title
stringlengths 8
155
| citations_google_scholar
int64 0
28.9k
| conference
stringclasses 5
values | forks
int64 0
46.3k
| issues
int64 0
12.2k
| lastModified
stringlengths 19
26
| repo_url
stringlengths 26
130
| stars
int64 0
75.9k
| title_google_scholar
stringlengths 8
155
| url_google_scholar
stringlengths 75
206
| watchers
int64 0
2.77k
| year
int64 2.02k
2.02k
|
|---|---|---|---|---|---|---|---|---|---|---|---|
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations | 20 | neurips | 0 | 0 | 2023-06-16 15:10:32.896000 | https://github.com/core-robotics-lab/personalized_neural_trees | 3 | Interpretable and personalized apprenticeship scheduling: Learning interpretable scheduling policies from heterogeneous user demonstrations | https://scholar.google.com/scholar?cluster=14212646123511615039&hl=en&as_sdt=0,33 | 4 | 2,020 |
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes | 29 | neurips | 1 | 0 | 2023-06-16 15:10:33.088000 | https://github.com/mxu34/mbrl-gpmm | 25 | Task-agnostic online reinforcement learning with an infinite mixture of gaussian processes | https://scholar.google.com/scholar?cluster=1015317596809472337&hl=en&as_sdt=0,44 | 5 | 2,020 |
Benchmarking Deep Learning Interpretability in Time Series Predictions | 101 | neurips | 16 | 4 | 2023-06-16 15:10:33.280000 | https://github.com/ayaabdelsalam91/TS-Interpretability-Benchmark | 73 | Benchmarking deep learning interpretability in time series predictions | https://scholar.google.com/scholar?cluster=15559999759803172954&hl=en&as_sdt=0,33 | 4 | 2,020 |
Federated Principal Component Analysis | 34 | neurips | 6 | 0 | 2023-06-16 15:10:33.486000 | https://github.com/andylamp/federated_pca | 33 | Federated principal component analysis | https://scholar.google.com/scholar?cluster=5556638479744885012&hl=en&as_sdt=0,33 | 3 | 2,020 |
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks | 26 | neurips | 2 | 0 | 2023-06-16 15:10:33.678000 | https://github.com/alevine0/patchSmoothing | 15 | (De) Randomized smoothing for certifiable defense against patch attacks | https://scholar.google.com/scholar?cluster=7126332887163750199&hl=en&as_sdt=0,14 | 2 | 2,020 |
SMYRF - Efficient Attention using Asymmetric Clustering | 24 | neurips | 5 | 0 | 2023-06-16 15:10:33.870000 | https://github.com/giannisdaras/smyrf | 47 | Smyrf-efficient attention using asymmetric clustering | https://scholar.google.com/scholar?cluster=3416137016272222933&hl=en&as_sdt=0,33 | 3 | 2,020 |
Neutralizing Self-Selection Bias in Sampling for Sortition | 23 | neurips | 0 | 0 | 2023-06-16 15:10:34.063000 | https://github.com/pgoelz/endtoend | 0 | Neutralizing self-selection bias in sampling for sortition | https://scholar.google.com/scholar?cluster=12253485634374856447&hl=en&as_sdt=0,36 | 3 | 2,020 |
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning | 9 | neurips | 1 | 4 | 2023-06-16 15:10:34.286000 | https://github.com/chandar-lab/LoCA | 4 | The LoCA regret: a consistent metric to evaluate model-based behavior in reinforcement learning | https://scholar.google.com/scholar?cluster=1039496506051846849&hl=en&as_sdt=0,5 | 4 | 2,020 |
Bootstrapping neural processes | 19 | neurips | 5 | 1 | 2023-06-16 15:10:34.490000 | https://github.com/juho-lee/bnp | 24 | Bootstrapping neural processes | https://scholar.google.com/scholar?cluster=10569982778154807572&hl=en&as_sdt=0,14 | 2 | 2,020 |
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs | 55 | neurips | 13 | 2 | 2023-06-16 15:10:34.683000 | https://github.com/Stalence/erdos_neu | 27 | Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs | https://scholar.google.com/scholar?cluster=6718013845786623075&hl=en&as_sdt=0,5 | 3 | 2,020 |
Neural Controlled Differential Equations for Irregular Time Series | 255 | neurips | 67 | 3 | 2023-06-16 15:10:34.875000 | https://github.com/patrick-kidger/NeuralCDE | 528 | Neural controlled differential equations for irregular time series | https://scholar.google.com/scholar?cluster=1622654869428402760&hl=en&as_sdt=0,5 | 18 | 2,020 |
