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GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement
| 64 |
neurips
| 18 | 2 |
2023-06-16 16:06:13.009000
|
https://github.com/applied-ai-lab/genesis
| 87 |
Genesis-v2: Inferring unordered object representations without iterative refinement
|
https://scholar.google.com/scholar?cluster=5704050688122267837&hl=en&as_sdt=0,5
| 4 | 2,021 |
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning
| 15 |
neurips
| 0 | 0 |
2023-06-16 16:06:13.210000
|
https://github.com/albertometelli/subgaussian-is
| 1 |
Subgaussian and differentiable importance sampling for off-policy evaluation and learning
|
https://scholar.google.com/scholar?cluster=11613603668630448953&hl=en&as_sdt=0,21
| 1 | 2,021 |
Fair Classification with Adversarial Perturbations
| 24 |
neurips
| 0 | 0 |
2023-06-16 16:06:13.409000
|
https://github.com/AnayMehrotra/Fair-classification-with-adversarial-perturbations
| 3 |
Fair classification with adversarial perturbations
|
https://scholar.google.com/scholar?cluster=6990181264383347779&hl=en&as_sdt=0,44
| 1 | 2,021 |
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces
| 27 |
neurips
| 1 | 0 |
2023-06-16 16:06:13.613000
|
https://github.com/aryandeshwal/ladder
| 11 |
Combining latent space and structured kernels for bayesian optimization over combinatorial spaces
|
https://scholar.google.com/scholar?cluster=7142356730368207972&hl=en&as_sdt=0,5
| 2 | 2,021 |
Gradual Domain Adaptation without Indexed Intermediate Domains
| 19 |
neurips
| 0 | 1 |
2023-06-16 16:06:13.813000
|
https://github.com/hongyouc/idol
| 3 |
Gradual domain adaptation without indexed intermediate domains
|
https://scholar.google.com/scholar?cluster=6843477456336193628&hl=en&as_sdt=0,33
| 2 | 2,021 |
Learning Markov State Abstractions for Deep Reinforcement Learning
| 16 |
neurips
| 4 | 0 |
2023-06-16 16:06:14.013000
|
https://github.com/camall3n/markov-state-abstractions
| 16 |
Learning markov state abstractions for deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=17056908587988458528&hl=en&as_sdt=0,6
| 2 | 2,021 |
Panoptic 3D Scene Reconstruction From a Single RGB Image
| 32 |
neurips
| 21 | 7 |
2023-06-16 16:06:14.212000
|
https://github.com/xheon/panoptic-reconstruction
| 151 |
Panoptic 3d scene reconstruction from a single rgb image
|
https://scholar.google.com/scholar?cluster=12832750898530092236&hl=en&as_sdt=0,5
| 11 | 2,021 |
Measuring Generalization with Optimal Transport
| 11 |
neurips
| 1 | 0 |
2023-06-16 16:06:14.412000
|
https://github.com/chingyaoc/kV-Margin
| 26 |
Measuring generalization with optimal transport
|
https://scholar.google.com/scholar?cluster=6085733723572289031&hl=en&as_sdt=0,34
| 3 | 2,021 |
Low-dimensional Structure in the Space of Language Representations is Reflected in Brain Responses
| 21 |
neurips
| 0 | 0 |
2023-06-16 16:06:14.611000
|
https://github.com/huthlab/rep_structure
| 2 |
Low-dimensional structure in the space of language representations is reflected in brain responses
|
https://scholar.google.com/scholar?cluster=10259223995030137805&hl=en&as_sdt=0,9
| 5 | 2,021 |
Locally Valid and Discriminative Prediction Intervals for Deep Learning Models
| 10 |
neurips
| 1 | 0 |
2023-06-16 16:06:14.810000
|
https://github.com/zlin7/lvd
| 12 |
Locally valid and discriminative prediction intervals for deep learning models
|
https://scholar.google.com/scholar?cluster=11921032232010944367&hl=en&as_sdt=0,44
| 1 | 2,021 |
Personalized Federated Learning With Gaussian Processes
| 44 |
neurips
| 7 | 0 |
2023-06-16 16:06:15.009000
|
https://github.com/IdanAchituve/pFedGP
| 25 |
Personalized federated learning with gaussian processes
|
https://scholar.google.com/scholar?cluster=10986123828571573534&hl=en&as_sdt=0,31
| 1 | 2,021 |
Implicit SVD for Graph Representation Learning
| 3 |
neurips
| 0 | 0 |
2023-06-16 16:06:15.209000
|
https://github.com/samihaija/isvd
| 16 |
Implicit SVD for Graph Representation Learning
|
https://scholar.google.com/scholar?cluster=8383713992891185869&hl=en&as_sdt=0,33
| 2 | 2,021 |
Offline Model-based Adaptable Policy Learning
| 16 |
