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Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome
| 1 |
neurips
| 0 | 1 |
2023-06-16 22:58:57.980000
|
https://github.com/cunningham-lab/augcoda
| 1 |
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome
|
https://scholar.google.com/scholar?cluster=14450220872219871310&hl=en&as_sdt=0,14
| 3 | 2,022 |
Wavelet Feature Maps Compression for Image-to-Image CNNs
| 2 |
neurips
| 4 | 0 |
2023-06-16 22:58:58.192000
|
https://github.com/BGUCompSci/WaveletCompressedConvolution
| 27 |
Wavelet Feature Maps Compression for Image-to-Image CNNs
|
https://scholar.google.com/scholar?cluster=14881442533144434153&hl=en&as_sdt=0,5
| 3 | 2,022 |
Model-Based Imitation Learning for Urban Driving
| 10 |
neurips
| 17 | 5 |
2023-06-16 22:58:58.449000
|
https://github.com/wayveai/mile
| 183 |
Model-based imitation learning for urban driving
|
https://scholar.google.com/scholar?cluster=4528068241168957372&hl=en&as_sdt=0,22
| 4 | 2,022 |
Online Training Through Time for Spiking Neural Networks
| 5 |
neurips
| 3 | 1 |
2023-06-16 22:58:58.661000
|
https://github.com/pkuxmq/ottt-snn
| 19 |
Online Training Through Time for Spiking Neural Networks
|
https://scholar.google.com/scholar?cluster=4277557500374843996&hl=en&as_sdt=0,5
| 1 | 2,022 |
SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:58:58.872000
|
https://github.com/anttwo/scone
| 17 |
SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration
|
https://scholar.google.com/scholar?cluster=4160345323864835005&hl=en&as_sdt=0,1
| 2 | 2,022 |
WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
| 14 |
neurips
| 16 | 1 |
2023-06-16 22:58:59.084000
|
https://github.com/princeton-nlp/WebShop
| 106 |
Webshop: Towards scalable real-world web interaction with grounded language agents
|
https://scholar.google.com/scholar?cluster=11660577557490092707&hl=en&as_sdt=0,5
| 9 | 2,022 |
Boosting Out-of-distribution Detection with Typical Features
| 5 |
neurips
| 34 | 2 |
2023-06-16 22:58:59.296000
|
https://github.com/alibaba/easyrobust
| 236 |
Boosting Out-of-distribution Detection with Typical Features
|
https://scholar.google.com/scholar?cluster=8201302688725034478&hl=en&as_sdt=0,26
| 8 | 2,022 |
Invariant and Transportable Representations for Anti-Causal Domain Shifts
| 5 |
neurips
| 0 | 0 |
2023-06-16 22:58:59.507000
|
https://github.com/ybjiaang/actir
| 10 |
Invariant and Transportable Representations for Anti-Causal Domain Shifts
|
https://scholar.google.com/scholar?cluster=6490723146131513979&hl=en&as_sdt=0,5
| 1 | 2,022 |
Bayesian inference via sparse Hamiltonian flows
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:58:59.722000
|
https://github.com/naitongchen/sparse-hamiltonian-flows
| 0 |
Bayesian inference via sparse Hamiltonian flows
|
https://scholar.google.com/scholar?cluster=11938722905840215074&hl=en&as_sdt=0,5
| 1 | 2,022 |
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:58:59.934000
|
https://github.com/poppinace/sapa
| 25 |
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
|
https://scholar.google.com/scholar?cluster=14123536763818309865&hl=en&as_sdt=0,5
| 2 | 2,022 |
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:59:00.146000
|
https://github.com/gatech-eic/s3-router
| 12 |
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
|
https://scholar.google.com/scholar?cluster=15574684827691207630&hl=en&as_sdt=0,5
| 3 | 2,022 |
Diversity vs. Recognizability: Human-like generalization in one-shot generative models
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:00.357000
|
https://github.com/serre-lab/diversity_vs_recognizability
| 4 |
Diversity vs. Recognizability: Human-like generalization in one-shot generative models
|
https://scholar.google.com/scholar?cluster=14721950743942422651&hl=en&as_sdt=0,5
| 16 | 2,022 |
Laplacian Autoencoders for Learning Stochastic Representations
| 3 |
neurips
| 7 | 0 |
2023-06-16 22:59:00.568000