Probabilistic Linear Solvers for Machine Learning | 13 | neurips | 0 | 0 | 2023-06-16 15:10:35.068000 | https://github.com/JonathanWenger/probabilistic-linear-solvers-for-ml | 3 | Probabilistic linear solvers for machine learning | https://scholar.google.com/scholar?cluster=1672427431265786249&hl=en&as_sdt=0,34 | 0 | 2,020 |
Multipole Graph Neural Operator for Parametric Partial Differential Equations | 188 | neurips | 64 | 5 | 2023-06-16 15:10:35.280000 | https://github.com/zongyi-li/graph-pde | 188 | Multipole graph neural operator for parametric partial differential equations | https://scholar.google.com/scholar?cluster=13318009799245280479&hl=en&as_sdt=0,31 | 14 | 2,020 |
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images | 143 | neurips | 6 | 2 | 2023-06-16 15:10:35.480000 | https://github.com/thunguyenphuoc/BlockGAN | 42 | Blockgan: Learning 3d object-aware scene representations from unlabelled images | https://scholar.google.com/scholar?cluster=10671381446972867942&hl=en&as_sdt=0,21 | 2 | 2,020 |
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables | 22 | neurips | 3 | 1 | 2023-06-16 15:10:35.673000 | https://github.com/JamesHujy/ELV | 20 | Towards interpretable natural language understanding with explanations as latent variables | https://scholar.google.com/scholar?cluster=922494767816650498&hl=en&as_sdt=0,33 | 2 | 2,020 |
The Mean-Squared Error of Double Q-Learning | 10 | neurips | 1 | 0 | 2023-06-16 15:10:35.865000 | https://github.com/wentaoweng/The-Mean-Squared-Error-of-Double-Q-Learning | 2 | The mean-squared error of double Q-learning | https://scholar.google.com/scholar?cluster=12658517305740432001&hl=en&as_sdt=0,47 | 1 | 2,020 |
Denoising Diffusion Probabilistic Models | 2,458 | neurips | 212 | 17 | 2023-06-16 15:10:36.058000 | https://github.com/hojonathanho/diffusion | 2,132 | Denoising diffusion probabilistic models | https://scholar.google.com/scholar?cluster=622631041436591387&hl=en&as_sdt=0,21 | 20 | 2,020 |
Barking up the right tree: an approach to search over molecule synthesis DAGs | 41 | neurips | 6 | 2 | 2023-06-16 15:10:36.250000 | https://github.com/john-bradshaw/synthesis-dags | 42 | Barking up the right tree: an approach to search over molecule synthesis dags | https://scholar.google.com/scholar?cluster=13448331198377833406&hl=en&as_sdt=0,33 | 1 | 2,020 |
Bandit Samplers for Training Graph Neural Networks | 32 | neurips | 2 | 2 | 2023-06-16 15:10:36.488000 | https://github.com/xavierzw/ogb-geniepath-bs | 3 | Bandit samplers for training graph neural networks | https://scholar.google.com/scholar?cluster=1856670325879954633&hl=en&as_sdt=0,33 | 1 | 2,020 |
Sampling from a k-DPP without looking at all items | 21 | neurips | 47 | 3 | 2023-06-16 15:10:36.681000 | https://github.com/guilgautier/DPPy | 204 | Sampling from a k-DPP without looking at all items | https://scholar.google.com/scholar?cluster=13828986995980178437&hl=en&as_sdt=0,10 | 16 | 2,020 |
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence | 27 | neurips | 3 | 0 | 2023-06-16 15:10:36.874000 | https://github.com/BorgwardtLab/fMRI_Cubical_Persistence | 13 | Uncovering the topology of time-varying fMRI data using cubical persistence | https://scholar.google.com/scholar?cluster=11461528831299808646&hl=en&as_sdt=0,44 | 7 | 2,020 |
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection | 49 | neurips | 3 | 2 | 2023-06-16 15:10:37.067000 | https://github.com/rmcong/CoADNet_NeurIPS20 | 18 | CoADNet: Collaborative aggregation-and-distribution networks for co-salient object detection | https://scholar.google.com/scholar?cluster=8678285635240455625&hl=en&as_sdt=0,50 | 4 | 2,020 |
Regularized linear autoencoders recover the principal components, eventually | 21 | neurips | 1 | 1 | 2023-06-16 15:10:37.274000 | https://github.com/XuchanBao/linear-ae | 14 | Regularized linear autoencoders recover the principal components, eventually | https://scholar.google.com/scholar?cluster=12136029486551136178&hl=en&as_sdt=0,26 | 2 | 2,020 |
UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection | 24 | neurips | 4 | 5 | 2023-06-16 15:10:37.474000 | https://github.com/shenyunhang/UWSOD | 20 | UWSOD: Toward fully-supervised-level capacity weakly supervised object detection | https://scholar.google.com/scholar?cluster=9107656569803100242&hl=en&as_sdt=0,5 | 3 | 2,020 |
Curriculum learning for multilevel budgeted combinatorial problems | 5 | neurips | 0 | 0 | 2023-06-16 15:10:37.666000 | https://github.com/AdelNabli/MCN | 3 | Curriculum learning for multilevel budgeted combinatorial problems | https://scholar.google.com/scholar?cluster=1657047408095143576&hl=en&as_sdt=0,39 | 2 | 2,020 |
Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data | 26 | neurips | 3 | 0 | 2023-06-16 15:10:37.858000 | https://github.com/AudeSportisse/PPCA_MNAR | 1 | Estimation and imputation in probabilistic principal component analysis with missing not at random data | https://scholar.google.com/scholar?cluster=2864178808450174600&hl=en&as_sdt=0,5 | 0 | 2,020 |
Correlation Robust Influence Maximization | 1 | neurips | 0 | 1 | 2023-06-16 15:10:38.051000 | https://github.com/justanothergithubber/corr-im | 7 | Correlation robust influence maximization | https://scholar.google.com/scholar?cluster=5585956565434768987&hl=en&as_sdt=0,5 | 2 | 2,020 |
Neuronal Gaussian Process Regression | 914 | neurips | 0 | 0 | 2023-06-16 15:10:38.243000 | https://github.com/j-friedrich/neuronalGPR | 2 | Deep neural networks as gaussian processes | https://scholar.google.com/scholar?cluster=6709509064500094656&hl=en&as_sdt=0,7 | 1 | 2,020 |
Implicit Distributional Reinforcement Learning | 10 | neurips | 3 | 1 | 2023-06-16 15:10:38.434000 | https://github.com/zhougroup/IDAC | 8 | Implicit distributional reinforcement learning | https://scholar.google.com/scholar?cluster=15829252829546371290&hl=en&as_sdt=0,5 | 2 | 2,020 |
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE | 35 | neurips | 4 | 0 | 2023-06-16 15:10:38.626000 | https://github.com/zhd96/pi-vae | 30 | Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE | https://scholar.google.com/scholar?cluster=619618171802037739&hl=en&as_sdt=0,44 | 2 | 2,020 |
Interior Point Solving for LP-based prediction+optimisation | 48 | neurips | 9 | 2 | 2023-06-16 15:10:38.819000 | https://github.com/JayMan91/NeurIPSIntopt | 14 | Interior point solving for lp-based prediction+ optimisation | https://scholar.google.com/scholar?cluster=1533126665853318342&hl=en&as_sdt=0,33 | 2 | 2,020 |
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks | 25 | neurips | 0 | 0 | 2023-06-16 15:10:39.011000 | https://github.com/romanpogodin/plausible-kernelized-bottleneck | 5 | Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks | https://scholar.google.com/scholar?cluster=18100053392278816994&hl=en&as_sdt=0,43 | 3 | 2,020 |
Understanding the Role of Training Regimes in Continual Learning | 123 | neurips | 11 | 6 | 2023-06-16 15:10:39.202000 | https://github.com/imirzadeh/stable-continual-learning | 71 | Understanding the role of training regimes in continual learning | https://scholar.google.com/scholar?cluster=13304877207545088213&hl=en&as_sdt=0,14 | 6 | 2,020 |
Training Stronger Baselines for Learning to Optimize | 30 | neurips | 7 | 1 | 2023-06-16 15:10:39.395000 | https://github.com/VITA-Group/L2O-Training-Techniques | 25 | Training stronger baselines for learning to optimize | https://scholar.google.com/scholar?cluster=16835534737946083220&hl=en&as_sdt=0,31 | 2 | 2,020 |
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss | 35 | neurips | 3 | 1 | 2023-06-16 15:10:39.589000 | https://github.com/yuruntian/HyNet | 54 | Hynet: Learning local descriptor with hybrid similarity measure and triplet loss | https://scholar.google.com/scholar?cluster=4475373303721859759&hl=en&as_sdt=0,10 | 5 | 2,020 |
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free | 28 | neurips | 9 | 0 | 2023-06-16 15:10:39.791000 | https://github.com/VITA-Group/Once-for-All-Adversarial-Training | 40 | Once-for-all adversarial training: In-situ tradeoff between robustness and accuracy for free | https://scholar.google.com/scholar?cluster=18012050461458046931&hl=en&as_sdt=0,5 | 9 | 2,020 |
