title
stringlengths 8
155
| citations_google_scholar
int64 0
28.9k
| conference
stringclasses 5
values | forks
int64 0
46.3k
| issues
int64 0
12.2k
| lastModified
stringlengths 19
26
| repo_url
stringlengths 26
130
| stars
int64 0
75.9k
| title_google_scholar
stringlengths 8
155
| url_google_scholar
stringlengths 75
206
| watchers
int64 0
2.77k
| year
int64 2.02k
2.02k
|
|---|---|---|---|---|---|---|---|---|---|---|---|
Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP | 4 | neurips | 3 | 0 | 2023-06-16 16:06:33.018000 | https://github.com/jsrozner/decrypt | 8 | Decrypting cryptic crosswords: Semantically complex wordplay puzzles as a target for nlp | https://scholar.google.com/scholar?cluster=3859471810944880726&hl=en&as_sdt=0,47 | 3 | 2,021 |
Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing | 26 | neurips | 1 | 0 | 2023-06-16 16:06:33.217000 | https://github.com/GenjiB/CM-Co-Occurrence-AVVP | 3 | Exploring cross-video and cross-modality signals for weakly-supervised audio-visual video parsing | https://scholar.google.com/scholar?cluster=12267728961291796610&hl=en&as_sdt=0,44 | 1 | 2,021 |
Dual Parameterization of Sparse Variational Gaussian Processes | 15 | neurips | 1 | 0 | 2023-06-16 16:06:33.417000 | https://github.com/AaltoML/t-SVGP | 7 | Dual parameterization of sparse variational Gaussian processes | https://scholar.google.com/scholar?cluster=12813330382195057867&hl=en&as_sdt=0,11 | 1 | 2,021 |
Hierarchical Skills for Efficient Exploration | 16 | neurips | 5 | 1 | 2023-06-16 16:06:33.616000 | https://github.com/facebookresearch/hsd3 | 44 | Hierarchical skills for efficient exploration | https://scholar.google.com/scholar?cluster=15461268367576192426&hl=en&as_sdt=0,11 | 8 | 2,021 |
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models | 5 | neurips | 2 | 0 | 2023-06-16 16:06:33.816000 | https://github.com/sisl/evsoftmax | 9 | Evidential softmax for sparse multimodal distributions in deep generative models | https://scholar.google.com/scholar?cluster=11285213067852338942&hl=en&as_sdt=0,15 | 9 | 2,021 |
DeepGEM: Generalized Expectation-Maximization for Blind Inversion | 7 | neurips | 2 | 0 | 2023-06-16 16:06:34.020000 | https://github.com/angelafgao/DeepGEM | 6 | DeepGEM: Generalized expectation-maximization for blind inversion | https://scholar.google.com/scholar?cluster=14209454194474907854&hl=en&as_sdt=0,9 | 3 | 2,021 |
Learning to Generate Visual Questions with Noisy Supervision | 6 | neurips | 0 | 0 | 2023-06-16 16:06:34.222000 | https://github.com/alanswift/dh-gan | 0 | Learning to generate visual questions with noisy supervision | https://scholar.google.com/scholar?cluster=5233322367909320124&hl=en&as_sdt=0,36 | 2 | 2,021 |
Numerical Composition of Differential Privacy | 58 | neurips | 9 | 4 | 2023-06-16 16:06:34.422000 | https://github.com/microsoft/prv_accountant | 44 | Numerical composition of differential privacy | https://scholar.google.com/scholar?cluster=2912362151232664509&hl=en&as_sdt=0,5 | 8 | 2,021 |
Hyperparameter Tuning is All You Need for LISTA | 8 | neurips | 6 | 1 | 2023-06-16 16:06:34.630000 | https://github.com/vita-group/hyperlista | 13 | Hyperparameter tuning is all you need for lista | https://scholar.google.com/scholar?cluster=4373381653773823100&hl=en&as_sdt=0,5 | 6 | 2,021 |
Foundations of Symbolic Languages for Model Interpretability | 11 | neurips | 0 | 0 | 2023-06-16 16:06:34.831000 | https://github.com/angryseal/foil-prototype | 1 | Foundations of symbolic languages for model interpretability | https://scholar.google.com/scholar?cluster=18188458823960639099&hl=en&as_sdt=0,44 | 3 | 2,021 |
Impression learning: Online representation learning with synaptic plasticity | 3 | neurips | 0 | 0 | 2023-06-16 16:06:35.031000 | https://github.com/colinbredenberg/impression-learning-camera-ready | 2 | Impression learning: Online representation learning with synaptic plasticity | https://scholar.google.com/scholar?cluster=18236139388730215945&hl=en&as_sdt=0,33 | 3 | 2,021 |