|
https://github.com/frederikwarburg/laplaceae
| 26 |
Laplacian autoencoders for learning stochastic representations
|
https://scholar.google.com/scholar?cluster=11700677382101407411&hl=en&as_sdt=0,44
| 3 | 2,022 |
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
| 3 |
neurips
| 2 | 2 |
2023-06-16 22:59:00.779000
|
https://github.com/kikimormay/fsl-tcbr
| 8 |
Alleviating the sample selection bias in few-shot learning by removing projection to the centroid
|
https://scholar.google.com/scholar?cluster=13443086589553855773&hl=en&as_sdt=0,5
| 3 | 2,022 |
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
| 2 |
neurips
| 2 | 0 |
2023-06-16 22:59:00.991000
|
https://github.com/xtra-computing/fedsim
| 14 |
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
|
https://scholar.google.com/scholar?cluster=14897829456700189277&hl=en&as_sdt=0,22
| 2 | 2,022 |
Cooperative Distribution Alignment via JSD Upper Bound
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:59:01.203000
|
https://github.com/inouye-lab/alignment-upper-bound
| 4 |
Cooperative Distribution Alignment via JSD Upper Bound
|
https://scholar.google.com/scholar?cluster=10366168387134029153&hl=en&as_sdt=0,5
| 0 | 2,022 |
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:01.432000
|
https://github.com/neerajwagh/evaluating-eeg-representations
| 12 |
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts
|
https://scholar.google.com/scholar?cluster=6355936438645874712&hl=en&as_sdt=0,14
| 2 | 2,022 |
Hierarchical Graph Transformer with Adaptive Node Sampling
| 10 |
neurips
| 4 | 0 |
2023-06-16 22:59:01.644000
|
https://github.com/zaixizhang/ans-gt
| 28 |
Hierarchical Graph Transformer with Adaptive Node Sampling
|
https://scholar.google.com/scholar?cluster=3439990593504526316&hl=en&as_sdt=0,33
| 3 | 2,022 |
Learning Options via Compression
| 1 |
neurips
| 3 | 1 |
2023-06-16 22:59:01.855000
|
https://github.com/yidingjiang/love
| 16 |
Learning Options via Compression
|
https://scholar.google.com/scholar?cluster=662325377379730259&hl=en&as_sdt=0,5
| 2 | 2,022 |
Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data
| 7 |
neurips
| 7 | 2 |
2023-06-16 22:59:02.067000
|
https://github.com/athms/learning-from-brains
| 32 |
Self-supervised learning of brain dynamics from broad neuroimaging data
|
https://scholar.google.com/scholar?cluster=16840620641875869687&hl=en&as_sdt=0,47
| 3 | 2,022 |
Characterization of Excess Risk for Locally Strongly Convex Population Risk
| 1 |
neurips
| 2,047 | 105 |
2023-06-16 22:59:02.278000
|
https://github.com/kuangliu/pytorch-cifar
| 5,349 |
Characterization of Excess Risk for Locally Strongly Convex Population Risk
|
https://scholar.google.com/scholar?cluster=17104238636879599315&hl=en&as_sdt=0,5
| 81 | 2,022 |
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
| 3 |
neurips
| 1 | 0 |
2023-06-16 22:59:02.491000
|
https://github.com/zhoulijia/moreau-envelope
| 0 |
A non-asymptotic moreau envelope theory for high-dimensional generalized linear models
|
https://scholar.google.com/scholar?cluster=2093430636599193484&hl=en&as_sdt=0,47
| 1 | 2,022 |
Towards Efficient 3D Object Detection with Knowledge Distillation
| 11 |
neurips
| 10 | 2 |
2023-06-16 22:59:02.702000
|
https://github.com/cvmi-lab/sparsekd
| 83 |
Towards efficient 3d object detection with knowledge distillation
|
https://scholar.google.com/scholar?cluster=4669452180689530857&hl=en&as_sdt=0,44
| 4 | 2,022 |
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
| 34 |
neurips
| 47 | 29 |
2023-06-16 22:59:02.913000
|
https://github.com/salesforce/coderl
| 376 |
Coderl: Mastering code generation through pretrained models and deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=16132461608551265231&hl=en&as_sdt=0,5
| 18 | 2,022 |
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization
| 28 |
neurips
| 21 | 1 |
2023-06-16 22:59:03.124000
|
https://github.com/wenhao-gao/mol_opt
| 103 |
