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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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