neurips
| 3 | 0 |
2023-06-16 16:06:15.407000
|
https://github.com/xionghuichen/maple
| 17 |
Offline model-based adaptable policy learning
|
https://scholar.google.com/scholar?cluster=4236652701971289768&hl=en&as_sdt=0,18
| 3 | 2,021 |
Ensembling Graph Predictions for AMR Parsing
| 15 |
neurips
| 5 | 2 |
2023-06-16 16:06:15.610000
|
https://github.com/ibm/graph_ensemble_learning
| 34 |
Ensembling graph predictions for AMR parsing
|
https://scholar.google.com/scholar?cluster=10642315014350686884&hl=en&as_sdt=0,44
| 13 | 2,021 |
On the interplay between data structure and loss function in classification problems
| 12 |
neurips
| 0 | 0 |
2023-06-16 16:06:15.810000
|
https://github.com/sdascoli/data-structure
| 1 |
On the interplay between data structure and loss function in classification problems
|
https://scholar.google.com/scholar?cluster=12068370246989147855&hl=en&as_sdt=0,23
| 2 | 2,021 |
Mixture Proportion Estimation and PU Learning:A Modern Approach
| 23 |
neurips
| 3 | 1 |
2023-06-16 16:06:16.009000
|
https://github.com/acmi-lab/pu_learning
| 31 |
Mixture proportion estimation and pu learning: a modern approach
|
https://scholar.google.com/scholar?cluster=16408997249461916765&hl=en&as_sdt=0,33
| 2 | 2,021 |
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks
| 31 |
neurips
| 2 | 0 |
2023-06-16 16:06:16.208000
|
https://github.com/IST-DASLab/ACDC
| 18 |
Ac/dc: Alternating compressed/decompressed training of deep neural networks
|
https://scholar.google.com/scholar?cluster=4491256831875771327&hl=en&as_sdt=0,5
| 6 | 2,021 |
HyperSPNs: Compact and Expressive Probabilistic Circuits
| 7 |
neurips
| 0 | 0 |
2023-06-16 16:06:16.408000
|
https://github.com/andyshih12/hyperspn
| 10 |
HyperSPNs: compact and expressive probabilistic circuits
|
https://scholar.google.com/scholar?cluster=13400910128328075358&hl=en&as_sdt=0,5
| 2 | 2,021 |
Scaling Vision with Sparse Mixture of Experts
| 176 |
neurips
| 40 | 10 |
2023-06-16 16:06:16.607000
|
https://github.com/google-research/vmoe
| 319 |
Scaling vision with sparse mixture of experts
|
https://scholar.google.com/scholar?cluster=1108172362434613333&hl=en&as_sdt=0,44
| 13 | 2,021 |
Adversarial Intrinsic Motivation for Reinforcement Learning
| 13 |
neurips
| 0 | 0 |
2023-06-16 16:06:16.807000
|
https://github.com/iDurugkar/adversarial-intrinsic-motivation
| 3 |
Adversarial intrinsic motivation for reinforcement learning
|
https://scholar.google.com/scholar?cluster=17506892387153258326&hl=en&as_sdt=0,44
| 1 | 2,021 |
L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization
| 7 |
neurips
| 0 | 0 |
2023-06-16 16:06:17.006000
|
https://github.com/jeremiemelo/l2ight
| 14 |
L2ight: Enabling on-chip learning for optical neural networks via efficient in-situ subspace optimization
|
https://scholar.google.com/scholar?cluster=12160624402740671006&hl=en&as_sdt=0,10
| 2 | 2,021 |
Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond
| 32 |
neurips
| 7 | 0 |
2023-06-16 16:06:17.206000
|
https://github.com/vis-opt-group/iaptt-gm
| 6 |
Towards gradient-based bilevel optimization with non-convex followers and beyond
|
https://scholar.google.com/scholar?cluster=4742630241589008678&hl=en&as_sdt=0,5
| 1 | 2,021 |
Multi-Facet Clustering Variational Autoencoders
| 10 |
neurips
| 8 | 1 |
2023-06-16 16:06:17.405000
|
https://github.com/FabianFalck/mfcvae
| 29 |
Multi-facet clustering variational autoencoders
|
https://scholar.google.com/scholar?cluster=16117521834362890782&hl=en&as_sdt=0,5
| 5 | 2,021 |
Searching the Search Space of Vision Transformer
| 19 |
neurips
| 167 | 24 |
2023-06-16 16:06:17.606000
|
https://github.com/microsoft/cream
| 1,078 |
Searching the search space of vision transformer
|
https://scholar.google.com/scholar?cluster=17171842121702147403&hl=en&as_sdt=0,44
| 25 | 2,021 |
Inverse Problems Leveraging Pre-trained Contrastive Representations
| 6 |
neurips
| 2 | 0 |
2023-06-16 16:06:17.805000
|
https://github.com/sriram-ravula/contrastive-inversion
| 26 |
Inverse problems leveraging pre-trained contrastive representations
|
https://scholar.google.com/scholar?cluster=13090230797997641705&hl=en&as_sdt=0,36