Sample efficiency matters: a benchmark for practical molecular optimization
|
https://scholar.google.com/scholar?cluster=5930505572386998572&hl=en&as_sdt=0,47
| 7 | 2,022 |
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:59:03.336000
|
https://github.com/liu-jc/mgnni
| 6 |
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
|
https://scholar.google.com/scholar?cluster=16464433978431539899&hl=en&as_sdt=0,5
| 2 | 2,022 |
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:59:03.548000
|
https://github.com/ronmckay/uqgan
| 7 |
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs
|
https://scholar.google.com/scholar?cluster=13580912183352857731&hl=en&as_sdt=0,33
| 1 | 2,022 |
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
| 4 |
neurips
| 0 | 1 |
2023-06-16 22:59:03.759000
|
https://github.com/zhanghaojie077/dps
| 8 |
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
|
https://scholar.google.com/scholar?cluster=204679375623303358&hl=en&as_sdt=0,5
| 2 | 2,022 |
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP
| 13 |
neurips
| 0 | 0 |
2023-06-16 22:59:03.971000
|
https://github.com/mlfoundations/clip_quality_not_quantity
| 14 |
Quality not quantity: On the interaction between dataset design and robustness of clip
|
https://scholar.google.com/scholar?cluster=1636514590207209786&hl=en&as_sdt=0,31
| 4 | 2,022 |
PaCo: Parameter-Compositional Multi-task Reinforcement Learning
| 2 |
neurips
| 1 | 0 |
2023-06-16 22:59:04.182000
|
https://github.com/ttotmoon/paco-mtrl
| 11 |
PaCo: Parameter-Compositional Multi-Task Reinforcement Learning
|
https://scholar.google.com/scholar?cluster=9186813213951917156&hl=en&as_sdt=0,5
| 5 | 2,022 |
A Contrastive Framework for Neural Text Generation
| 31 |
neurips
| 36 | 7 |
2023-06-16 22:59:04.394000
|
https://github.com/yxuansu/simctg
| 407 |
A contrastive framework for neural text generation
|
https://scholar.google.com/scholar?cluster=6130101757033194122&hl=en&as_sdt=0,33
| 9 | 2,022 |
Exploring the Latent Space of Autoencoders with Interventional Assays
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:59:04.606000
|
https://github.com/felixludos/latent-responses
| 4 |
Exploring the Latent Space of Autoencoders with Interventional Assays
|
https://scholar.google.com/scholar?cluster=11218726005566161204&hl=en&as_sdt=0,5
| 2 | 2,022 |
Fair Wrapping for Black-box Predictions
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:59:04.818000
|
https://github.com/alexandersoen/alpha-tree-fair-wrappers
| 0 |
Fair Wrapping for Black-box Predictions
|
https://scholar.google.com/scholar?cluster=17408270699563829594&hl=en&as_sdt=0,47
| 2 | 2,022 |
Meta-Learning Dynamics Forecasting Using Task Inference
| 12 |
neurips
| 3 | 0 |
2023-06-16 22:59:05.030000
|
https://github.com/rose-stl-lab/dynamic-adaptation-network
| 19 |
Meta-learning dynamics forecasting using task inference
|
https://scholar.google.com/scholar?cluster=1635699152041148916&hl=en&as_sdt=0,33
| 3 | 2,022 |
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement
| 7 |
neurips
| 0 | 0 |
2023-06-16 22:59:05.260000
|
https://github.com/palm-ml/smile
| 7 |
One positive label is sufficient: Single-positive multi-label learning with label enhancement
|
https://scholar.google.com/scholar?cluster=17678484826346617889&hl=en&as_sdt=0,5
| 1 | 2,022 |
This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish
| 3 |
neurips
| 1 | 2 |
2023-06-16 22:59:05.476000
|
https://github.com/clarin-pl/lepiszcze
| 10 |
This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish
|
https://scholar.google.com/scholar?cluster=4248229270344393819&hl=en&as_sdt=0,33
| 5 | 2,022 |
Insights into Pre-training via Simpler Synthetic Tasks
| 6 |
neurips
| 3 | 3 |
2023-06-16 22:59:05.687000
|
https://github.com/felixzli/synthetic_pretraining
| 35 |
Insights into pre-training via simpler synthetic tasks
|
https://scholar.google.com/scholar?cluster=16551759409379033165&hl=en&as_sdt=0,1