How Well do Feature Visualizations Support Causal Understanding of CNN Activations? | 10 | neurips | 2 | 0 | 2023-06-16 16:06:35.231000 | https://github.com/brendel-group/causal-understanding-via-visualizations | 7 | How Well do Feature Visualizations Support Causal Understanding of CNN Activations? | https://scholar.google.com/scholar?cluster=18425728687494143861&hl=en&as_sdt=0,44 | 3 | 2,021 |
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems | 2 | neurips | 1 | 0 | 2023-06-16 16:06:35.431000 | https://github.com/facebookresearch/self_defeating_improvements | 4 | Fixes that fail: Self-defeating improvements in machine-learning systems | https://scholar.google.com/scholar?cluster=11324765281769913396&hl=en&as_sdt=0,15 | 8 | 2,021 |
Coarse-to-fine Animal Pose and Shape Estimation | 5 | neurips | 8 | 2 | 2023-06-16 16:06:35.631000 | https://github.com/chaneyddtt/coarse-to-fine-3d-animal | 26 | Coarse-to-fine animal pose and shape estimation | https://scholar.google.com/scholar?cluster=13174062854434293383&hl=en&as_sdt=0,5 | 2 | 2,021 |
Meta-Learning Sparse Implicit Neural Representations | 13 | neurips | 3 | 0 | 2023-06-16 16:06:35.832000 | https://github.com/jaeho-lee/MetaSparseINR | 45 | Meta-learning sparse implicit neural representations | https://scholar.google.com/scholar?cluster=15081844900772325837&hl=en&as_sdt=0,5 | 4 | 2,021 |
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation | 134 | neurips | 65 | 2 | 2023-06-16 16:06:36.032000 | https://github.com/hkchengrex/STCN | 480 | Rethinking space-time networks with improved memory coverage for efficient video object segmentation | https://scholar.google.com/scholar?cluster=972182322509240859&hl=en&as_sdt=0,11 | 8 | 2,021 |
Towards Efficient and Effective Adversarial Training | 28 | neurips | 1 | 1 | 2023-06-16 16:06:36.231000 | https://github.com/val-iisc/nuat | 15 | Towards efficient and effective adversarial training | https://scholar.google.com/scholar?cluster=11235823005919220194&hl=en&as_sdt=0,5 | 13 | 2,021 |
Intriguing Properties of Contrastive Losses | 115 | neurips | 570 | 69 | 2023-06-16 16:06:36.430000 | https://github.com/google-research/simclr | 3,562 | Intriguing properties of contrastive losses | https://scholar.google.com/scholar?cluster=4366111052607966532&hl=en&as_sdt=0,11 | 46 | 2,021 |
Detecting Moments and Highlights in Videos via Natural Language Queries | 44 | neurips | 32 | 7 | 2023-06-16 16:06:36.629000 | https://github.com/jayleicn/moment_detr | 163 | Detecting moments and highlights in videos via natural language queries | https://scholar.google.com/scholar?cluster=2821905623322398755&hl=en&as_sdt=0,44 | 10 | 2,021 |
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems | 9 | neurips | 2 | 0 | 2023-06-16 16:06:36.829000 | https://github.com/andrschl/stable-ndde | 4 | Learning stable deep dynamics models for partially observed or delayed dynamical systems | https://scholar.google.com/scholar?cluster=9330759144731592211&hl=en&as_sdt=0,33 | 1 | 2,021 |
An Uncertainty Principle is a Price of Privacy-Preserving Microdata | 13 | neurips | 0 | 0 | 2023-06-16 16:06:37.030000 | https://github.com/uscensusbureau/CostOfMicrodataNeurIPS2021 | 1 | An uncertainty principle is a price of privacy-preserving microdata | https://scholar.google.com/scholar?cluster=731929662689496666&hl=en&as_sdt=0,33 | 6 | 2,021 |
Fairness in Ranking under Uncertainty | 23 | neurips | 0 | 0 | 2023-06-16 16:06:37.229000 | https://github.com/ashudeep/ranking-fairness-uncertainty | 9 | Fairness in ranking under uncertainty | https://scholar.google.com/scholar?cluster=8766040345698032418&hl=en&as_sdt=0,5 | 2 | 2,021 |
Generalized Proximal Policy Optimization with Sample Reuse | 16 | neurips | 0 | 2 | 2023-06-16 16:06:37.428000 | https://github.com/jqueeney/geppo | 13 | Generalized proximal policy optimization with sample reuse | https://scholar.google.com/scholar?cluster=4171321851465762143&hl=en&as_sdt=0,10 | 1 | 2,021 |
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data | 20 | neurips | 7 | 1 | 2023-06-16 16:06:37.628000 | https://github.com/zju-vipa/mosaickd | 39 | Mosaicking to distill: Knowledge distillation from out-of-domain data | https://scholar.google.com/scholar?cluster=14692300996784513137&hl=en&as_sdt=0,5 | 4 | 2,021 |