Rotated Binary Neural Network | 96 | neurips | 19 | 1 | 2023-06-16 15:10:40.004000 | https://github.com/lmbxmu/RBNN | 75 | Rotated binary neural network | https://scholar.google.com/scholar?cluster=9922290527765380994&hl=en&as_sdt=0,43 | 7 | 2,020 |
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian | 10 | neurips | 1 | 0 | 2023-06-16 15:10:40.197000 | https://github.com/lorenzodallamico/CoDeBetHe.jl | 3 | Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian | https://scholar.google.com/scholar?cluster=926942398566929130&hl=en&as_sdt=0,5 | 1 | 2,020 |
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness | 242 | neurips | 178 | 119 | 2023-06-16 15:10:40.389000 | https://github.com/google/uncertainty-baselines | 1,242 | Simple and principled uncertainty estimation with deterministic deep learning via distance awareness | https://scholar.google.com/scholar?cluster=7900448883391646024&hl=en&as_sdt=0,36 | 20 | 2,020 |
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment | 6 | neurips | 0 | 0 | 2023-06-16 15:10:40.582000 | https://github.com/TavorB/adaptiveSpectral | 0 | Adaptive learning of rank-one models for efficient pairwise sequence alignment | https://scholar.google.com/scholar?cluster=669545011100513558&hl=en&as_sdt=0,46 | 3 | 2,020 |
Hierarchical nucleation in deep neural networks | 15 | neurips | 2 | 0 | 2023-06-16 15:10:40.774000 | https://github.com/diegodoimo/hierarchical_nucleation | 6 | Hierarchical nucleation in deep neural networks | https://scholar.google.com/scholar?cluster=12500887125921827469&hl=en&as_sdt=0,5 | 3 | 2,020 |
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains | 1,009 | neurips | 106 | 12 | 2023-06-16 15:10:40.966000 | https://github.com/tancik/fourier-feature-networks | 1,030 | Fourier features let networks learn high frequency functions in low dimensional domains | https://scholar.google.com/scholar?cluster=14572159759264088577&hl=en&as_sdt=0,10 | 21 | 2,020 |
Graph Geometry Interaction Learning | 56 | neurips | 6 | 4 | 2023-06-16 15:10:41.159000 | https://github.com/CheriseZhu/GIL | 40 | Graph geometry interaction learning | https://scholar.google.com/scholar?cluster=4238397629187106403&hl=en&as_sdt=0,5 | 3 | 2,020 |
Differentiable Augmentation for Data-Efficient GAN Training | 393 | neurips | 171 | 23 | 2023-06-16 15:10:41.350000 | https://github.com/mit-han-lab/data-efficient-gans | 1,192 | Differentiable augmentation for data-efficient gan training | https://scholar.google.com/scholar?cluster=6801864056016037549&hl=en&as_sdt=0,33 | 20 | 2,020 |
Heuristic Domain Adaptation | 28 | neurips | 9 | 0 | 2023-06-16 15:10:41.543000 | https://github.com/cuishuhao/HDA | 56 | Heuristic domain adaptation | https://scholar.google.com/scholar?cluster=5256770897520044696&hl=en&as_sdt=0,6 | 1 | 2,020 |
Learning Certified Individually Fair Representations | 60 | neurips | 2 | 0 | 2023-06-16 15:10:41.734000 | https://github.com/eth-sri/lcifr | 23 | Learning certified individually fair representations | https://scholar.google.com/scholar?cluster=5926392332798964524&hl=en&as_sdt=0,5 | 10 | 2,020 |
Automatic Curriculum Learning through Value Disagreement | 65 | neurips | 11 | 2 | 2023-06-16 15:10:41.926000 | https://github.com/zzyunzhi/vds | 24 | Automatic curriculum learning through value disagreement | https://scholar.google.com/scholar?cluster=6154929220771761601&hl=en&as_sdt=0,47 | 2 | 2,020 |
The NetHack Learning Environment | 94 | neurips | 102 | 16 | 2023-06-16 15:10:42.118000 | https://github.com/facebookresearch/nle | 870 | The nethack learning environment | https://scholar.google.com/scholar?cluster=11088505534192632756&hl=en&as_sdt=0,23 | 29 | 2,020 |
Language and Visual Entity Relationship Graph for Agent Navigation | 76 | neurips | 4 | 0 | 2023-06-16 15:10:42.309000 | https://github.com/YicongHong/Entity-Graph-VLN | 37 | Language and visual entity relationship graph for agent navigation | https://scholar.google.com/scholar?cluster=6555828545880639427&hl=en&as_sdt=0,33 | 3 | 2,020 |