| 4 | 2,021 |
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation
| 36 |
neurips
| 11 | 1 |
2023-06-16 16:06:18.005000
|
https://github.com/thibsej/unbalanced_gromov_wasserstein
| 32 |
The unbalanced gromov wasserstein distance: Conic formulation and relaxation
|
https://scholar.google.com/scholar?cluster=4621301821355236560&hl=en&as_sdt=0,47
| 4 | 2,021 |
Diffusion Models Beat GANs on Image Synthesis
| 1,377 |
neurips
| 611 | 66 |
2023-06-16 16:06:18.204000
|
https://github.com/openai/guided-diffusion
| 4,248 |
Diffusion models beat gans on image synthesis
|
https://scholar.google.com/scholar?cluster=17982230494456470673&hl=en&as_sdt=0,31
| 122 | 2,021 |
A Biased Graph Neural Network Sampler with Near-Optimal Regret
| 16 |
neurips
| 2 | 0 |
2023-06-16 16:06:18.404000
|
https://github.com/QingruZhang/Thanos
| 3 |
A biased graph neural network sampler with near-optimal regret
|
https://scholar.google.com/scholar?cluster=10280015035200600286&hl=en&as_sdt=0,11
| 2 | 2,021 |
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry
| 17 |
neurips
| 3 | 0 |
2023-06-16 16:06:18.603000
|
https://github.com/andyjm3/AI-vs-BW
| 3 |
On Riemannian optimization over positive definite matrices with the Bures-Wasserstein geometry
|
https://scholar.google.com/scholar?cluster=2437471067279904808&hl=en&as_sdt=0,5
| 1 | 2,021 |
Refining Language Models with Compositional Explanations
| 18 |
neurips
| 0 | 0 |
2023-06-16 16:06:18.802000
|
https://github.com/INK-USC/expl-refinement
| 13 |
Refining language models with compositional explanations
|
https://scholar.google.com/scholar?cluster=5798502945545314166&hl=en&as_sdt=0,10
| 4 | 2,021 |
What can linearized neural networks actually say about generalization?
| 18 |
neurips
| 1 | 0 |
2023-06-16 16:06:19.009000
|
https://github.com/gortizji/linearized-networks
| 13 |
What can linearized neural networks actually say about generalization?
|
https://scholar.google.com/scholar?cluster=14899962507858942209&hl=en&as_sdt=0,33
| 3 | 2,021 |
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
| 67 |
neurips
| 54 | 18 |
2023-06-16 16:06:19.208000
|
https://github.com/facebookresearch/SEAL_OGB
| 178 |
Labeling trick: A theory of using graph neural networks for multi-node representation learning
|
https://scholar.google.com/scholar?cluster=2266754779755324127&hl=en&as_sdt=0,5
| 10 | 2,021 |
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients
| 20 |
neurips
| 0 | 0 |
2023-06-16 16:06:19.407000
|
https://github.com/lijunyi95/superadam
| 15 |
Super-adam: faster and universal framework of adaptive gradients
|
https://scholar.google.com/scholar?cluster=14703252703783820284&hl=en&as_sdt=0,33
| 3 | 2,021 |
Denoising Normalizing Flow
| 12 |
neurips
| 1 | 0 |
2023-06-16 16:06:19.607000
|
https://github.com/chrvt/denoising-normalizing-flow
| 18 |
Denoising normalizing flow
|
https://scholar.google.com/scholar?cluster=17109941513992323915&hl=en&as_sdt=0,5
| 2 | 2,021 |
Attention over Learned Object Embeddings Enables Complex Visual Reasoning
| 38 |
neurips
| 2,436 | 170 |
2023-06-16 16:06:19.806000
|
https://github.com/deepmind/deepmind-research
| 11,904 |
Attention over learned object embeddings enables complex visual reasoning
|
https://scholar.google.com/scholar?cluster=127829313460149801&hl=en&as_sdt=0,33
| 336 | 2,021 |
Differentiable Learning Under Triage
| 21 |
neurips
| 2 | 0 |
2023-06-16 16:06:20.005000
|
https://github.com/Networks-Learning/differentiable-learning-under-triage
| 3 |
Differentiable learning under triage
|
https://scholar.google.com/scholar?cluster=3465216605112056644&hl=en&as_sdt=0,15
| 2 | 2,021 |
An Image is Worth More Than a Thousand Words: Towards Disentanglement in The Wild
| 20 |
neurips
| 5 | 2 |
2023-06-16 16:06:20.205000
|
https://github.com/avivga/zerodim
| 18 |
An image is worth more than a thousand words: Towards disentanglement in the wild
|
https://scholar.google.com/scholar?cluster=10161122564731884451&hl=en&as_sdt=0,44
| 1 | 2,021 |
Efficient Statistical Assessment of Neural Network Corruption Robustness
| 5 |
neurips
| 0 | 0 |