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection | 17 | neurips | 3 | 0 | 2023-06-16 16:06:37.827000 | https://github.com/jiwei0921/jsm | 9 | Joint semantic mining for weakly supervised RGB-d salient object detection | https://scholar.google.com/scholar?cluster=6195993508190373693&hl=en&as_sdt=0,5 | 2 | 2,021 |
Contrastive Learning for Neural Topic Model | 17 | neurips | 3 | 3 | 2023-06-16 16:06:38.027000 | https://github.com/nguyentthong/CLNTM | 25 | Contrastive learning for neural topic model | https://scholar.google.com/scholar?cluster=10430438034264335741&hl=en&as_sdt=0,43 | 1 | 2,021 |
ATISS: Autoregressive Transformers for Indoor Scene Synthesis | 37 | neurips | 37 | 4 | 2023-06-16 16:06:38.230000 | https://github.com/nv-tlabs/atiss | 180 | Atiss: Autoregressive transformers for indoor scene synthesis | https://scholar.google.com/scholar?cluster=7663672356809385769&hl=en&as_sdt=0,33 | 16 | 2,021 |
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks | 4 | neurips | 4 | 0 | 2023-06-16 16:06:38.429000 | https://github.com/hsndbk4/gdws | 8 | Generalized depthwise-separable convolutions for adversarially robust and efficient neural networks | https://scholar.google.com/scholar?cluster=7917258653849335165&hl=en&as_sdt=0,22 | 1 | 2,021 |
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers | 1,290 | neurips | 264 | 76 | 2023-06-16 16:06:38.629000 | https://github.com/NVlabs/SegFormer | 1,684 | SegFormer: Simple and efficient design for semantic segmentation with transformers | https://scholar.google.com/scholar?cluster=11165298458048562314&hl=en&as_sdt=0,23 | 28 | 2,021 |
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning | 15 | neurips | 3 | 0 | 2023-06-16 16:06:38.829000 | https://github.com/compdyn/rl_grid_coarsen | 4 | Optimization-based algebraic multigrid coarsening using reinforcement learning | https://scholar.google.com/scholar?cluster=17469824213122053869&hl=en&as_sdt=0,10 | 2 | 2,021 |
Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems | 7 | neurips | 2 | 0 | 2023-06-16 16:06:39.029000 | https://github.com/vita-group/dept | 6 | Delayed propagation transformer: A universal computation engine towards practical control in cyber-physical systems | https://scholar.google.com/scholar?cluster=15971168398161981111&hl=en&as_sdt=0,29 | 6 | 2,021 |
Explaining Latent Representations with a Corpus of Examples | 12 | neurips | 8 | 0 | 2023-06-16 16:06:39.229000 | https://github.com/jonathancrabbe/simplex | 19 | Explaining latent representations with a corpus of examples | https://scholar.google.com/scholar?cluster=4017090788883976971&hl=en&as_sdt=0,22 | 3 | 2,021 |
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks | 12 | neurips | 0 | 0 | 2023-06-16 16:06:39.428000 | https://github.com/neuroailab/mec | 9 | Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks | https://scholar.google.com/scholar?cluster=9080982339262478119&hl=en&as_sdt=0,5 | 4 | 2,021 |
FACMAC: Factored Multi-Agent Centralised Policy Gradients | 89 | neurips | 26 | 8 | 2023-06-16 16:06:39.628000 | https://github.com/schroederdewitt/multiagent_mujoco | 250 | Facmac: Factored multi-agent centralised policy gradients | https://scholar.google.com/scholar?cluster=3516187907112505295&hl=en&as_sdt=0,47 | 9 | 2,021 |
EDGE: Explaining Deep Reinforcement Learning Policies | 15 | neurips | 0 | 1 | 2023-06-16 16:06:39.828000 | https://github.com/henrygwb/edge | 11 | Edge: Explaining deep reinforcement learning policies | https://scholar.google.com/scholar?cluster=10612065413768368207&hl=en&as_sdt=0,50 | 3 | 2,021 |
Learning to Assimilate in Chaotic Dynamical Systems | 5 | neurips | 0 | 0 | 2023-06-16 16:06:40.027000 | https://github.com/mikemccabe210/amortizedassimilation | 4 | Learning to assimilate in chaotic dynamical systems | https://scholar.google.com/scholar?cluster=4578338476402919317&hl=en&as_sdt=0,5 | 1 | 2,021 |
Object-aware Contrastive Learning for Debiased Scene Representation | 28 | neurips | 7 | 0 | 2023-06-16 16:06:40.227000 | https://github.com/alinlab/object-aware-contrastive | 43 | Object-aware contrastive learning for debiased scene representation | https://scholar.google.com/scholar?cluster=8671394054522107055&hl=en&as_sdt=0,11 | 5 | 2,021 |