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping | 23 | neurips | 11 | 2 | 2023-06-16 15:10:42.501000 | https://github.com/CherBass/ICAM | 50 | ICAM: interpretable classification via disentangled representations and feature attribution mapping | https://scholar.google.com/scholar?cluster=2236890371359287899&hl=en&as_sdt=0,32 | 4 | 2,020 |
Boosting Adversarial Training with Hypersphere Embedding | 101 | neurips | 13 | 1 | 2023-06-16 15:10:42.693000 | https://github.com/ShawnXYang/AT_HE | 31 | Boosting adversarial training with hypersphere embedding | https://scholar.google.com/scholar?cluster=9611585396722104249&hl=en&as_sdt=0,36 | 3 | 2,020 |
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs | 406 | neurips | 15 | 1 | 2023-06-16 15:10:42.885000 | https://github.com/GemsLab/H2GCN | 77 | Beyond homophily in graph neural networks: Current limitations and effective designs | https://scholar.google.com/scholar?cluster=13096699314940165476&hl=en&as_sdt=0,5 | 4 | 2,020 |
Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent | 10 | neurips | 1 | 0 | 2023-06-16 15:10:43.077000 | https://github.com/sskoul/ID2216 | 4 | Efficient online learning of optimal rankings: Dimensionality reduction via gradient descent | https://scholar.google.com/scholar?cluster=17654222550374080796&hl=en&as_sdt=0,48 | 1 | 2,020 |
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification | 39 | neurips | 102 | 34 | 2023-06-16 15:10:43.269000 | https://github.com/VLL-HD/FrEIA | 663 | Training normalizing flows with the information bottleneck for competitive generative classification | https://scholar.google.com/scholar?cluster=7085738876441578622&hl=en&as_sdt=0,49 | 20 | 2,020 |
Deep Statistical Solvers | 14 | neurips | 3 | 1 | 2023-06-16 15:10:43.466000 | https://github.com/bdonon/DeepStatisticalSolvers | 4 | Deep statistical solvers | https://scholar.google.com/scholar?cluster=5761359200414766377&hl=en&as_sdt=0,5 | 1 | 2,020 |
Distributionally Robust Parametric Maximum Likelihood Estimation | 9 | neurips | 0 | 0 | 2023-06-16 15:10:43.658000 | https://github.com/angelosgeorghiou/DR-Parametric-MLE | 2 | Distributionally robust parametric maximum likelihood estimation | https://scholar.google.com/scholar?cluster=11917985486247358648&hl=en&as_sdt=0,33 | 1 | 2,020 |
Deep Transformation-Invariant Clustering | 26 | neurips | 10 | 0 | 2023-06-16 15:10:43.850000 | https://github.com/monniert/dti-clustering | 67 | Deep transformation-invariant clustering | https://scholar.google.com/scholar?cluster=10717088515136058764&hl=en&as_sdt=0,5 | 3 | 2,020 |
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree | 17 | neurips | 0 | 0 | 2023-06-16 15:10:44.043000 | https://github.com/functionadvanced/basis_pursuit_code | 0 | Overfitting can be harmless for basis pursuit, but only to a degree | https://scholar.google.com/scholar?cluster=12884966030435629698&hl=en&as_sdt=0,5 | 2 | 2,020 |
Improving Generalization in Reinforcement Learning with Mixture Regularization | 64 | neurips | 8 | 1 | 2023-06-16 15:10:44.234000 | https://github.com/kaixin96/mixreg | 30 | Improving generalization in reinforcement learning with mixture regularization | https://scholar.google.com/scholar?cluster=3278230157932570215&hl=en&as_sdt=0,5 | 3 | 2,020 |
Learning from Aggregate Observations | 21 | neurips | 0 | 0 | 2023-06-16 15:10:44.427000 | https://github.com/YivanZhang/lio | 9 | Learning from aggregate observations | https://scholar.google.com/scholar?cluster=17146709459337763149&hl=en&as_sdt=0,33 | 2 | 2,020 |
Subgraph Neural Networks | 78 | neurips | 31 | 14 | 2023-06-16 15:10:44.619000 | https://github.com/mims-harvard/SubGNN | 155 | Subgraph neural networks | https://scholar.google.com/scholar?cluster=12519651667437268024&hl=en&as_sdt=0,44 | 9 | 2,020 |
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search | 260 | neurips | 146 | 45 | 2023-06-16 15:10:44.813000 | https://github.com/jaywalnut310/glow-tts | 554 | Glow-tts: A generative flow for text-to-speech via monotonic alignment search | https://scholar.google.com/scholar?cluster=4995990667849283087&hl=en&as_sdt=0,10 | 19 | 2,020 |