2023-06-16 16:06:20.405000
|
https://github.com/karimtito/efficient-statistical
| 0 |
Efficient Statistical Assessment of Neural Network Corruption Robustness
|
https://scholar.google.com/scholar?cluster=9015952957201151715&hl=en&as_sdt=0,23
| 1 | 2,021 |
Realistic evaluation of transductive few-shot learning
| 17 |
neurips
| 2 | 0 |
2023-06-16 16:06:20.603000
|
https://github.com/oveilleux/realistic_transductive_few_shot
| 16 |
Realistic evaluation of transductive few-shot learning
|
https://scholar.google.com/scholar?cluster=779657779998908467&hl=en&as_sdt=0,1
| 2 | 2,021 |
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes
| 6 |
neurips
| 2 | 0 |
2023-06-16 16:06:20.803000
|
https://github.com/secure-ai-systems-group/qu-anti-zation
| 8 |
Qu-anti-zation: Exploiting quantization artifacts for achieving adversarial outcomes
|
https://scholar.google.com/scholar?cluster=3502987218108347003&hl=en&as_sdt=0,47
| 1 | 2,021 |
Integrating Tree Path in Transformer for Code Representation
| 23 |
neurips
| 0 | 0 |
2023-06-16 16:06:21.003000
|
https://github.com/awdhanpeng/tptrans
| 0 |
Integrating tree path in transformer for code representation
|
https://scholar.google.com/scholar?cluster=12295099562232904052&hl=en&as_sdt=0,5
| 1 | 2,021 |
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
| 524 |
neurips
| 63 | 10 |
2023-06-16 16:06:21.202000
|
https://github.com/Meituan-AutoML/Twins
| 511 |
Twins: Revisiting the design of spatial attention in vision transformers
|
https://scholar.google.com/scholar?cluster=5060121065165184210&hl=en&as_sdt=0,5
| 14 | 2,021 |
Data-Efficient Instance Generation from Instance Discrimination
| 45 |
neurips
| 4 | 6 |
2023-06-16 16:06:21.402000
|
https://github.com/genforce/insgen
| 97 |
Data-efficient instance generation from instance discrimination
|
https://scholar.google.com/scholar?cluster=1497192105347715658&hl=en&as_sdt=0,5
| 9 | 2,021 |
Differentiable Equilibrium Computation with Decision Diagrams for Stackelberg Models of Combinatorial Congestion Games
| 1 |
neurips
| 0 | 0 |
2023-06-16 16:06:21.602000
|
https://github.com/nttcslab/diff-eq-comput-zdd
| 2 |
Differentiable equilibrium computation with decision diagrams for stackelberg models of combinatorial congestion games
|
https://scholar.google.com/scholar?cluster=9969998986747783383&hl=en&as_sdt=0,22
| 2 | 2,021 |
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System
| 8 |
neurips
| 2 | 0 |
2023-06-16 16:06:21.801000
|
https://github.com/rothkopflab/inverse-optimal-control
| 2 |
Inverse optimal control adapted to the noise characteristics of the human sensorimotor system
|
https://scholar.google.com/scholar?cluster=5865855006238055136&hl=en&as_sdt=0,33
| 1 | 2,021 |
Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment
| 5 |
neurips
| 1 | 2 |
2023-06-16 16:06:22.001000
|
https://github.com/CTI-VISION/Mirror-Sample
| 5 |
Reducing the covariate shift by mirror samples in cross domain alignment
|
https://scholar.google.com/scholar?cluster=4872841041858237151&hl=en&as_sdt=0,23
| 1 | 2,021 |
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
| 8 |
neurips
| 10 | 3 |
2023-06-16 16:06:22.202000
|
https://github.com/jrwnter/pigvae
| 35 |
Permutation-invariant variational autoencoder for graph-level representation learning
|
https://scholar.google.com/scholar?cluster=11891489203108750561&hl=en&as_sdt=0,5
| 3 | 2,021 |
3DP3: 3D Scene Perception via Probabilistic Programming
| 24 |
neurips
| 3 | 0 |
2023-06-16 16:06:22.403000
|
https://github.com/probcomp/threedp3
| 10 |
3DP3: 3D scene perception via probabilistic programming
|
https://scholar.google.com/scholar?cluster=6863695141270884118&hl=en&as_sdt=0,33
| 12 | 2,021 |
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements
| 24 |
neurips
| 3 | 0 |
2023-06-16 16:06:22.602000
|
https://github.com/fjxmlzn/BSN
| 36 |
Why spectral normalization stabilizes gans: Analysis and improvements
|
https://scholar.google.com/scholar?cluster=17254495230402208234&hl=en&as_sdt=0,5
| 1 | 2,021 |
MADE: Exploration via Maximizing Deviation from Explored Regions
| 24 |
neurips
| 3 | 0 |
