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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 |
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