| 3 | 2,022 |
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities
| 12 |
neurips
| 0 | 0 |
2023-06-16 22:59:05.899000
|
https://github.com/eduardgorbunov/potentials_and_last_iter_convergence_for_vips
| 1 |
Last-iterate convergence of optimistic gradient method for monotone variational inequalities
|
https://scholar.google.com/scholar?cluster=15310707348220215972&hl=en&as_sdt=0,5
| 3 | 2,022 |
3DILG: Irregular Latent Grids for 3D Generative Modeling
| 12 |
neurips
| 3 | 7 |
2023-06-16 22:59:06.110000
|
https://github.com/1zb/3DILG
| 73 |
3DILG: Irregular latent grids for 3d generative modeling
|
https://scholar.google.com/scholar?cluster=9112340556841265802&hl=en&as_sdt=0,10
| 6 | 2,022 |
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
| 2 |
neurips
| 4 | 1 |
2023-06-16 22:59:06.322000
|
https://github.com/ylab-open/mets-cov
| 29 |
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
|
https://scholar.google.com/scholar?cluster=14166404945235521589&hl=en&as_sdt=0,33
| 1 | 2,022 |
Continual Learning In Environments With Polynomial Mixing Times
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:59:06.534000
|
https://github.com/sharathraparthy/polynomial-mixing-times
| 1 |
Continual learning in environments with polynomial mixing times
|
https://scholar.google.com/scholar?cluster=148193105914487593&hl=en&as_sdt=0,20
| 2 | 2,022 |
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts
| 6 |
neurips
| 1 | 1 |
2023-06-16 22:59:06.745000
|
https://github.com/spcl/ens10
| 12 |
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast
|
https://scholar.google.com/scholar?cluster=1680500847408838511&hl=en&as_sdt=0,14
| 7 | 2,022 |
Unsupervised Cross-Task Generalization via Retrieval Augmentation
| 16 |
neurips
| 1 | 1 |
2023-06-16 22:59:06.961000
|
https://github.com/INK-USC/ReCross
| 20 |
Unsupervised cross-task generalization via retrieval augmentation
|
https://scholar.google.com/scholar?cluster=17714217089004895750&hl=en&as_sdt=0,19
| 2 | 2,022 |
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:59:07.172000
|
https://github.com/kai-wen-yang/lpa3
| 5 |
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
|
https://scholar.google.com/scholar?cluster=13625284013490795521&hl=en&as_sdt=0,44
| 1 | 2,022 |
Coordinate Linear Variance Reduction for Generalized Linear Programming
| 5 |
neurips
| 0 | 0 |
2023-06-16 22:59:07.384000
|
https://github.com/ericlincc/efficient-glp
| 0 |
Coordinate linear variance reduction for generalized linear programming
|
https://scholar.google.com/scholar?cluster=4608782560643046588&hl=en&as_sdt=0,23
| 1 | 2,022 |
Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models
| 3 |
neurips
| 15 | 2 |
2023-06-16 22:59:07.598000
|
https://github.com/ckczzj/pdae
| 184 |
Unsupervised representation learning from pre-trained diffusion probabilistic models
|
https://scholar.google.com/scholar?cluster=10369587863928600247&hl=en&as_sdt=0,5
| 11 | 2,022 |
To update or not to update? Neurons at equilibrium in deep models
| 1 |
neurips
| 2 | 0 |
2023-06-16 22:59:07.810000
|
https://github.com/eidoslab/neq
| 1 |
To update or not to update? Neurons at equilibrium in deep models
|
https://scholar.google.com/scholar?cluster=16721968109836533918&hl=en&as_sdt=0,10
| 2 | 2,022 |
Large Language Models are Zero-Shot Reasoners
| 361 |
neurips
| 38 | 3 |
2023-06-16 22:59:08.029000
|
https://github.com/kojima-takeshi188/zero_shot_cot
| 218 |
Large language models are zero-shot reasoners
|
https://scholar.google.com/scholar?cluster=3629340874362196998&hl=en&as_sdt=0,5
| 2 | 2,022 |
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
| 5 |
neurips
| 3 | 0 |
2023-06-16 22:59:08.252000
|
https://github.com/prs-eth/film-ensemble
| 20 |
Film-ensemble: Probabilistic deep learning via feature-wise linear modulation
|
https://scholar.google.com/scholar?cluster=13764162934319607563&hl=en&as_sdt=0,31
| 5 | 2,022 |