2023-06-16 16:06:22.802000
|
https://github.com/tianjunz/MADE
| 17 |
Made: Exploration via maximizing deviation from explored regions
|
https://scholar.google.com/scholar?cluster=8010522815020070662&hl=en&as_sdt=0,5
| 4 | 2,021 |
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
| 586 |
neurips
| 504 | 187 |
2023-06-16 16:06:23.001000
|
https://github.com/salesforce/lavis
| 5,506 |
Align before fuse: Vision and language representation learning with momentum distillation
|
https://scholar.google.com/scholar?cluster=2949653561196582978&hl=en&as_sdt=0,20
| 75 | 2,021 |
Variational Model Inversion Attacks
| 28 |
neurips
| 3 | 4 |
2023-06-16 16:06:23.201000
|
https://github.com/wangkua1/vmi
| 16 |
Variational model inversion attacks
|
https://scholar.google.com/scholar?cluster=14139666548957095548&hl=en&as_sdt=0,29
| 3 | 2,021 |
Graph Neural Networks with Adaptive Residual
| 21 |
neurips
| 4 | 2 |
2023-06-16 16:06:23.401000
|
https://github.com/lxiaorui/airgnn
| 15 |
Graph neural networks with adaptive residual
|
https://scholar.google.com/scholar?cluster=15094075369662309997&hl=en&as_sdt=0,34
| 3 | 2,021 |
TriBERT: Human-centric Audio-visual Representation Learning
| 4 |
neurips
| 2 | 3 |
2023-06-16 16:06:23.601000
|
https://github.com/ubc-vision/tribert
| 10 |
TriBERT: Human-centric Audio-visual Representation Learning
|
https://scholar.google.com/scholar?cluster=8373124147207590076&hl=en&as_sdt=0,5
| 1 | 2,021 |
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning
| 7 |
neurips
| 0 | 1 |
2023-06-16 16:06:23.801000
|
https://github.com/frt03/inference-based-rl
| 17 |
Co-adaptation of algorithmic and implementational innovations in inference-based deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=13585717862866911576&hl=en&as_sdt=0,5
| 0 | 2,021 |
Can fMRI reveal the representation of syntactic structure in the brain?
| 15 |
neurips
| 2 | 0 |
2023-06-16 16:06:24.009000
|
https://github.com/anikethjr/brain_syntactic_representations
| 4 |
Can fMRI reveal the representation of syntactic structure in the brain?
|
https://scholar.google.com/scholar?cluster=8612814511404914759&hl=en&as_sdt=0,5
| 4 | 2,021 |
Robust Implicit Networks via Non-Euclidean Contractions
| 22 |
neurips
| 1 | 0 |
2023-06-16 16:06:24.213000
|
https://github.com/davydovalexander/non-euclidean_mon_op_net
| 0 |
Robust implicit networks via non-Euclidean contractions
|
https://scholar.google.com/scholar?cluster=13884163203137511779&hl=en&as_sdt=0,21
| 1 | 2,021 |
Efficient methods for Gaussian Markov random fields under sparse linear constraints
| 4 |
neurips
| 1 | 0 |
2023-06-16 16:06:24.414000
|
https://github.com/JonasWallin/CB
| 1 |
Efficient methods for Gaussian Markov random fields under sparse linear constraints
|
https://scholar.google.com/scholar?cluster=8649010472840775906&hl=en&as_sdt=0,44
| 3 | 2,021 |
On Provable Benefits of Depth in Training Graph Convolutional Networks
| 39 |
neurips
| 1 | 0 |
2023-06-16 16:06:24.614000
|
https://github.com/CongWeilin/DGCN
| 10 |
On provable benefits of depth in training graph convolutional networks
|
https://scholar.google.com/scholar?cluster=12386140121969765106&hl=en&as_sdt=0,5
| 3 | 2,021 |
Meta-Adaptive Nonlinear Control: Theory and Algorithms
| 13 |
neurips
| 11 | 0 |
2023-06-16 16:06:24.814000
|
https://github.com/GuanyaShi/Online-Meta-Adaptive-Control
| 38 |
Meta-adaptive nonlinear control: Theory and algorithms
|
https://scholar.google.com/scholar?cluster=3468826703271927093&hl=en&as_sdt=0,5
| 3 | 2,021 |
Compositional Reinforcement Learning from Logical Specifications
| 37 |
neurips
| 3 | 0 |
2023-06-16 16:06:25.021000
|
https://github.com/keyshor/dirl
| 11 |
Compositional reinforcement learning from logical specifications
|
https://scholar.google.com/scholar?cluster=14766586595229560420&hl=en&as_sdt=0,22
| 1 | 2,021 |
Credit Assignment Through Broadcasting a Global Error Vector
| 11 |
neurips
| 2 | 0 |
2023-06-16 16:06:25.221000
|
https://github.com/davidclark1/vectorizednets
| 2 |
Credit assignment through broadcasting a global error vector
|
https://scholar.google.com/scholar?cluster=3727698490784134497&hl=en&as_sdt=0,5
