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Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure | 45 | icml | 21 | 1 | 2023-06-17 03:57:39.829000 | https://github.com/google/madi | 62 | Interpretable, multidimensional, multimodal anomaly detection with negative sampling for detection of device failure | https://scholar.google.com/scholar?cluster=3739930474828740815&hl=en&as_sdt=0,33 | 10 | 2,020 |
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation | 10 | icml | 0 | 1 | 2023-06-17 03:57:40.031000 | https://github.com/GeorgiosSmyrnis/multiclass_minimization_icml2020 | 1 | Multiclass neural network minimization via tropical newton polytope approximation | https://scholar.google.com/scholar?cluster=2547708256108168456&hl=en&as_sdt=0,31 | 2 | 2,020 |
Bridging the Gap Between f-GANs and Wasserstein GANs | 36 | icml | 4 | 0 | 2023-06-17 03:57:40.234000 | https://github.com/ermongroup/f-wgan | 14 | Bridging the gap between f-gans and wasserstein gans | https://scholar.google.com/scholar?cluster=15572821134317773979&hl=en&as_sdt=0,44 | 6 | 2,020 |
Hypernetwork approach to generating point clouds | 25 | icml | 4 | 1 | 2023-06-17 03:57:40.435000 | https://github.com/gmum/3d-point-clouds-HyperCloud | 26 | Hypernetwork approach to generating point clouds | https://scholar.google.com/scholar?cluster=1381462816428622645&hl=en&as_sdt=0,10 | 7 | 2,020 |
Which Tasks Should Be Learned Together in Multi-task Learning? | 333 | icml | 13 | 7 | 2023-06-17 03:57:40.637000 | https://github.com/tstandley/taskgrouping | 89 | Which tasks should be learned together in multi-task learning? | https://scholar.google.com/scholar?cluster=11792880914150945674&hl=en&as_sdt=0,5 | 2 | 2,020 |
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information | 8 | icml | 1 | 0 | 2023-06-17 03:57:40.839000 | https://github.com/karlstratos/ammi | 11 | Learning discrete structured representations by adversarially maximizing mutual information | https://scholar.google.com/scholar?cluster=10269620235757517949&hl=en&as_sdt=0,10 | 2 | 2,020 |
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks | 101 | icml | 0 | 0 | 2023-06-17 03:57:41.041000 | https://github.com/davidstutz/icml2020-confidence-calibrated-adversarial-training | 9 | Confidence-calibrated adversarial training: Generalizing to unseen attacks | https://scholar.google.com/scholar?cluster=14154958119332735093&hl=en&as_sdt=0,5 | 4 | 2,020 |
Adaptive Estimator Selection for Off-Policy Evaluation | 23 | icml | 2 | 0 | 2023-06-17 03:57:41.249000 | https://github.com/VowpalWabbit/slope-experiments | 3 | Adaptive estimator selection for off-policy evaluation | https://scholar.google.com/scholar?cluster=578911518697866009&hl=en&as_sdt=0,49 | 4 | 2,020 |
Multi-Agent Routing Value Iteration Network | 33 | icml | 14 | 0 | 2023-06-17 03:57:41.451000 | https://github.com/uber/MARVIN | 50 | Multi-agent routing value iteration network | https://scholar.google.com/scholar?cluster=16960600258669760447&hl=en&as_sdt=0,5 | 5 | 2,020 |
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery | 18 | icml | 2 | 0 | 2023-06-17 03:57:41.652000 | https://github.com/tagas/bQCD | 2 | Distinguishing cause from effect using quantiles: Bivariate quantile causal discovery | https://scholar.google.com/scholar?cluster=15617920136874649205&hl=en&as_sdt=0,5 | 1 | 2,020 |