Novelty Search in Representational Space for Sample Efficient Exploration | 32 | neurips | 2 | 2 | 2023-06-16 15:10:45.008000 | https://github.com/taodav/nsrs | 11 | Novelty search in representational space for sample efficient exploration | https://scholar.google.com/scholar?cluster=15188964487009178721&hl=en&as_sdt=0,31 | 2 | 2,020 |
Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice | 25 | neurips | 0 | 0 | 2023-06-16 15:10:45.202000 | https://github.com/ShufanWangBGM/OAfMSSRwMLA | 1 | Online algorithms for multi-shop ski rental with machine learned advice | https://scholar.google.com/scholar?cluster=16607821741984068758&hl=en&as_sdt=0,11 | 1 | 2,020 |
Learning Invariants through Soft Unification | 7 | neurips | 1 | 0 | 2023-06-16 15:10:45.395000 | https://github.com/nuric/softuni | 4 | Learning invariants through soft unification | https://scholar.google.com/scholar?cluster=3931986727564031878&hl=en&as_sdt=0,5 | 1 | 2,020 |
Variational Bayesian Monte Carlo with Noisy Likelihoods | 26 | neurips | 2 | 0 | 2023-06-16 15:10:45.588000 | https://github.com/lacerbi/infbench | 3 | Variational bayesian monte carlo with noisy likelihoods | https://scholar.google.com/scholar?cluster=10498124267733273591&hl=en&as_sdt=0,5 | 5 | 2,020 |
Adversarial Distributional Training for Robust Deep Learning | 79 | neurips | 8 | 1 | 2023-06-16 15:10:45.781000 | https://github.com/dongyp13/Adversarial-Distributional-Training | 58 | Adversarial distributional training for robust deep learning | https://scholar.google.com/scholar?cluster=4714059054130702686&hl=en&as_sdt=0,5 | 1 | 2,020 |
Greedy inference with structure-exploiting lazy maps | 34 | neurips | 1 | 7 | 2023-06-16 15:10:45.973000 | https://github.com/MichaelCBrennan/lazymaps | 1 | Greedy inference with structure-exploiting lazy maps | https://scholar.google.com/scholar?cluster=12098930486710559887&hl=en&as_sdt=0,10 | 2 | 2,020 |
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning | 37 | neurips | 32 | 17 | 2023-06-16 15:10:46.166000 | https://github.com/snuspl/nimble | 239 | Nimble: Lightweight and parallel gpu task scheduling for deep learning | https://scholar.google.com/scholar?cluster=7176715468062683010&hl=en&as_sdt=0,47 | 10 | 2,020 |
Finding the Homology of Decision Boundaries with Active Learning | 13 | neurips | 0 | 0 | 2023-06-16 15:10:46.357000 | https://github.com/wayne0908/Active-Learning-Homology | 2 | Finding the homology of decision boundaries with active learning | https://scholar.google.com/scholar?cluster=16953441847668604826&hl=en&as_sdt=0,41 | 2 | 2,020 |
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars | 17 | neurips | 0 | 0 | 2023-06-16 15:10:46.550000 | https://github.com/Zoesgithub/MNCE-RL | 6 | Reinforced molecular optimization with neighborhood-controlled grammars | https://scholar.google.com/scholar?cluster=5211013872342896595&hl=en&as_sdt=0,5 | 2 | 2,020 |
Certified Defense to Image Transformations via Randomized Smoothing | 41 | neurips | 1 | 0 | 2023-06-16 15:10:46.742000 | https://github.com/eth-sri/transformation-smoothing | 3 | Certified defense to image transformations via randomized smoothing | https://scholar.google.com/scholar?cluster=9373644649920608208&hl=en&as_sdt=0,5 | 8 | 2,020 |
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks | 29 | neurips | 3 | 0 | 2023-06-16 15:10:46.934000 | https://github.com/RobustGraph/RoboGraph | 9 | Certified robustness of graph convolution networks for graph classification under topological attacks | https://scholar.google.com/scholar?cluster=8395286682706237378&hl=en&as_sdt=0,5 | 3 | 2,020 |
Zero-Resource Knowledge-Grounded Dialogue Generation | 44 | neurips | 9 | 6 | 2023-06-16 15:10:47.126000 | https://github.com/nlpxucan/ZRKGC | 36 | Zero-resource knowledge-grounded dialogue generation | https://scholar.google.com/scholar?cluster=6981655446810272506&hl=en&as_sdt=0,39 | 4 | 2,020 |