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
| 6 |
neurips
| 1 | 0 |
2023-06-16 22:59:08.464000
|
https://github.com/n3il666/meta-dmoe
| 18 |
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
|
https://scholar.google.com/scholar?cluster=18362067030660551332&hl=en&as_sdt=0,11
| 2 | 2,022 |
Revisiting Neural Scaling Laws in Language and Vision
| 13 |
neurips
| 7,321 | 1,026 |
2023-06-16 22:59:08.675000
|
https://github.com/google-research/google-research
| 29,788 |
Revisiting neural scaling laws in language and vision
|
https://scholar.google.com/scholar?cluster=13068882041594031695&hl=en&as_sdt=0,5
| 727 | 2,022 |
Long Range Graph Benchmark
| 22 |
neurips
| 10 | 4 |
2023-06-16 22:59:08.887000
|
https://github.com/vijaydwivedi75/lrgb
| 91 |
Long range graph benchmark
|
https://scholar.google.com/scholar?cluster=15245934587823122580&hl=en&as_sdt=0,48
| 2 | 2,022 |
Active Learning Through a Covering Lens
| 7 |
neurips
| 4 | 0 |
2023-06-16 22:59:09.098000
|
https://github.com/avihu111/typiclust
| 44 |
Active learning through a covering lens
|
https://scholar.google.com/scholar?cluster=6727917146532281789&hl=en&as_sdt=0,44
| 4 | 2,022 |
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
| 7 |
neurips
| 3 | 0 |
2023-06-16 22:59:09.310000
|
https://github.com/bat-sheva/conformal-learning
| 12 |
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning
|
https://scholar.google.com/scholar?cluster=9463717142610823747&hl=en&as_sdt=0,9
| 1 | 2,022 |
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
| 10 |
neurips
| 72 | 44 |
2023-06-16 22:59:09.523000
|
https://github.com/sail-sg/envpool
| 858 |
Envpool: A highly parallel reinforcement learning environment execution engine
|
https://scholar.google.com/scholar?cluster=16477244974274952547&hl=en&as_sdt=0,33
| 20 | 2,022 |
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models
| 4 |
neurips
| 5 | 0 |
2023-06-16 22:59:09.736000
|
https://github.com/chenwu98/generative-visual-prompt
| 108 |
Generative visual prompt: Unifying distributional control of pre-trained generative models
|
https://scholar.google.com/scholar?cluster=818769065864571776&hl=en&as_sdt=0,37
| 1 | 2,022 |
FNeVR: Neural Volume Rendering for Face Animation
| 5 |
neurips
| 2 | 2 |
2023-06-16 22:59:09.947000
|
https://github.com/zengbohan0217/FNeVR
| 24 |
FNeVR: Neural Volume Rendering for Face Animation
|
https://scholar.google.com/scholar?cluster=15199852463833222528&hl=en&as_sdt=0,5
| 2 | 2,022 |
Domain Adaptation under Open Set Label Shift
| 8 |
neurips
| 2 | 1 |
2023-06-16 22:59:10.158000
|
https://github.com/acmi-lab/open-set-label-shift
| 22 |
Domain adaptation under open set label shift
|
https://scholar.google.com/scholar?cluster=16553393786888596205&hl=en&as_sdt=0,5
| 2 | 2,022 |
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
| 5 |
neurips
| 0 | 1 |
2023-06-16 22:59:10.371000
|
https://github.com/umd-huang-lab/wocar-rl
| 10 |
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
|
https://scholar.google.com/scholar?cluster=12094552498707389158&hl=en&as_sdt=0,48
| 3 | 2,022 |
Stochastic Multiple Target Sampling Gradient Descent
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:59:10.583000
|
https://github.com/VietHoang1512/MT-SGD
| 10 |
Stochastic Multiple Target Sampling Gradient Descent
|
https://scholar.google.com/scholar?cluster=10047163033454446473&hl=en&as_sdt=0,43
| 1 | 2,022 |
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment
| 2 |
neurips
| 0 | 1 |
2023-06-16 22:59:10.794000
|
https://github.com/chr26195/caseq
| 17 |
Towards out-of-distribution sequential event prediction: A causal treatment
|
https://scholar.google.com/scholar?cluster=17121151690728293112&hl=en&as_sdt=0,44
| 1 | 2,022 |
Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:59:11.006000
|
https://github.com/yimengmin/geometricscatteringmaximalclique
| 3 |
Can Hybrid Geometric Scattering Networks Help Solve the Maximal Clique Problem?
