_sdt=0,1 | 3 | 2,022 |
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models | 2 | neurips | 2 | 0 | 2023-06-16 22:58:50.328000 | https://github.com/llyx97/sparse-and-robust-plm | 20 | A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models | https://scholar.google.com/scholar?cluster=12965321937141963299&hl=en&as_sdt=0,39 | 1 | 2,022 |
Pareto Set Learning for Expensive Multi-Objective Optimization | 6 | neurips | 5 | 1 | 2023-06-16 22:58:50.539000 | https://github.com/xi-l/psl-mobo | 6 | Pareto Set Learning for Expensive Multi-Objective Optimization | https://scholar.google.com/scholar?cluster=16507134535796504804&hl=en&as_sdt=0,32 | 3 | 2,022 |
Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem | 0 | neurips | 0 | 0 | 2023-06-16 22:58:50.750000 | https://github.com/raunakkmr/non-monotonic-resource-utilization-in-the-bandits-with-knapsacks-problem-code | 3 | Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem | https://scholar.google.com/scholar?cluster=12804888557627073813&hl=en&as_sdt=0,5 | 1 | 2,022 |
Efficient identification of informative features in simulation-based inference | 2 | neurips | 0 | 0 | 2023-06-16 22:58:50.962000 | https://github.com/berenslab/fslm_repo | 0 | Efficient identification of informative features in simulation-based inference | https://scholar.google.com/scholar?cluster=9408830879778530143&hl=en&as_sdt=0,5 | 2 | 2,022 |
Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift | 18 | neurips | 0 | 0 | 2023-06-16 22:58:51.173000 | https://github.com/kebaek/agreement-on-the-line | 1 | Agreement-on-the-line: Predicting the performance of neural networks under distribution shift | https://scholar.google.com/scholar?cluster=16040179081922789785&hl=en&as_sdt=0,15 | 1 | 2,022 |
Large-Scale Differentiable Causal Discovery of Factor Graphs | 7 | neurips | 2 | 1 | 2023-06-16 22:58:51.385000 | https://github.com/genentech/dcdfg | 15 | Large-scale differentiable causal discovery of factor graphs | https://scholar.google.com/scholar?cluster=336010023327316095&hl=en&as_sdt=0,29 | 2 | 2,022 |
Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss | 1 | neurips | 0 | 0 | 2023-06-16 22:58:51.597000 | https://github.com/IBM/JLPlusPlus | 5 | Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss | https://scholar.google.com/scholar?cluster=5393693491306876887&hl=en&as_sdt=0,5 | 3 | 2,022 |
Few-shot Image Generation via Adaptation-Aware Kernel Modulation | 5 | neurips | 1 | 0 | 2023-06-16 22:58:51.808000 | https://github.com/yunqing-me/AdAM | 10 | Few-shot image generation via adaptation-aware kernel modulation | https://scholar.google.com/scholar?cluster=4742360547792769040&hl=en&as_sdt=0,5 | 2 | 2,022 |
Learning to Follow Instructions in Text-Based Games | 5 | neurips | 0 | 0 | 2023-06-16 22:58:52.019000 | https://github.com/mathieutuli/ltl-gata | 4 | Learning to follow instructions in text-based games | https://scholar.google.com/scholar?cluster=2065963607262919529&hl=en&as_sdt=0,44 | 2 | 2,022 |
Improving Variational Autoencoders with Density Gap-based Regularization | 1 | neurips | 0 | 0 | 2023-06-16 22:58:52.230000 | https://github.com/zhangjf-nlp/dg-vaes | 3 | Improving Variational Autoencoders with Density Gap-based Regularization | https://scholar.google.com/scholar?cluster=5008460593978673315&hl=en&as_sdt=0,26 | 1 | 2,022 |
RISE: Robust Individualized Decision Learning with Sensitive Variables | 5 | neurips | 1 | 0 | 2023-06-16 22:58:52.442000 | https://github.com/ellenxtan/rise | 6 | Rise: Robust individualized decision learning with sensitive variables | https://scholar.google.com/scholar?cluster=14552433169165007620&hl=en&as_sdt=0,39 | 3 | 2,022 |
Beyond neural scaling laws: beating power law scaling via data pruning | 67 | neurips | 2 | 0 | 2023-06-16 22:58:52.653000 | https://github.com/rgeirhos/dataset-pruning-metrics | 19 | Beyond neural scaling laws: beating power law scaling via data pruning | https://scholar.google.com/scholar?cluster=14309238955014761855&hl=en&as_sdt=0,33 | 1 | 2,022 |