| 1 | 2,021 |
An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives
| 17 |
neurips
| 0 | 0 |
2023-06-16 16:06:25.422000
|
https://github.com/qiqi-helloworld/recover
| 10 |
An online method for a class of distributionally robust optimization with non-convex objectives
|
https://scholar.google.com/scholar?cluster=5357983070298547802&hl=en&as_sdt=0,5
| 3 | 2,021 |
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
| 7 |
neurips
| 1 | 0 |
2023-06-16 16:06:25.621000
|
https://github.com/ehsan-mokhtarian/l-marvel
| 0 |
Recursive causal structure learning in the presence of latent variables and selection bias
|
https://scholar.google.com/scholar?cluster=10465421088099872721&hl=en&as_sdt=0,14
| 1 | 2,021 |
Spectral embedding for dynamic networks with stability guarantees
| 9 |
neurips
| 1 | 0 |
2023-06-16 16:06:25.821000
|
https://github.com/iggallagher/Dynamic-Network-Embedding
| 1 |
Spectral embedding for dynamic networks with stability guarantees
|
https://scholar.google.com/scholar?cluster=15639417691972104804&hl=en&as_sdt=0,34
| 1 | 2,021 |
Infinite Time Horizon Safety of Bayesian Neural Networks
| 9 |
neurips
| 1 | 0 |
2023-06-16 16:06:26.021000
|
https://github.com/mlech26l/bayesian_nn_safety
| 0 |
Infinite time horizon safety of Bayesian neural networks
|
https://scholar.google.com/scholar?cluster=3317282080720132097&hl=en&as_sdt=0,33
| 2 | 2,021 |
On the Estimation Bias in Double Q-Learning
| 4 |
neurips
| 0 | 2 |
2023-06-16 16:06:26.222000
|
https://github.com/stilwell-git/doubly-bounded-q-learning
| 1 |
On the Estimation Bias in Double Q-Learning
|
https://scholar.google.com/scholar?cluster=6701423240345765419&hl=en&as_sdt=0,5
| 2 | 2,021 |
Non-Gaussian Gaussian Processes for Few-Shot Regression
| 9 |
neurips
| 1 | 0 |
2023-06-16 16:06:26.421000
|
https://github.com/gmum/non-gaussian-gaussian-processes
| 17 |
Non-gaussian gaussian processes for few-shot regression
|
https://scholar.google.com/scholar?cluster=13494016610404817418&hl=en&as_sdt=0,44
| 6 | 2,021 |
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
| 32 |
neurips
| 7 | 4 |
2023-06-16 16:06:26.621000
|
https://github.com/yiqinyang/icq
| 48 |
Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning
|
https://scholar.google.com/scholar?cluster=3861157451473520917&hl=en&as_sdt=0,6
| 1 | 2,021 |
K-Net: Towards Unified Image Segmentation
| 138 |
neurips
| 43 | 13 |
2023-06-16 16:06:26.828000
|
https://github.com/zwwwayne/k-net
| 442 |
K-net: Towards unified image segmentation
|
https://scholar.google.com/scholar?cluster=9601688478354935911&hl=en&as_sdt=0,34
| 10 | 2,021 |
Learning Collaborative Policies to Solve NP-hard Routing Problems
| 33 |
neurips
| 3 | 1 |
2023-06-16 16:06:27.027000
|
https://github.com/alstn12088/lcp
| 10 |
Learning collaborative policies to solve NP-hard routing problems
|
https://scholar.google.com/scholar?cluster=6269783259343290144&hl=en&as_sdt=0,36
| 1 | 2,021 |
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum
| 5 |
neurips
| 1 | 1 |
2023-06-16 16:06:27.227000
|
https://github.com/shuang-ao/co-pilot
| 1 |
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum
|
https://scholar.google.com/scholar?cluster=13913848066450327449&hl=en&as_sdt=0,22
| 1 | 2,021 |
Kernel Identification Through Transformers
| 5 |
neurips
| 1 | 0 |
2023-06-16 16:06:27.427000
|
https://github.com/frgsimpson/kitt
| 8 |
Kernel identification through transformers
|
https://scholar.google.com/scholar?cluster=17623460492368615234&hl=en&as_sdt=0,5
| 5 | 2,021 |
Curriculum Design for Teaching via Demonstrations: Theory and Applications
| 6 |
neurips
| 1 | 0 |
2023-06-16 16:06:27.626000
|
https://github.com/adishs/neurips2021_curriculum-teaching-demonstrations_code
| 2 |
Curriculum Design for Teaching via Demonstrations: Theory and Applications
|
https://scholar.google.com/scholar?cluster=15048435849390075589&hl=en&as_sdt=0,5
| 2 | 2,021 |
Dynamic Causal Bayesian Optimization
| 13 |
neurips
| 9 | 0 |
2023-06-16 16:06:27.826000
|
https://github.com/neildhir/dcbo
| 24 |
Dynamic causal Bayesian optimization
|