DropNet: Reducing Neural Network Complexity via Iterative Pruning | 25 | icml | 7 | 0 | 2023-06-17 03:57:41.854000 | https://github.com/tanchongmin/DropNet | 14 | Dropnet: Reducing neural network complexity via iterative pruning | https://scholar.google.com/scholar?cluster=5847979658470311835&hl=en&as_sdt=0,5 | 1 | 2,020 |
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies | 13 | icml | 3 | 0 | 2023-06-17 03:57:42.056000 | https://github.com/MLD3/RL-Set-Valued-Policy | 12 | Clinician-in-the-loop decision making: Reinforcement learning with near-optimal set-valued policies | https://scholar.google.com/scholar?cluster=2625470057202017453&hl=en&as_sdt=0,5 | 2 | 2,020 |
Variational Imitation Learning with Diverse-quality Demonstrations | 26 | icml | 3 | 0 | 2023-06-17 03:57:42.258000 | https://github.com/voot-t/vild_code | 13 | Variational imitation learning with diverse-quality demonstrations | https://scholar.google.com/scholar?cluster=17459982405311544718&hl=en&as_sdt=0,5 | 2 | 2,020 |
Inductive Relation Prediction by Subgraph Reasoning | 213 | icml | 50 | 9 | 2023-06-17 03:57:42.460000 | https://github.com/kkteru/grail | 166 | Inductive relation prediction by subgraph reasoning | https://scholar.google.com/scholar?cluster=14042316464156946923&hl=en&as_sdt=0,33 | 4 | 2,020 |
Few-shot Domain Adaptation by Causal Mechanism Transfer | 71 | icml | 13 | 41 | 2023-06-17 03:57:42.662000 | https://github.com/takeshi-teshima/few-shot-domain-adaptation-by-causal-mechanism-transfer | 34 | Few-shot domain adaptation by causal mechanism transfer | https://scholar.google.com/scholar?cluster=15173839596303603057&hl=en&as_sdt=0,5 | 3 | 2,020 |
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions | 22 | icml | 1 | 0 | 2023-06-17 03:57:42.864000 | https://github.com/ds2p/dea | 2 | Convolutional dictionary learning based auto-encoders for natural exponential-family distributions | https://scholar.google.com/scholar?cluster=17717998361857407154&hl=en&as_sdt=0,47 | 3 | 2,020 |
Choice Set Optimization Under Discrete Choice Models of Group Decisions | 6 | icml | 1 | 0 | 2023-06-17 03:57:43.086000 | https://github.com/tomlinsonk/choice-set-opt | 9 | Choice set optimization under discrete choice models of group decisions | https://scholar.google.com/scholar?cluster=9509628446146574324&hl=en&as_sdt=0,5 | 5 | 2,020 |
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics | 69 | icml | 12 | 6 | 2023-06-17 03:57:43.288000 | https://github.com/KrishnaswamyLab/TrajectoryNet | 72 | Trajectorynet: A dynamic optimal transport network for modeling cellular dynamics | https://scholar.google.com/scholar?cluster=13927969516648778690&hl=en&as_sdt=0,33 | 8 | 2,020 |
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | 29 | icml | 9 | 3 | 2023-06-17 03:57:43.490000 | https://github.com/tgcsaba/GPSig | 37 | Bayesian learning from sequential data using gaussian processes with signature covariances | https://scholar.google.com/scholar?cluster=5665279431482036771&hl=en&as_sdt=0,33 | 3 | 2,020 |
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations | 75 | icml | 5 | 0 | 2023-06-17 03:57:43.693000 | https://github.com/ftramer/Excessive-Invariance | 25 | Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations | https://scholar.google.com/scholar?cluster=12838198146332206865&hl=en&as_sdt=0,47 | 6 | 2,020 |
Bayesian Differential Privacy for Machine Learning | 58 | icml | 4 | 0 | 2023-06-17 03:57:43.895000 | https://github.com/AlekseiTriastcyn/bayesian-differential-privacy | 16 | Bayesian differential privacy for machine learning | https://scholar.google.com/scholar?cluster=2037504457051740866&hl=en&as_sdt=0,5 | 2 | 2,020 |