Targeted Adversarial Perturbations for Monocular Depth Prediction | 29 | neurips | 3 | 0 | 2023-06-16 15:10:47.319000 | https://github.com/alexklwong/targeted-adversarial-perturbations-monocular-depth | 12 | Targeted adversarial perturbations for monocular depth prediction | https://scholar.google.com/scholar?cluster=16134290645127049160&hl=en&as_sdt=0,34 | 3 | 2,020 |
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties | 6 | neurips | 1 | 0 | 2023-06-16 15:10:47.510000 | https://github.com/boschresearch/Structured_DGP | 3 | Beyond the mean-field: Structured deep Gaussian processes improve the predictive uncertainties | https://scholar.google.com/scholar?cluster=8221968686369534160&hl=en&as_sdt=0,5 | 4 | 2,020 |
PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals | 16 | neurips | 3 | 0 | 2023-06-16 15:10:47.702000 | https://github.com/henrycharlesworth/PlanGAN | 17 | Plangan: Model-based planning with sparse rewards and multiple goals | https://scholar.google.com/scholar?cluster=16931214957382065939&hl=en&as_sdt=0,33 | 1 | 2,020 |
Bad Global Minima Exist and SGD Can Reach Them | 57 | neurips | 2 | 0 | 2023-06-16 15:10:47.894000 | https://github.com/chao1224/BadGlobalMinima | 9 | Bad global minima exist and sgd can reach them | https://scholar.google.com/scholar?cluster=4377222193710581368&hl=en&as_sdt=0,33 | 1 | 2,020 |
A Closer Look at Accuracy vs. Robustness | 196 | neurips | 14 | 0 | 2023-06-16 15:10:48.086000 | https://github.com/yangarbiter/robust-local-lipschitz | 82 | A closer look at accuracy vs. robustness | https://scholar.google.com/scholar?cluster=13806860877256503450&hl=en&as_sdt=0,5 | 7 | 2,020 |
Spin-Weighted Spherical CNNs | 46 | neurips | 1 | 0 | 2023-06-16 15:10:48.286000 | https://github.com/daniilidis-group/swscnn | 21 | Spin-weighted spherical cnns | https://scholar.google.com/scholar?cluster=13743708889227032297&hl=en&as_sdt=0,11 | 8 | 2,020 |
Baxter Permutation Process | 10 | neurips | 1 | 0 | 2023-06-16 15:10:48.485000 | https://github.com/nttcslab/baxter-permutation-process | 6 | Baxter permutation process | https://scholar.google.com/scholar?cluster=290903901363151335&hl=en&as_sdt=0,5 | 5 | 2,020 |
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation | 36 | neurips | 744 | 214 | 2023-06-16 15:10:48.681000 | https://github.com/awslabs/autogluon | 5,850 | Fast, accurate, and simple models for tabular data via augmented distillation | https://scholar.google.com/scholar?cluster=15277756439655303211&hl=en&as_sdt=0,47 | 91 | 2,020 |
Approximate Cross-Validation for Structured Models | 12 | neurips | 0 | 0 | 2023-06-16 15:10:48.876000 | https://github.com/SoumyaTGhosh/structured-infinitesimal-jackknife | 1 | Approximate cross-validation for structured models | https://scholar.google.com/scholar?cluster=5677418794939060287&hl=en&as_sdt=0,5 | 5 | 2,020 |
Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation | 15 | neurips | 8 | 3 | 2023-06-16 15:10:49.069000 | https://github.com/sajadn/Exemplar-VAE | 65 | Exemplar vae: Linking generative models, nearest neighbor retrieval, and data augmentation | https://scholar.google.com/scholar?cluster=1402621202580730115&hl=en&as_sdt=0,34 | 3 | 2,020 |
Debiased Contrastive Learning | 340 | neurips | 33 | 3 | 2023-06-16 15:10:49.271000 | https://github.com/chingyaoc/DCL | 263 | Debiased contrastive learning | https://scholar.google.com/scholar?cluster=9278834174999362411&hl=en&as_sdt=0,5 | 8 | 2,020 |
UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree | 45 | neurips | 6 | 3 | 2023-06-16 15:10:49.495000 | https://github.com/kacperkan/ucsgnet | 32 | UCSG-NET-unsupervised discovering of constructive solid geometry tree | https://scholar.google.com/scholar?cluster=7447193649830937821&hl=en&as_sdt=0,11 | 1 | 2,020 |
COT-GAN: Generating Sequential Data via Causal Optimal Transport | 59 | neurips | 10 | 3 | 2023-06-16 15:10:49.688000 | https://github.com/tianlinxu312/cot-gan | 28 | Cot-gan: Generating sequential data via causal optimal transport | https://scholar.google.com/scholar?cluster=2786319985224529897&hl=en&as_sdt=0,5 | 0 | 2,020 |