Evaluating Efficient Performance Estimators of Neural Architectures | 36 | neurips | 27 | 13 | 2023-06-16 16:06:40.428000 | https://github.com/walkerning/aw_nas | 224 | Evaluating efficient performance estimators of neural architectures | https://scholar.google.com/scholar?cluster=12282663317439735649&hl=en&as_sdt=0,5 | 20 | 2,021 |
How can classical multidimensional scaling go wrong? | 1 | neurips | 0 | 0 | 2023-06-16 16:06:40.627000 | https://github.com/rsonthal/Trace-cMDS | 2 | How can classical multidimensional scaling go wrong? | https://scholar.google.com/scholar?cluster=2328869828786838167&hl=en&as_sdt=0,15 | 1 | 2,021 |
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones | 21 | neurips | 2 | 1 | 2023-06-16 16:06:40.828000 | https://github.com/snap-stanford/ConE | 17 | Modeling heterogeneous hierarchies with relation-specific hyperbolic cones | https://scholar.google.com/scholar?cluster=6519771613830294216&hl=en&as_sdt=0,39 | 4 | 2,021 |
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality | 20 | neurips | 4 | 0 | 2023-06-16 16:06:41.028000 | https://github.com/Stanford-ILIAD/Confidence-Aware-Imitation-Learning | 25 | Confidence-aware imitation learning from demonstrations with varying optimality | https://scholar.google.com/scholar?cluster=8104587392600832950&hl=en&as_sdt=0,22 | 5 | 2,021 |
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections | 20 | neurips | 0 | 0 | 2023-06-16 16:06:41.227000 | https://github.com/kimiandj/fast_sw | 2 | Fast approximation of the sliced-Wasserstein distance using concentration of random projections | https://scholar.google.com/scholar?cluster=16052307560028480988&hl=en&as_sdt=0,47 | 2 | 2,021 |
Causal Navigation by Continuous-time Neural Networks | 28 | neurips | 0 | 0 | 2023-06-16 16:06:41.427000 | https://github.com/mit-drl/deepdrone-public | 0 | Causal navigation by continuous-time neural networks | https://scholar.google.com/scholar?cluster=17904682122382854627&hl=en&as_sdt=0,23 | 4 | 2,021 |
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers | 121 | neurips | 27 | 8 | 2023-06-16 16:06:41.627000 | https://github.com/facebookresearch/Motionformer | 207 | Keeping your eye on the ball: Trajectory attention in video transformers | https://scholar.google.com/scholar?cluster=15297477857724176854&hl=en&as_sdt=0,47 | 11 | 2,021 |
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning | 8 | neurips | 3 | 0 | 2023-06-16 16:06:41.828000 | https://github.com/xionghuichen/codas | 6 | Cross-modal domain adaptation for cost-efficient visual reinforcement learning | https://scholar.google.com/scholar?cluster=2567086608461815937&hl=en&as_sdt=0,47 | 3 | 2,021 |
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation | 62 | neurips | 18 | 1 | 2023-06-16 16:06:42.028000 | https://github.com/jiamings/d2c | 105 | D2c: Diffusion-decoding models for few-shot conditional generation | https://scholar.google.com/scholar?cluster=10192213298820143142&hl=en&as_sdt=0,1 | 4 | 2,021 |
Out-of-Distribution Generalization in Kernel Regression | 7 | neurips | 1 | 0 | 2023-06-16 16:06:42.227000 | https://github.com/pehlevan-group/kernel-ood-generalization | 2 | Out-of-distribution generalization in kernel regression | https://scholar.google.com/scholar?cluster=569923176833535725&hl=en&as_sdt=0,5 | 1 | 2,021 |
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective | 27 | neurips | 6 | 0 | 2023-06-16 16:06:42.427000 | https://github.com/jeremy313/fl-wbc | 31 | Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective | https://scholar.google.com/scholar?cluster=828507894680614464&hl=en&as_sdt=0,44 | 1 | 2,021 |
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote | 7 | neurips | 0 | 1 | 2023-06-16 16:06:42.626000 | https://github.com/stephanlorenzen/majorityvotebounds | 12 | Chebyshev-Cantelli PAC-Bayes-Bennett inequality for the weighted majority vote | https://scholar.google.com/scholar?cluster=1599783900613292078&hl=en&as_sdt=0,23 | 2 | 2,021 |
The Inductive Bias of Quantum Kernels | 43 | neurips | 1 | 0 | 2023-06-16 16:06:42.826000 | https://github.com/jmkuebler/quantumbias | 3 | The inductive bias of quantum kernels | https://scholar.google.com/scholar?cluster=11262844044070115079&hl=en&as_sdt=0,5 | 2 | 2,021 |