https://scholar.google.com/scholar?cluster=16636999477420016377&hl=en&as_sdt=0,5
| 1 | 2,021 |
Equivariant Manifold Flows
| 8 |
neurips
| 0 | 0 |
2023-06-16 16:06:28.026000
|
https://github.com/cuai/equivariant-manifold-flows
| 7 |
Equivariant manifold flows
|
https://scholar.google.com/scholar?cluster=13655183730986062647&hl=en&as_sdt=0,5
| 2 | 2,021 |
Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation
| 8 |
neurips
| 6 | 0 |
2023-06-16 16:06:28.225000
|
https://github.com/fangyanwen1106/RLANet
| 23 |
Recurrence along depth: Deep convolutional neural networks with recurrent layer aggregation
|
https://scholar.google.com/scholar?cluster=4477865436853861704&hl=en&as_sdt=0,47
| 2 | 2,021 |
Independent Prototype Propagation for Zero-Shot Compositionality
| 20 |
neurips
| 2 | 0 |
2023-06-16 16:06:28.425000
|
https://github.com/FrankRuis/ProtoProp
| 10 |
Independent prototype propagation for zero-shot compositionality
|
https://scholar.google.com/scholar?cluster=13176465019073119909&hl=en&as_sdt=0,30
| 4 | 2,021 |
Universal Graph Convolutional Networks
| 31 |
neurips
| 5 | 1 |
2023-06-16 16:06:28.624000
|
https://github.com/jindi-tju/U-GCN
| 15 |
Universal graph convolutional networks
|
https://scholar.google.com/scholar?cluster=2138305562153632619&hl=en&as_sdt=0,5
| 1 | 2,021 |
Adversarial Feature Desensitization
| 8 |
neurips
| 0 | 0 |
2023-06-16 16:06:28.824000
|
https://github.com/BashivanLab/afd
| 6 |
Adversarial feature desensitization
|
https://scholar.google.com/scholar?cluster=435468338701140175&hl=en&as_sdt=0,36
| 1 | 2,021 |
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition
| 66 |
neurips
| 12 | 3 |
2023-06-16 16:06:29.023000
|
https://github.com/cgtuebingen/Neural-PIL
| 80 |
Neural-pil: Neural pre-integrated lighting for reflectance decomposition
|
https://scholar.google.com/scholar?cluster=3379298908758464795&hl=en&as_sdt=0,5
| 8 | 2,021 |
Extracting Deformation-Aware Local Features by Learning to Deform
| 4 |
neurips
| 4 | 4 |
2023-06-16 16:06:29.223000
|
https://github.com/verlab/DEAL_NeurIPS_2021
| 24 |
Extracting deformation-aware local features by learning to deform
|
https://scholar.google.com/scholar?cluster=14581155560161473029&hl=en&as_sdt=0,1
| 5 | 2,021 |
Gradient-based Hyperparameter Optimization Over Long Horizons
| 5 |
neurips
| 0 | 0 |
2023-06-16 16:06:29.422000
|
https://github.com/polo5/fds
| 10 |
Gradient-based hyperparameter optimization over long horizons
|
https://scholar.google.com/scholar?cluster=6997241772832263952&hl=en&as_sdt=0,5
| 1 | 2,021 |
The Causal-Neural Connection: Expressiveness, Learnability, and Inference
| 41 |
neurips
| 1 | 0 |
2023-06-16 16:06:29.622000
|
https://github.com/causalailab/neuralcausalmodels
| 6 |
The causal-neural connection: Expressiveness, learnability, and inference
|
https://scholar.google.com/scholar?cluster=10952897351624704856&hl=en&as_sdt=0,5
| 1 | 2,021 |
R-Drop: Regularized Dropout for Neural Networks
| 189 |
neurips
| 107 | 1 |
2023-06-16 16:06:29.821000
|
https://github.com/dropreg/R-Drop
| 816 |
R-drop: Regularized dropout for neural networks
|
https://scholar.google.com/scholar?cluster=2475537860429813567&hl=en&as_sdt=0,47
| 5 | 2,021 |
Diversity Enhanced Active Learning with Strictly Proper Scoring Rules
| 8 |
neurips
| 0 | 0 |
2023-06-16 16:06:30.021000
|
https://github.com/davidtw999/bemps
| 8 |
Diversity enhanced active learning with strictly proper scoring rules
|
https://scholar.google.com/scholar?cluster=8484595844255881124&hl=en&as_sdt=0,41
| 1 | 2,021 |
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
| 21 |
neurips
| 10 | 1 |
2023-06-16 16:06:30.222000
|
https://github.com/clovaai/SSUL
| 51 |
SSUL: Semantic segmentation with unknown label for exemplar-based class-incremental learning
|
https://scholar.google.com/scholar?cluster=2873857324904043175&hl=en&as_sdt=0,31
| 5 | 2,021 |
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling
| 5 |
neurips
| 1 | 0 |
2023-06-16 16:06:30.421000
|
https://github.com/gregversteeg/esh_dynamics
| 31 |
Hamiltonian dynamics with non-newtonian momentum for rapid sampling
|