Single Point Transductive Prediction | 2 | icml | 0 | 0 | 2023-06-17 03:57:44.098000 | https://github.com/nileshtrip/SPTransducPredCode | 3 | Single point transductive prediction | https://scholar.google.com/scholar?cluster=4391877212575021385&hl=en&as_sdt=0,36 | 2 | 2,020 |
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks | 111 | icml | 2 | 0 | 2023-06-17 03:57:44.299000 | https://github.com/MadryLab/ImageNetMultiLabel | 28 | From imagenet to image classification: Contextualizing progress on benchmarks | https://scholar.google.com/scholar?cluster=17622651192510371827&hl=en&as_sdt=0,5 | 9 | 2,020 |
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network | 11 | icml | 0 | 0 | 2023-06-17 03:57:44.502000 | https://github.com/TuKo/dRNN | 5 | Approximating stacked and bidirectional recurrent architectures with the delayed recurrent neural network | https://scholar.google.com/scholar?cluster=1436978091908679295&hl=en&as_sdt=0,14 | 3 | 2,020 |
Uncertainty Estimation Using a Single Deep Deterministic Neural Network | 304 | icml | 32 | 2 | 2023-06-17 03:57:44.703000 | https://github.com/y0ast/deterministic-uncertainty-quantification | 239 | Uncertainty estimation using a single deep deterministic neural network | https://scholar.google.com/scholar?cluster=16222536793080297152&hl=en&as_sdt=0,32 | 7 | 2,020 |
Born-Again Tree Ensembles | 50 | icml | 5 | 6 | 2023-06-17 03:57:44.937000 | https://github.com/vidalt/BA-Trees | 56 | Born-again tree ensembles | https://scholar.google.com/scholar?cluster=16560127278940498393&hl=en&as_sdt=0,5 | 4 | 2,020 |
New Oracle-Efficient Algorithms for Private Synthetic Data Release | 45 | icml | 2 | 0 | 2023-06-17 03:57:45.141000 | https://github.com/giusevtr/fem | 7 | New oracle-efficient algorithms for private synthetic data release | https://scholar.google.com/scholar?cluster=18163576365323257065&hl=en&as_sdt=0,36 | 2 | 2,020 |
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space | 275 | icml | 53 | 16 | 2023-06-17 03:57:45.343000 | https://github.com/anvoynov/GANLatentDiscovery | 406 | Unsupervised discovery of interpretable directions in the gan latent space | https://scholar.google.com/scholar?cluster=13408893088338762457&hl=en&as_sdt=0,5 | 10 | 2,020 |
Safe Reinforcement Learning in Constrained Markov Decision Processes | 87 | icml | 8 | 0 | 2023-06-17 03:57:45.552000 | https://github.com/akifumi-wachi-4/safe_near_optimal_mdp | 38 | Safe reinforcement learning in constrained Markov decision processes | https://scholar.google.com/scholar?cluster=13376476556539351032&hl=en&as_sdt=0,44 | 1 | 2,020 |
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching | 76 | icml | 7 | 0 | 2023-06-17 03:57:45.755000 | https://github.com/PeisongWang/BitSplit | 38 | Towards accurate post-training network quantization via bit-split and stitching | https://scholar.google.com/scholar?cluster=958273940309910649&hl=en&as_sdt=0,5 | 2 | 2,020 |
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles | 137 | icml | 32 | 14 | 2023-06-17 03:57:45.958000 | https://github.com/TonghanWang/ROMA | 136 | Roma: Multi-agent reinforcement learning with emergent roles | https://scholar.google.com/scholar?cluster=10158010923788252116&hl=en&as_sdt=0,5 | 4 | 2,020 |