Pretraining Representations for Data-Efficient Reinforcement Learning | 68 | neurips | 7 | 1 | 2023-06-16 16:06:43.026000 | https://github.com/mila-iqia/SGI | 46 | Pretraining representations for data-efficient reinforcement learning | https://scholar.google.com/scholar?cluster=14071558291421901639&hl=en&as_sdt=0,5 | 5 | 2,021 |
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? | 34 | neurips | 2 | 0 | 2023-06-16 16:06:43.225000 | https://github.com/boone891214/sanity-check-LTH | 5 | Sanity checks for lottery tickets: Does your winning ticket really win the jackpot? | https://scholar.google.com/scholar?cluster=205912691143067869&hl=en&as_sdt=0,32 | 2 | 2,021 |
Understanding Interlocking Dynamics of Cooperative Rationalization | 17 | neurips | 1 | 3 | 2023-06-16 16:06:43.426000 | https://github.com/gorov/understanding_interlocking | 1 | Understanding interlocking dynamics of cooperative rationalization | https://scholar.google.com/scholar?cluster=7420731120310310890&hl=en&as_sdt=0,5 | 1 | 2,021 |
Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning | 20 | neurips | 0 | 1 | 2023-06-16 16:06:43.625000 | https://github.com/jasonzhang929/BVFT_empirical_experiments | 6 | Towards hyperparameter-free policy selection for offline reinforcement learning | https://scholar.google.com/scholar?cluster=9175248700275907762&hl=en&as_sdt=0,5 | 2 | 2,021 |
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals | 5 | neurips | 0 | 0 | 2023-06-16 16:06:43.831000 | https://github.com/langliu95/divergence-frontier-bounds | 0 | Divergence frontiers for generative models: Sample complexity, quantization effects, and frontier integrals | https://scholar.google.com/scholar?cluster=3503189319971497942&hl=en&as_sdt=0,5 | 1 | 2,021 |
Consistency Regularization for Variational Auto-Encoders | 34 | neurips | 2 | 0 | 2023-06-16 16:06:44.032000 | https://github.com/sinhasam/crvae | 2 | Consistency regularization for variational auto-encoders | https://scholar.google.com/scholar?cluster=15925452780992311811&hl=en&as_sdt=0,5 | 3 | 2,021 |
Interactive Label Cleaning with Example-based Explanations | 13 | neurips | 1 | 0 | 2023-06-16 16:06:44.233000 | https://github.com/abonte/cincer | 10 | Interactive label cleaning with example-based explanations | https://scholar.google.com/scholar?cluster=9096815047813990175&hl=en&as_sdt=0,33 | 4 | 2,021 |
Glance-and-Gaze Vision Transformer | 45 | neurips | 2 | 1 | 2023-06-16 16:06:44.432000 | https://github.com/yucornetto/GG-Transformer | 28 | Glance-and-gaze vision transformer | https://scholar.google.com/scholar?cluster=1431816651418361565&hl=en&as_sdt=0,14 | 7 | 2,021 |
Self-Supervised GANs with Label Augmentation | 11 | neurips | 3 | 0 | 2023-06-16 16:06:44.632000 | https://github.com/houliangict/ssgan-la | 19 | Self-supervised gans with label augmentation | https://scholar.google.com/scholar?cluster=14631812487211747492&hl=en&as_sdt=0,4 | 1 | 2,021 |
Shape As Points: A Differentiable Poisson Solver | 74 | neurips | 31 | 6 | 2023-06-16 16:06:44.833000 | https://github.com/autonomousvision/shape_as_points | 444 | Shape as points: A differentiable poisson solver | https://scholar.google.com/scholar?cluster=11152020817998179193&hl=en&as_sdt=0,5 | 23 | 2,021 |
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks | 20 | neurips | 0 | 0 | 2023-06-16 16:06:45.033000 | https://github.com/rice-eic/robust-scratch-ticket | 13 | Drawing robust scratch tickets: Subnetworks with inborn robustness are found within randomly initialized networks | https://scholar.google.com/scholar?cluster=1112960580717486938&hl=en&as_sdt=0,10 | 2 | 2,021 |
Rectifying the Shortcut Learning of Background for Few-Shot Learning | 47 | neurips | 16 | 0 | 2023-06-16 16:06:45.233000 | https://github.com/Frankluox/FewShotCodeBase | 84 | Rectifying the shortcut learning of background for few-shot learning | https://scholar.google.com/scholar?cluster=4412946575250832431&hl=en&as_sdt=0,20 | 4 | 2,021 |