https://scholar.google.com/scholar?cluster=8697297470988026011&hl=en&as_sdt=0,10
| 3 | 2,021 |
Dynamic Normalization and Relay for Video Action Recognition
| 2 |
neurips
| 1 | 1 |
2023-06-16 16:06:30.621000
|
https://github.com/caidonkey/dnr
| 3 |
Dynamic normalization and relay for video action recognition
|
https://scholar.google.com/scholar?cluster=17545308458532261547&hl=en&as_sdt=0,5
| 2 | 2,021 |
True Few-Shot Learning with Language Models
| 168 |
neurips
| 11 | 1 |
2023-06-16 16:06:30.821000
|
https://github.com/ethanjperez/true_few_shot
| 138 |
True few-shot learning with language models
|
https://scholar.google.com/scholar?cluster=1955924689354059509&hl=en&as_sdt=0,33
| 2 | 2,021 |
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
| 43 |
neurips
| 16 | 1 |
2023-06-16 16:06:31.021000
|
https://github.com/yining043/VRP-DACT
| 55 |
Learning to iteratively solve routing problems with dual-aspect collaborative transformer
|
https://scholar.google.com/scholar?cluster=13083892741487844240&hl=en&as_sdt=0,43
| 2 | 2,021 |
Learning interaction rules from multi-animal trajectories via augmented behavioral models
| 11 |
neurips
| 0 | 0 |
2023-06-16 16:06:31.220000
|
https://github.com/keisuke198619/abm
| 6 |
Learning interaction rules from multi-animal trajectories via augmented behavioral models
|
https://scholar.google.com/scholar?cluster=13190745890031985835&hl=en&as_sdt=0,10
| 2 | 2,021 |
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications
| 8 |
neurips
| 23 | 2 |
2023-06-16 16:06:31.420000
|
https://github.com/deepmind/jax_verify
| 126 |
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications
|
https://scholar.google.com/scholar?cluster=4180484968407632121&hl=en&as_sdt=0,33
| 8 | 2,021 |
Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure
| 5 |
neurips
| 0 | 0 |
2023-06-16 16:06:31.620000
|
https://github.com/avivros007/factored-mdp-with-unknown-structure
| 0 |
Oracle-efficient regret minimization in factored mdps with unknown structure
|
https://scholar.google.com/scholar?cluster=10644518817824113787&hl=en&as_sdt=0,5
| 1 | 2,021 |
Making the most of your day: online learning for optimal allocation of time
| 3 |
neurips
| 0 | 0 |
2023-06-16 16:06:31.820000
|
https://github.com/eboursier/making_most_of_your_time
| 1 |
Making the most of your day: online learning for optimal allocation of time
|
https://scholar.google.com/scholar?cluster=391436083487229673&hl=en&as_sdt=0,8
| 1 | 2,021 |
Continuous Doubly Constrained Batch Reinforcement Learning
| 16 |
neurips
| 1 | 0 |
2023-06-16 16:06:32.019000
|
https://github.com/amazon-research/cdc-batch-rl
| 8 |
Continuous doubly constrained batch reinforcement learning
|
https://scholar.google.com/scholar?cluster=4821141646205094799&hl=en&as_sdt=0,10
| 2 | 2,021 |
Score-based Generative Modeling in Latent Space
| 219 |
neurips
| 45 | 6 |
2023-06-16 16:06:32.219000
|
https://github.com/NVlabs/LSGM
| 280 |
Score-based generative modeling in latent space
|
https://scholar.google.com/scholar?cluster=1591095957629218534&hl=en&as_sdt=0,5
| 8 | 2,021 |
Deep Conditional Gaussian Mixture Model for Constrained Clustering
| 14 |
neurips
| 4 | 1 |
2023-06-16 16:06:32.419000
|
https://github.com/lauramanduchi/DC-GMM
| 21 |
Deep conditional gaussian mixture model for constrained clustering
|
https://scholar.google.com/scholar?cluster=10567997347878882967&hl=en&as_sdt=0,33
| 1 | 2,021 |
Bootstrap Your Object Detector via Mixed Training
| 5 |
neurips
| 6 | 3 |
2023-06-16 16:06:32.618000
|
https://github.com/mendelxu/mixtraining
| 55 |
Bootstrap your object detector via mixed training
|
https://scholar.google.com/scholar?cluster=14330595085341601931&hl=en&as_sdt=0,5
| 11 | 2,021 |
One Explanation is Not Enough: Structured Attention Graphs for Image Classification
| 7 |
neurips
| 3 | 0 |
2023-06-16 16:06:32.817000
|
https://github.com/viv92/structured-attention-graphs
| 23 |
One explanation is not enough: structured attention graphs for image classification
|
https://scholar.google.com/scholar?cluster=2997308629773140284&hl=en&as_sdt=0,7
| 2 | 2,021 |
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