|
https://scholar.google.com/scholar?cluster=7138348032927670715&hl=en&as_sdt=0,5
| 2 | 2,022 |
Physically-Based Face Rendering for NIR-VIS Face Recognition
| 1 |
neurips
| 4,432 | 910 |
2023-06-16 22:59:11.218000
|
https://github.com/deepinsight/insightface
| 16,032 |
Physically-Based Face Rendering for NIR-VIS Face Recognition
|
https://scholar.google.com/scholar?cluster=6409917825922546177&hl=en&as_sdt=0,43
| 479 | 2,022 |
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
| 3 |
neurips
| 2 | 0 |
2023-06-16 22:59:11.439000
|
https://github.com/val-iisc/saddle-longtail
| 11 |
Escaping saddle points for effective generalization on class-imbalanced data
|
https://scholar.google.com/scholar?cluster=12550749956843640624&hl=en&as_sdt=0,5
| 13 | 2,022 |
A2: Efficient Automated Attacker for Boosting Adversarial Training
| 4 |
neurips
| 1 | 0 |
2023-06-16 22:59:11.650000
|
https://github.com/alipay/A2-efficient-automated-attacker-for-boosting-adversarial-training
| 4 |
A2: Efficient automated attacker for boosting adversarial training
|
https://scholar.google.com/scholar?cluster=13326470772747253603&hl=en&as_sdt=0,5
| 2 | 2,022 |
Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising
| 23 |
neurips
| 20 | 12 |
2023-06-16 22:59:11.862000
|
https://github.com/NVlabs/nvdiffrecmc
| 249 |
Shape, light & material decomposition from images using monte carlo rendering and denoising
|
https://scholar.google.com/scholar?cluster=16786831417304918950&hl=en&as_sdt=0,5
| 9 | 2,022 |
Reconstructing Training Data From Trained Neural Networks
| 21 |
neurips
| 13 | 0 |
2023-06-16 22:59:12.074000
|
https://github.com/nivha/dataset_reconstruction
| 33 |
Reconstructing training data from trained neural networks
|
https://scholar.google.com/scholar?cluster=4430126406980448960&hl=en&as_sdt=0,43
| 5 | 2,022 |
Behavior Transformers: Cloning $k$ modes with one stone
| 28 |
neurips
| 10 | 1 |
2023-06-16 22:59:12.285000
|
https://github.com/notmahi/bet
| 59 |
Behavior Transformers: Cloning modes with one stone
|
https://scholar.google.com/scholar?cluster=6874272481284678006&hl=en&as_sdt=0,5
| 7 | 2,022 |
Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement
| 6 |
neurips
| 158 | 4 |
2023-06-16 22:59:12.497000
|
https://github.com/paddlepaddle/paddlespatial
| 278 |
Generative time series forecasting with diffusion, denoise, and disentanglement
|
https://scholar.google.com/scholar?cluster=10694050975663316103&hl=en&as_sdt=0,5
| 10 | 2,022 |
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
| 14 |
neurips
| 4 | 0 |
2023-06-16 22:59:12.708000
|
https://github.com/pralab/IndicatorsOfAttackFailure
| 16 |
Indicators of attack failure: Debugging and improving optimization of adversarial examples
|
https://scholar.google.com/scholar?cluster=6397860680603996993&hl=en&as_sdt=0,40
| 4 | 2,022 |
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:59:12.920000
|
https://github.com/helena-yuhan-liu/biolhessrnn
| 2 |
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
|
https://scholar.google.com/scholar?cluster=6396873608730348265&hl=en&as_sdt=0,11
| 1 | 2,022 |
VITA: Video Instance Segmentation via Object Token Association
| 21 |
neurips
| 11 | 4 |
2023-06-16 22:59:13.131000
|
https://github.com/sukjunhwang/vita
| 79 |
Vita: Video instance segmentation via object token association
|
https://scholar.google.com/scholar?cluster=14992032927196950732&hl=en&as_sdt=0,47
| 6 | 2,022 |
Truncated proposals for scalable and hassle-free simulation-based inference
| 7 |
neurips
| 2 | 0 |
2023-06-16 22:59:13.343000
|
https://github.com/mackelab/tsnpe_neurips
| 2 |
Truncated proposals for scalable and hassle-free simulation-based inference
|
https://scholar.google.com/scholar?cluster=16561248332012832367&hl=en&as_sdt=0,23
| 2 | 2,022 |
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
| 95 |
neurips
| 84 | 17 |
2023-06-16 22:59:13.556000
|
https://github.com/guochengqian/pointnext
| 534 |
Pointnext: Revisiting pointnet++ with improved training and scaling strategies
|