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning | 10 | neurips | 0 | 0 | 2023-06-16 16:06:45.433000 | https://github.com/uuujf/morl | 2 | Accommodating picky customers: Regret bound and exploration complexity for multi-objective reinforcement learning | https://scholar.google.com/scholar?cluster=15706169705724363755&hl=en&as_sdt=0,39 | 1 | 2,021 |
The Emergence of Objectness: Learning Zero-shot Segmentation from Videos | 20 | neurips | 8 | 5 | 2023-06-16 16:06:45.632000 | https://github.com/rt219/the-emergence-of-objectness | 49 | The emergence of objectness: Learning zero-shot segmentation from videos | https://scholar.google.com/scholar?cluster=2619393052877495723&hl=en&as_sdt=0,5 | 6 | 2,021 |
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference | 20 | neurips | 0 | 1 | 2023-06-16 16:06:45.832000 | https://github.com/ucla-starai/circuit-ops-atlas | 4 | A compositional atlas of tractable circuit operations for probabilistic inference | https://scholar.google.com/scholar?cluster=1664691014930951801&hl=en&as_sdt=0,32 | 3 | 2,021 |
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator | 3 | neurips | 3 | 0 | 2023-06-16 16:06:46.033000 | https://github.com/alekdimi/carms | 1 | CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator | https://scholar.google.com/scholar?cluster=9402651328510741907&hl=en&as_sdt=0,3 | 1 | 2,021 |
Representing Long-Range Context for Graph Neural Networks with Global Attention | 70 | neurips | 17 | 5 | 2023-06-16 16:06:46.233000 | https://github.com/ucbrise/graphtrans | 86 | Representing long-range context for graph neural networks with global attention | https://scholar.google.com/scholar?cluster=4846274432308577518&hl=en&as_sdt=0,5 | 5 | 2,021 |
Implicit Transformer Network for Screen Content Image Continuous Super-Resolution | 15 | neurips | 7 | 3 | 2023-06-16 16:06:46.432000 | https://github.com/codyshen0000/itsrn | 40 | Implicit transformer network for screen content image continuous super-resolution | https://scholar.google.com/scholar?cluster=11245794845935575123&hl=en&as_sdt=0,10 | 8 | 2,021 |
Channel Permutations for N:M Sparsity | 17 | neurips | 1,213 | 656 | 2023-06-16 16:06:46.632000 | https://github.com/NVIDIA/apex | 7,297 | Channel permutations for n: m sparsity | https://scholar.google.com/scholar?cluster=11721196871022248200&hl=en&as_sdt=0,10 | 100 | 2,021 |
Video Instance Segmentation using Inter-Frame Communication Transformers | 73 | neurips | 13 | 3 | 2023-06-16 16:06:46.832000 | https://github.com/sukjunhwang/IFC | 85 | Video instance segmentation using inter-frame communication transformers | https://scholar.google.com/scholar?cluster=10954642986790215849&hl=en&as_sdt=0,44 | 5 | 2,021 |
Progressive Coordinate Transforms for Monocular 3D Object Detection | 44 | neurips | 10 | 6 | 2023-06-16 16:06:47.032000 | https://github.com/amazon-research/progressive-coordinate-transforms | 62 | Progressive coordinate transforms for monocular 3d object detection | https://scholar.google.com/scholar?cluster=9147402197404882623&hl=en&as_sdt=0,26 | 4 | 2,021 |
Structured Reordering for Modeling Latent Alignments in Sequence Transduction | 17 | neurips | 19 | 0 | 2023-06-16 16:06:47.231000 | https://github.com/berlino/tensor2struct-public | 83 | Structured reordering for modeling latent alignments in sequence transduction | https://scholar.google.com/scholar?cluster=16621898977325649055&hl=en&as_sdt=0,11 | 6 | 2,021 |
HNPE: Leveraging Global Parameters for Neural Posterior Estimation | 2 | neurips | 2 | 1 | 2023-06-16 16:06:47.431000 | https://github.com/plcrodrigues/hnpe | 11 | HNPE: Leveraging global parameters for neural posterior estimation | https://scholar.google.com/scholar?cluster=65503754638557364&hl=en&as_sdt=0,10 | 6 | 2,021 |
Alignment Attention by Matching Key and Query Distributions | 5 | neurips | 1 | 0 | 2023-06-16 16:06:47.631000 | https://github.com/szhang42/alignment_attention | 6 | Alignment attention by matching key and query distributions | https://scholar.google.com/scholar?cluster=4032930998238119032&hl=en&as_sdt=0,34 | 2 | 2,021 |
Settling the Variance of Multi-Agent Policy Gradients | 24 | neurips | 7 | 3 | 2023-06-16 16:06:47.831000 | https://github.com/morning9393/optimal-baseline-for-multi-agent-policy-gradients | 24 | Settling the variance of multi-agent policy gradients | https://scholar.google.com/scholar?cluster=3289943660848512969&hl=en&as_sdt=0,14 | 1 | 2,021 |