Continuously Indexed Domain Adaptation | 77 | icml | 18 | 3 | 2023-06-17 03:57:46.161000 | https://github.com/hehaodele/CIDA | 108 | Continuously indexed domain adaptation | https://scholar.google.com/scholar?cluster=3441708260891083426&hl=en&as_sdt=0,33 | 6 | 2,020 |
Frustratingly Simple Few-Shot Object Detection | 306 | icml | 215 | 56 | 2023-06-17 03:57:46.362000 | https://github.com/ucbdrive/few-shot-object-detection | 961 | Frustratingly simple few-shot object detection | https://scholar.google.com/scholar?cluster=13847197306360708920&hl=en&as_sdt=0,5 | 28 | 2,020 |
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere | 946 | icml | 34 | 0 | 2023-06-17 03:57:46.578000 | https://github.com/SsnL/align_uniform | 354 | Understanding contrastive representation learning through alignment and uniformity on the hypersphere | https://scholar.google.com/scholar?cluster=5122266742982340747&hl=en&as_sdt=0,3 | 11 | 2,020 |
Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions | 73 | icml | 51 | 5 | 2023-06-17 03:57:46.781000 | https://github.com/uber-research/poet | 233 | Enhanced poet: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions | https://scholar.google.com/scholar?cluster=17583648324422024748&hl=en&as_sdt=0,44 | 15 | 2,020 |
Haar Graph Pooling | 62 | icml | 5 | 6 | 2023-06-17 03:57:46.983000 | https://github.com/YuGuangWang/HaarPool | 9 | Haar graph pooling | https://scholar.google.com/scholar?cluster=196487871230108211&hl=en&as_sdt=0,34 | 2 | 2,020 |
Deep Streaming Label Learning | 29 | icml | 2 | 1 | 2023-06-17 03:57:47.187000 | https://github.com/DSLLcode/DSLL | 5 | Deep streaming label learning | https://scholar.google.com/scholar?cluster=13962185185630699460&hl=en&as_sdt=0,5 | 1 | 2,020 |
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates | 7 | icml | 0 | 0 | 2023-06-17 03:57:47.389000 | https://github.com/BoXHED/BoXHED1.0 | 6 | BoXHED: Boosted eXact hazard estimator with dynamic covariates | https://scholar.google.com/scholar?cluster=4269847056654945250&hl=en&as_sdt=0,3 | 1 | 2,020 |
Optimizing Data Usage via Differentiable Rewards | 41 | icml | 0 | 0 | 2023-06-17 03:57:47.591000 | https://github.com/cindyxinyiwang/DataSelection | 2 | Optimizing data usage via differentiable rewards | https://scholar.google.com/scholar?cluster=4407582239871274683&hl=en&as_sdt=0,11 | 1 | 2,020 |
Loss Function Search for Face Recognition | 45 | icml | 8 | 5 | 2023-06-17 03:57:47.794000 | https://github.com/tiandunx/loss_function_search | 37 | Loss function search for face recognition | https://scholar.google.com/scholar?cluster=4661570129688704480&hl=en&as_sdt=0,31 | 3 | 2,020 |
Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling | 20 | icml | 6 | 2 | 2023-06-17 03:57:47.996000 | https://github.com/AutumnWu/Streamlined-Off-Policy-Learning | 18 | Striving for simplicity and performance in off-policy DRL: Output normalization and non-uniform sampling | https://scholar.google.com/scholar?cluster=11197578875286418478&hl=en&as_sdt=0,5 | 4 | 2,020 |
Thompson Sampling via Local Uncertainty | 16 | icml | 2 | 1 | 2023-06-17 03:57:48.199000 | https://github.com/Zhendong-Wang/Thompson-Sampling-via-Local-Uncertainty | 3 | Thompson sampling via local uncertainty | https://scholar.google.com/scholar?cluster=15106467344904481899&hl=en&as_sdt=0,10 | 1 | 2,020 |
The Implicit and Explicit Regularization Effects of Dropout | 91 | icml | 2 | 0 | 2023-06-17 03:57:48.400000 | https://github.com/cwein3/dropout-analytical | 4 | The implicit and explicit regularization effects of dropout | https://scholar.google.com/scholar?cluster=7315580872864689276&hl=en&as_sdt=0,44 | 2 | 2,020 |
