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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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