Controllable and Compositional Generation with Latent-Space Energy-Based Models | 30 | neurips | 9 | 0 | 2023-06-16 16:06:48.031000 | https://github.com/NVlabs/LACE | 66 | Controllable and compositional generation with latent-space energy-based models | https://scholar.google.com/scholar?cluster=3651132171595385407&hl=en&as_sdt=0,15 | 4 | 2,021 |
Reverse-Complement Equivariant Networks for DNA Sequences | 8 | neurips | 1 | 0 | 2023-06-16 16:06:48.232000 | https://github.com/vincentx15/equi-rc | 9 | Reverse-complement equivariant networks for DNA sequences | https://scholar.google.com/scholar?cluster=10144921581759903612&hl=en&as_sdt=0,33 | 3 | 2,021 |
Temporal-attentive Covariance Pooling Networks for Video Recognition | 12 | neurips | 7 | 0 | 2023-06-16 16:06:48.432000 | https://github.com/ZilinGao/Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition | 23 | Temporal-attentive covariance pooling networks for video recognition | https://scholar.google.com/scholar?cluster=9201162908941511387&hl=en&as_sdt=0,5 | 1 | 2,021 |
Marginalised Gaussian Processes with Nested Sampling | 8 | neurips | 0 | 0 | 2023-06-16 16:06:48.632000 | https://github.com/frgsimpson/nsampling | 0 | Marginalised gaussian processes with nested sampling | https://scholar.google.com/scholar?cluster=17735373973978612966&hl=en&as_sdt=0,5 | 1 | 2,021 |
Provably Faster Algorithms for Bilevel Optimization | 71 | neurips | 4 | 0 | 2023-06-16 16:06:48.835000 | https://github.com/JunjieYang97/MRVRBO | 12 | Provably faster algorithms for bilevel optimization | https://scholar.google.com/scholar?cluster=9607977285586216355&hl=en&as_sdt=0,48 | 1 | 2,021 |
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction | 31 | neurips | 6 | 2 | 2023-06-16 16:06:49.035000 | https://github.com/seongjunyun/neo_gnns | 27 | Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction | https://scholar.google.com/scholar?cluster=2697789317033616944&hl=en&as_sdt=0,47 | 2 | 2,021 |
Self-Supervised Multi-Object Tracking with Cross-input Consistency | 11 | neurips | 2 | 5 | 2023-06-16 16:06:49.246000 | https://github.com/favyen/uns20 | 14 | Self-supervised multi-object tracking with cross-input consistency | https://scholar.google.com/scholar?cluster=13432924091721167465&hl=en&as_sdt=0,5 | 1 | 2,021 |
Tree in Tree: from Decision Trees to Decision Graphs | 1 | neurips | 1 | 0 | 2023-06-16 16:06:49.456000 | https://github.com/BingzhaoZhu/TnTDecisionGraph | 10 | Tree in Tree: from Decision Trees to Decision Graphs | https://scholar.google.com/scholar?cluster=13675421190274880404&hl=en&as_sdt=0,33 | 1 | 2,021 |
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles | 57 | neurips | 40 | 7 | 2023-06-16 16:06:49.657000 | https://github.com/PattanaikL/GeoMol | 133 | Geomol: Torsional geometric generation of molecular 3d conformer ensembles | https://scholar.google.com/scholar?cluster=12713922106835404541&hl=en&as_sdt=0,33 | 7 | 2,021 |
Implicit Semantic Response Alignment for Partial Domain Adaptation | 5 | neurips | 1 | 0 | 2023-06-16 16:06:49.859000 | https://github.com/implicit-seman-align/implicit-semantic-response-alignment | 4 | Implicit semantic response alignment for partial domain adaptation | https://scholar.google.com/scholar?cluster=17586829602447359052&hl=en&as_sdt=0,5 | 1 | 2,021 |
ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation | 23 | neurips | 12 | 4 | 2023-06-16 16:06:50.105000 | https://github.com/microsoft/UDA | 83 | ToAlign: task-oriented alignment for unsupervised domain adaptation | https://scholar.google.com/scholar?cluster=9142110376115272009&hl=en&as_sdt=0,5 | 7 | 2,021 |
Safe Reinforcement Learning by Imagining the Near Future | 23 | neurips | 6 | 1 | 2023-06-16 16:06:50.305000 | https://github.com/gwthomas/safe-mbpo | 29 | Safe reinforcement learning by imagining the near future | https://scholar.google.com/scholar?cluster=7090557022345376881&hl=en&as_sdt=0,5 | 2 | 2,021 |