Maximum Class Separation as Inductive Bias in One Matrix | 6 | neurips | 2 | 1 | 2023-06-16 22:58:52.866000 | https://github.com/tkasarla/max-separation-as-inductive-bias | 23 | Maximum class separation as inductive bias in one matrix | https://scholar.google.com/scholar?cluster=15315241654161942906&hl=en&as_sdt=0,48 | 5 | 2,022 |
Redundant representations help generalization in wide neural networks | 1 | neurips | 0 | 0 | 2023-06-16 22:58:53.077000 | https://github.com/diegodoimo/redundant_representation | 1 | Redundant representations help generalization in wide neural networks | https://scholar.google.com/scholar?cluster=11398110079007886002&hl=en&as_sdt=0,10 | 1 | 2,022 |
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning | 3 | neurips | 0 | 0 | 2023-06-16 22:58:53.289000 | https://github.com/romainchor/datascience | 0 | Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning | https://scholar.google.com/scholar?cluster=16572219026525753208&hl=en&as_sdt=0,5 | 2 | 2,022 |
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes | 0 | neurips | 0 | 0 | 2023-06-16 22:58:53.501000 | https://github.com/BatsResearch/mazzetto-neurips22-code | 2 | Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes | https://scholar.google.com/scholar?cluster=8233985458905085430&hl=en&as_sdt=0,34 | 3 | 2,022 |
Graph Neural Networks with Adaptive Readouts | 5 | neurips | 1 | 0 | 2023-06-16 22:58:53.713000 | https://github.com/davidbuterez/gnn-neural-readouts | 16 | Graph Neural Networks with Adaptive Readouts | https://scholar.google.com/scholar?cluster=16233387568833455709&hl=en&as_sdt=0,22 | 2 | 2,022 |
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games | 5 | neurips | 3 | 1 | 2023-06-16 22:58:53.924000 | https://github.com/shichangzh/gstarx | 8 | GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games | https://scholar.google.com/scholar?cluster=7993639036305387244&hl=en&as_sdt=0,5 | 2 | 2,022 |
Low-Rank Modular Reinforcement Learning via Muscle Synergy | 2 | neurips | 1 | 1 | 2023-06-16 22:58:54.136000 | https://github.com/drdh/synergy-rl | 4 | Low-Rank Modular Reinforcement Learning via Muscle Synergy | https://scholar.google.com/scholar?cluster=15949324168109968004&hl=en&as_sdt=0,5 | 1 | 2,022 |
Faster Deep Reinforcement Learning with Slower Online Network | 0 | neurips | 1 | 0 | 2023-06-16 22:58:54.348000 | https://github.com/amazon-research/fast-rl-with-slow-updates | 15 | Faster deep reinforcement learning with slower online network | https://scholar.google.com/scholar?cluster=8991673976969240285&hl=en&as_sdt=0,5 | 1 | 2,022 |
Green Hierarchical Vision Transformer for Masked Image Modeling | 24 | neurips | 5 | 1 | 2023-06-16 22:58:54.559000 | https://github.com/layneh/greenmim | 146 | Green hierarchical vision transformer for masked image modeling | https://scholar.google.com/scholar?cluster=5575721172969217810&hl=en&as_sdt=0,5 | 3 | 2,022 |
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation | 7 | neurips | 2 | 0 | 2023-06-16 22:58:54.771000 | https://github.com/montefiore-ai/balanced-nre | 11 | Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation | https://scholar.google.com/scholar?cluster=2070151199404142004&hl=en&as_sdt=0,5 | 4 | 2,022 |
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs | 19 | neurips | 0 | 0 | 2023-06-16 22:58:54.982000 | https://github.com/eboursier/gfdynamics | 4 | Gradient flow dynamics of shallow relu networks for square loss and orthogonal inputs | https://scholar.google.com/scholar?cluster=7952131240669274846&hl=en&as_sdt=0,5 | 2 | 2,022 |
Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited | 0 | neurips | 0 | 0 | 2023-06-16 22:58:55.193000 | https://github.com/nlskrg/node_centric_walk_kernels | 0 | Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited | https://scholar.google.com/scholar?cluster=3035963861391187619&hl=en&as_sdt=0,33 | 1 | 2,022 |