https://scholar.google.com/scholar?cluster=14072888861532659606&hl=en&as_sdt=0,19
| 12 | 2,022 |
Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:59:13.767000
|
https://github.com/yellowshippo/penn-neurips2022
| 19 |
Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions
|
https://scholar.google.com/scholar?cluster=17090530239776984300&hl=en&as_sdt=0,5
| 2 | 2,022 |
Mismatched No More: Joint Model-Policy Optimization for Model-Based RL
| 11 |
neurips
| 1 | 0 |
2023-06-16 22:59:13.979000
|
https://github.com/ben-eysenbach/mnm
| 18 |
Mismatched no more: Joint model-policy optimization for model-based rl
|
https://scholar.google.com/scholar?cluster=5999896080884397819&hl=en&as_sdt=0,23
| 2 | 2,022 |
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks
| 13 |
neurips
| 0 | 0 |
2023-06-16 22:59:14.191000
|
https://github.com/rodsveiga/phdiag_sgd
| 3 |
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks
|
https://scholar.google.com/scholar?cluster=5970904952393293482&hl=en&as_sdt=0,10
| 2 | 2,022 |
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency
| 3 |
neurips
| 2 | 0 |
2023-06-16 22:59:14.403000
|
https://github.com/virajprabhu/pacmac
| 18 |
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency
|
https://scholar.google.com/scholar?cluster=13259793333316816742&hl=en&as_sdt=0,22
| 3 | 2,022 |
Sample-Then-Optimize Batch Neural Thompson Sampling
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:59:14.614000
|
https://github.com/daizhongxiang/sto-bnts
| 5 |
Sample-then-optimize batch neural thompson sampling
|
https://scholar.google.com/scholar?cluster=866282396542393930&hl=en&as_sdt=0,3
| 1 | 2,022 |
Efficient and Stable Fully Dynamic Facility Location
| 1 |
neurips
| 7,321 | 1,026 |
2023-06-16 22:59:14.825000
|
https://github.com/google-research/google-research
| 29,788 |
Efficient and Stable Fully Dynamic Facility Location
|
https://scholar.google.com/scholar?cluster=12708856198271717764&hl=en&as_sdt=0,5
| 727 | 2,022 |
Sharpness-Aware Training for Free
| 29 |
neurips
| 1 | 0 |
2023-06-16 22:59:15.036000
|
https://github.com/angusdujw/saf
| 9 |
Sharpness-aware training for free
|
https://scholar.google.com/scholar?cluster=5747357425500146304&hl=en&as_sdt=0,5
| 2 | 2,022 |
Inception Transformer
| 111 |
neurips
| 16 | 7 |
2023-06-16 22:59:15.248000
|
https://github.com/sail-sg/iformer
| 192 |
Inception transformer
|
https://scholar.google.com/scholar?cluster=610621467807251926&hl=en&as_sdt=0,44
| 16 | 2,022 |
Mesoscopic modeling of hidden spiking neurons
| 2 |
neurips
| 1 | 0 |
2023-06-16 22:59:15.470000
|
https://github.com/epfl-lcn/neulvm
| 0 |
Mesoscopic modeling of hidden spiking neurons
|
https://scholar.google.com/scholar?cluster=7842440954111495341&hl=en&as_sdt=0,5
| 0 | 2,022 |
SageMix: Saliency-Guided Mixup for Point Clouds
| 6 |
neurips
| 2 | 2 |
2023-06-16 22:59:15.681000
|
https://github.com/mlvlab/SageMix
| 19 |
Sagemix: Saliency-guided mixup for point clouds
|
https://scholar.google.com/scholar?cluster=1906739869004818181&hl=en&as_sdt=0,14
| 5 | 2,022 |
Denoising Diffusion Restoration Models
| 136 |
neurips
| 38 | 15 |
2023-06-16 22:59:15.892000
|
https://github.com/bahjat-kawar/ddrm
| 375 |
Denoising diffusion restoration models
|
https://scholar.google.com/scholar?cluster=9684379988322593312&hl=en&as_sdt=0,3
| 6 | 2,022 |
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
| 0 |
neurips
| 3 | 0 |
2023-06-16 22:59:16.104000
|
https://github.com/RoyalSkye/ATCL
| 12 |
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
|
https://scholar.google.com/scholar?cluster=7990357189849554296&hl=en&as_sdt=0,5
| 2 | 2,022 |
BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis
| 10 |
neurips
| 133 | 24 |
2023-06-16 22:59:16.315000
|
https://github.com/microsoft/NeuralSpeech
| 1,007 |
Binauralgrad: A two-stage conditional diffusion probabilistic model for binaural audio synthesis
|
https://scholar.google.com/scholar?cluster=3061602532633994428&hl=en&as_sdt=0,36
| 30 | 2,022 |
ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild
| 0 |
neurips
| 2 | 1 |
2023-06-16 22:59:16.528000