Towards Biologically Plausible Convolutional Networks | 19 | neurips | 3 | 0 | 2023-06-16 16:06:50.506000 | https://github.com/romanpogodin/towards-bio-plausible-conv | 13 | Towards biologically plausible convolutional networks | https://scholar.google.com/scholar?cluster=17481866081274764684&hl=en&as_sdt=0,41 | 2 | 2,021 |
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification | 253 | neurips | 65 | 1 | 2023-06-16 16:06:50.706000 | https://github.com/raoyongming/DynamicViT | 474 | Dynamicvit: Efficient vision transformers with dynamic token sparsification | https://scholar.google.com/scholar?cluster=14185047449981394536&hl=en&as_sdt=0,5 | 11 | 2,021 |
Learning Transferable Adversarial Perturbations | 18 | neurips | 2 | 1 | 2023-06-16 16:06:50.944000 | https://github.com/krishnakanthnakka/transferable_perturbations | 21 | Learning transferable adversarial perturbations | https://scholar.google.com/scholar?cluster=13743701895740098488&hl=en&as_sdt=0,14 | 0 | 2,021 |
PortaSpeech: Portable and High-Quality Generative Text-to-Speech | 39 | neurips | 87 | 16 | 2023-06-16 16:06:51.144000 | https://github.com/natspeech/natspeech | 866 | Portaspeech: Portable and high-quality generative text-to-speech | https://scholar.google.com/scholar?cluster=4177501522773357655&hl=en&as_sdt=0,44 | 20 | 2,021 |
Learning Treatment Effects in Panels with General Intervention Patterns | 8 | neurips | 0 | 0 | 2023-06-16 16:06:51.344000 | https://github.com/TianyiPeng/Causal-Inference-Code | 0 | Learning treatment effects in panels with general intervention patterns | https://scholar.google.com/scholar?cluster=15798441898822677855&hl=en&as_sdt=0,36 | 2 | 2,021 |
Lossy Compression for Lossless Prediction | 39 | neurips | 7 | 1 | 2023-06-16 16:06:51.544000 | https://github.com/YannDubs/lossyless | 96 | Lossy compression for lossless prediction | https://scholar.google.com/scholar?cluster=767597494653209957&hl=en&as_sdt=0,39 | 8 | 2,021 |
CCVS: Context-aware Controllable Video Synthesis | 23 | neurips | 0 | 3 | 2023-06-16 16:06:51.745000 | https://github.com/16lemoing/ccvs | 19 | Ccvs: context-aware controllable video synthesis | https://scholar.google.com/scholar?cluster=4232968738296404748&hl=en&as_sdt=0,10 | 2 | 2,021 |
Deep Extrapolation for Attribute-Enhanced Generation | 11 | neurips | 11 | 2 | 2023-06-16 16:06:51.945000 | https://github.com/salesforce/genhance | 29 | Deep extrapolation for attribute-enhanced generation | https://scholar.google.com/scholar?cluster=14781609515979252520&hl=en&as_sdt=0,44 | 6 | 2,021 |
Generalized DataWeighting via Class-Level Gradient Manipulation | 11 | neurips | 3 | 0 | 2023-06-16 16:06:52.145000 | https://github.com/ggchen1997/gdw-nips2021 | 18 | Generalized dataweighting via class-level gradient manipulation | https://scholar.google.com/scholar?cluster=4782284978839575069&hl=en&as_sdt=0,5 | 2 | 2,021 |
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation | 5 | neurips | 4 | 0 | 2023-06-16 16:06:52.346000 | https://github.com/canqin001/efficient_graph_similarity_computation | 27 | Slow learning and fast inference: Efficient graph similarity computation via knowledge distillation | https://scholar.google.com/scholar?cluster=7481527456044774037&hl=en&as_sdt=0,5 | 2 | 2,021 |
Posterior Meta-Replay for Continual Learning | 27 | neurips | 3 | 0 | 2023-06-16 16:06:52.545000 | https://github.com/chrhenning/posterior_replay_cl | 13 | Posterior meta-replay for continual learning | https://scholar.google.com/scholar?cluster=13065615771261410719&hl=en&as_sdt=0,31 | 1 | 2,021 |
Optimizing Reusable Knowledge for Continual Learning via Metalearning | 19 | neurips | 3 | 1 | 2023-06-16 16:06:52.745000 | https://github.com/JuliousHurtado/meta-training-setup | 9 | Optimizing reusable knowledge for continual learning via metalearning | https://scholar.google.com/scholar?cluster=17458269254540986123&hl=en&as_sdt=0,5 | 2 | 2,021 |
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation | 61 | neurips | 14 | 0 | 2023-06-16 16:06:52.946000 | https://github.com/ivam-he/BernNet | 38 | Bernnet: Learning arbitrary graph spectral filters via bernstein approximation | https://scholar.google.com/scholar?cluster=16143355753899412222&hl=en&as_sdt=0,3 | 2 | 2,021 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.