Understanding spiking networks through convex optimization | 13 | neurips | 7 | 0 | 2023-06-16 15:10:49.881000 | https://github.com/machenslab/spikes | 14 | Understanding spiking networks through convex optimization | https://scholar.google.com/scholar?cluster=13728762608347936383&hl=en&as_sdt=0,5 | 1 | 2,020 |
Large-Scale Methods for Distributionally Robust Optimization | 105 | neurips | 6 | 1 | 2023-06-16 15:10:50.074000 | https://github.com/daniellevy/fast-dro | 43 | Large-scale methods for distributionally robust optimization | https://scholar.google.com/scholar?cluster=4841990441300957739&hl=en&as_sdt=0,18 | 4 | 2,020 |
Adversarial Example Games | 41 | neurips | 6 | 1 | 2023-06-16 15:10:50.269000 | https://github.com/joeybose/Adversarial-Example-Games | 24 | Adversarial example games | https://scholar.google.com/scholar?cluster=4037988847325628992&hl=en&as_sdt=0,5 | 4 | 2,020 |
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts | 20 | neurips | 0 | 0 | 2023-06-16 15:10:50.478000 | https://github.com/leoozy/JointRD_Neurips2020 | 1 | Residual distillation: Towards portable deep neural networks without shortcuts | https://scholar.google.com/scholar?cluster=4325972833602775025&hl=en&as_sdt=0,5 | 1 | 2,020 |
Further Analysis of Outlier Detection with Deep Generative Models | 31 | neurips | 2 | 0 | 2023-06-16 15:10:50.670000 | https://github.com/thu-ml/ood-dgm | 8 | Further analysis of outlier detection with deep generative models | https://scholar.google.com/scholar?cluster=9058630234791749340&hl=en&as_sdt=0,5 | 8 | 2,020 |
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning | 12 | neurips | 2 | 0 | 2023-06-16 15:10:50.862000 | https://github.com/Mehooz/BIRD_code | 12 | Bridging imagination and reality for model-based deep reinforcement learning | https://scholar.google.com/scholar?cluster=1362648394458598270&hl=en&as_sdt=0,5 | 2 | 2,020 |
Adversarial Learning for Robust Deep Clustering | 56 | neurips | 2 | 2 | 2023-06-16 15:10:51.054000 | https://github.com/xdxuyang/ALRDC | 13 | Adversarial learning for robust deep clustering | https://scholar.google.com/scholar?cluster=10569924986373874642&hl=en&as_sdt=0,34 | 1 | 2,020 |
Learning Mutational Semantics | 4 | neurips | 2 | 0 | 2023-06-16 15:10:51.247000 | https://github.com/brianhie/mutational-semantics-neurips2020 | 8 | Learning mutational semantics | https://scholar.google.com/scholar?cluster=4282139572655553972&hl=en&as_sdt=0,5 | 3 | 2,020 |
Learning to Learn Variational Semantic Memory | 14 | neurips | 2 | 3 | 2023-06-16 15:10:51.439000 | https://github.com/YDU-uva/VSM | 5 | Learning to learn variational semantic memory | https://scholar.google.com/scholar?cluster=6158298679245013068&hl=en&as_sdt=0,39 | 2 | 2,020 |
Finer Metagenomic Reconstruction via Biodiversity Optimization | 0 | neurips | 0 | 0 | 2023-06-16 15:10:51.630000 | https://github.com/dkoslicki/MinimizeBiologicalDiversity | 0 | Finer metagenomic reconstruction via biodiversity optimization | https://scholar.google.com/scholar?cluster=7553946638597085018&hl=en&as_sdt=0,44 | 2 | 2,020 |
Self-Paced Deep Reinforcement Learning | 30 | neurips | 2 | 3 | 2023-06-16 15:10:51.822000 | https://github.com/psclklnk/spdl | 25 | Self-paced deep reinforcement learning | https://scholar.google.com/scholar?cluster=12390741012444342538&hl=en&as_sdt=0,31 | 1 | 2,020 |
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples | 37 | neurips | 3 | 0 | 2023-06-16 15:10:52.014000 | https://github.com/jayjaynandy/maximize-representation-gap | 7 | Towards maximizing the representation gap between in-domain & out-of-distribution examples | https://scholar.google.com/scholar?cluster=9854712856118279269&hl=en&as_sdt=0,44 | 2 | 2,020 |
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks | 140 | neurips | 13 | 5 | 2023-06-16 15:10:52.206000 | https://github.com/mims-harvard/GNNGuard | 49 | Gnnguard: Defending graph neural networks against adversarial attacks | https://scholar.google.com/scholar?cluster=16210304984392782174&hl=en&as_sdt=0,5 | 5 | 2,020 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.