Multi-agent Dynamic Algorithm Configuration | 9 | neurips | 7 | 0 | 2023-06-16 22:58:55.423000 | https://github.com/lamda-bbo/madac | 18 | Multi-agent Dynamic Algorithm Configuration | https://scholar.google.com/scholar?cluster=18124893361074952166&hl=en&as_sdt=0,5 | 1 | 2,022 |
TaSIL: Taylor Series Imitation Learning | 9 | neurips | 1 | 0 | 2023-06-16 22:58:55.634000 | https://github.com/unstable-zeros/tasil | 3 | Tasil: Taylor series imitation learning | https://scholar.google.com/scholar?cluster=5196638265754138969&hl=en&as_sdt=0,33 | 1 | 2,022 |
Continuous MDP Homomorphisms and Homomorphic Policy Gradient | 2 | neurips | 0 | 0 | 2023-06-16 22:58:55.846000 | https://github.com/sahandrez/homomorphic_policy_gradient | 12 | Continuous MDP Homomorphisms and Homomorphic Policy Gradient | https://scholar.google.com/scholar?cluster=765221308115729349&hl=en&as_sdt=0,33 | 3 | 2,022 |
Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance | 5 | neurips | 2 | 0 | 2023-06-16 22:58:56.058000 | https://github.com/uw-madison-lee-lab/score-wasserstein | 12 | Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance | https://scholar.google.com/scholar?cluster=2627264767154274760&hl=en&as_sdt=0,14 | 2 | 2,022 |
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs | 11 | neurips | 1 | 1 | 2023-06-16 22:58:56.287000 | https://github.com/yangzez/ood-link-prediction-generalization-mpnn | 1 | Ood link prediction generalization capabilities of message-passing gnns in larger test graphs | https://scholar.google.com/scholar?cluster=14377211411789123424&hl=en&as_sdt=0,36 | 1 | 2,022 |
Algorithms with Prediction Portfolios | 1 | neurips | 0 | 0 | 2023-06-16 22:58:56.499000 | https://github.com/tlavastida/predictionportfolios | 1 | Algorithms with Prediction Portfolios | https://scholar.google.com/scholar?cluster=15626362245695114867&hl=en&as_sdt=0,5 | 2 | 2,022 |
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs | 3 | neurips | 3 | 0 | 2023-06-16 22:58:56.711000 | https://github.com/csuastt/HardConstraint | 4 | A unified Hard-constraint framework for solving geometrically complex PDEs | https://scholar.google.com/scholar?cluster=12354346662201419102&hl=en&as_sdt=0,5 | 2 | 2,022 |
Optimal and Adaptive Monteiro-Svaiter Acceleration | 11 | neurips | 0 | 0 | 2023-06-16 22:58:56.922000 | https://github.com/danielle-hausler/ms-optimal | 1 | Optimal and adaptive monteiro-svaiter acceleration | https://scholar.google.com/scholar?cluster=6181840744509618668&hl=en&as_sdt=0,5 | 1 | 2,022 |
SparCL: Sparse Continual Learning on the Edge | 8 | neurips | 2 | 0 | 2023-06-16 22:58:57.134000 | https://github.com/neu-spiral/SparCL | 14 | Sparcl: Sparse continual learning on the edge | https://scholar.google.com/scholar?cluster=7160494277089589433&hl=en&as_sdt=0,5 | 5 | 2,022 |
Adaptively Exploiting d-Separators with Causal Bandits | 3 | neurips | 0 | 0 | 2023-06-16 22:58:57.345000 | https://github.com/blairbilodeau/adaptive-causal-bandits | 5 | Adaptively exploiting d-separators with causal bandits | https://scholar.google.com/scholar?cluster=10113006239041370847&hl=en&as_sdt=0,23 | 1 | 2,022 |
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP | 37 | neurips | 12 | 1 | 2023-06-16 22:58:57.558000 | https://github.com/ml-jku/cloob | 143 | Cloob: Modern hopfield networks with infoloob outperform clip | https://scholar.google.com/scholar?cluster=3714890763443837424&hl=en&as_sdt=0,33 | 9 | 2,022 |
Language Conditioned Spatial Relation Reasoning for 3D Object Grounding | 3 | neurips | 3 | 1 | 2023-06-16 22:58:57.770000 | https://github.com/cshizhe/vil3dref | 31 | Language Conditioned Spatial Relation Reasoning for 3D Object Grounding | https://scholar.google.com/scholar?cluster=14666951856631208351&hl=en&as_sdt=0,14 | 2 | 2,022 |
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