|
https://github.com/tudelft-spc-lab/conflab
| 0 |
ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild
|
https://scholar.google.com/scholar?cluster=10626615625989793283&hl=en&as_sdt=0,43
| 2 | 2,022 |
Predictive Querying for Autoregressive Neural Sequence Models
| 2 |
neurips
| 2 | 0 |
2023-06-16 22:59:16.739000
|
https://github.com/ajboyd2/prob_seq_queries
| 0 |
Predictive querying for autoregressive neural sequence models
|
https://scholar.google.com/scholar?cluster=9455015108688236225&hl=en&as_sdt=0,26
| 1 | 2,022 |
Learning State-Aware Visual Representations from Audible Interactions
| 5 |
neurips
| 2 | 5 |
2023-06-16 22:59:16.951000
|
https://github.com/HimangiM/RepLAI
| 8 |
Learning state-aware visual representations from audible interactions
|
https://scholar.google.com/scholar?cluster=10557769016177465822&hl=en&as_sdt=0,15
| 1 | 2,022 |
DISCO: Adversarial Defense with Local Implicit Functions
| 4 |
neurips
| 1 | 2 |
2023-06-16 22:59:17.162000
|
https://github.com/chihhuiho/disco
| 5 |
DISCO: Adversarial Defense with Local Implicit Functions
|
https://scholar.google.com/scholar?cluster=14390816602060782578&hl=en&as_sdt=0,43
| 1 | 2,022 |
RORL: Robust Offline Reinforcement Learning via Conservative Smoothing
| 14 |
neurips
| 2 | 0 |
2023-06-16 22:59:17.374000
|
https://github.com/yangrui2015/rorl
| 9 |
Rorl: Robust offline reinforcement learning via conservative smoothing
|
https://scholar.google.com/scholar?cluster=12160465194138286098&hl=en&as_sdt=0,5
| 2 | 2,022 |
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:59:17.586000
|
https://github.com/ha0ransun/lbp_scale
| 6 |
Optimal scaling for locally balanced proposals in discrete spaces
|
https://scholar.google.com/scholar?cluster=9220497344062023085&hl=en&as_sdt=0,31
| 1 | 2,022 |
Zero-Shot 3D Drug Design by Sketching and Generating
| 2 |
neurips
| 8 | 3 |
2023-06-16 22:59:17.799000
|
https://github.com/longlongman/DESERT
| 17 |
Zero-Shot 3D Drug Design by Sketching and Generating
|
https://scholar.google.com/scholar?cluster=17297896301377574979&hl=en&as_sdt=0,33
| 2 | 2,022 |
Optimal Comparator Adaptive Online Learning with Switching Cost
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:59:18.010000
|
https://github.com/zhiyuzz/neurips2022-adaptive-switching
| 0 |
Optimal Comparator Adaptive Online Learning with Switching Cost
|
https://scholar.google.com/scholar?cluster=14092705801881163803&hl=en&as_sdt=0,5
| 1 | 2,022 |
Neur2SP: Neural Two-Stage Stochastic Programming
| 7 |
neurips
| 3 | 0 |
2023-06-16 22:59:18.226000
|
https://github.com/khalil-research/neur2sp
| 14 |
Neur2sp: Neural two-stage stochastic programming
|
https://scholar.google.com/scholar?cluster=297850610846238239&hl=en&as_sdt=0,20
| 2 | 2,022 |
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:18.455000
|
https://github.com/puetpaper/PUExtraTrees
| 9 |
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
|
https://scholar.google.com/scholar?cluster=749148422214785418&hl=en&as_sdt=0,26
| 1 | 2,022 |
A Fast Post-Training Pruning Framework for Transformers
| 11 |
neurips
| 13 | 6 |
2023-06-16 22:59:18.667000
|
https://github.com/WoosukKwon/retraining-free-pruning
| 98 |
A Fast Post-Training Pruning Framework for Transformers
|
https://scholar.google.com/scholar?cluster=8295752471626103240&hl=en&as_sdt=0,33
| 5 | 2,022 |
Interventions, Where and How? Experimental Design for Causal Models at Scale
| 9 |
neurips
| 4 | 0 |
2023-06-16 22:59:18.881000
|
https://github.com/yannadani/cbed
| 15 |
Interventions, where and how? experimental design for causal models at scale
|
https://scholar.google.com/scholar?cluster=2079194149700665764&hl=en&as_sdt=0,5
| 1 | 2,022 |
Single-phase deep learning in cortico-cortical networks
| 8 |
neurips
| 0 | 0 |
2023-06-16 22:59:19.092000
|
https://github.com/neuralml/burstccn
| 6 |
Single-phase deep learning in cortico-cortical networks
|
https://scholar.google.com/scholar?cluster=17225201709003399719&hl=en&as_sdt=0,5
| 1 | 2,022 |
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