How Good is the Bayes Posterior in Deep Neural Networks Really? | 274 | icml | 7,322 | 1,026 | 2023-06-17 03:57:48.601000 | https://github.com/google-research/google-research | 29,791 | How good is the bayes posterior in deep neural networks really? | https://scholar.google.com/scholar?cluster=11185773961293705941&hl=en&as_sdt=0,36 | 727 | 2,020 |
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes | 12 | icml | 12 | 2 | 2023-06-17 03:57:48.804000 | https://github.com/AaltoML/kalman-jax | 86 | State space expectation propagation: Efficient inference schemes for temporal Gaussian processes | https://scholar.google.com/scholar?cluster=3634962580178312612&hl=en&as_sdt=0,5 | 10 | 2,020 |
Efficiently sampling functions from Gaussian process posteriors | 107 | icml | 16 | 0 | 2023-06-17 03:57:49.006000 | https://github.com/j-wilson/GPflowSampling | 57 | Efficiently sampling functions from Gaussian process posteriors | https://scholar.google.com/scholar?cluster=15698699983460471132&hl=en&as_sdt=0,39 | 3 | 2,020 |
Obtaining Adjustable Regularization for Free via Iterate Averaging | 4 | icml | 1 | 0 | 2023-06-17 03:57:49.208000 | https://github.com/uuujf/IterAvg | 3 | Obtaining adjustable regularization for free via iterate averaging | https://scholar.google.com/scholar?cluster=8907876046676470481&hl=en&as_sdt=0,23 | 1 | 2,020 |
DeltaGrad: Rapid retraining of machine learning models | 94 | icml | 1 | 1 | 2023-06-17 03:57:49.410000 | https://github.com/thuwuyinjun/DeltaGrad | 19 | Deltagrad: Rapid retraining of machine learning models | https://scholar.google.com/scholar?cluster=5989632010826923243&hl=en&as_sdt=0,5 | 1 | 2,020 |
On the Noisy Gradient Descent that Generalizes as SGD | 66 | icml | 2 | 0 | 2023-06-17 03:57:49.612000 | https://github.com/uuujf/MultiNoise | 4 | On the noisy gradient descent that generalizes as sgd | https://scholar.google.com/scholar?cluster=7998772173539396288&hl=en&as_sdt=0,5 | 2 | 2,020 |
Stronger and Faster Wasserstein Adversarial Attacks | 18 | icml | 9 | 1 | 2023-06-17 03:57:49.813000 | https://github.com/watml/fast-wasserstein-adversarial | 21 | Stronger and faster wasserstein adversarial attacks | https://scholar.google.com/scholar?cluster=5877536134148697532&hl=en&as_sdt=0,31 | 5 | 2,020 |
On the Generalization Effects of Linear Transformations in Data Augmentation | 57 | icml | 6 | 3 | 2023-06-17 03:57:50.016000 | https://github.com/SenWu/dauphin | 28 | On the generalization effects of linear transformations in data augmentation | https://scholar.google.com/scholar?cluster=18304073580439494047&hl=en&as_sdt=0,5 | 5 | 2,020 |
Generative Flows with Matrix Exponential | 4 | icml | 0 | 0 | 2023-06-17 03:57:50.218000 | https://github.com/changyi7231/MEF | 10 | Generative flows with matrix exponential | https://scholar.google.com/scholar?cluster=5544738884567808407&hl=en&as_sdt=0,5 | 1 | 2,020 |
Maximum-and-Concatenation Networks | 1 | icml | 0 | 0 | 2023-06-17 03:57:50.422000 | https://github.com/XingyuXie/Maximum-and-Concatenation-Networks | 3 | Maximum-and-concatenation networks | https://scholar.google.com/scholar?cluster=6894098060248560789&hl=en&as_sdt=0,24 | 3 | 2,020 |
Zeno++: Robust Fully Asynchronous SGD | 74 | icml | 2 | 0 | 2023-06-17 03:57:50.623000 | https://github.com/xcgoner/iclr2020_zeno_async | 11 | Zeno++: Robust fully asynchronous SGD | https://scholar.google.com/scholar?cluster=6498141081528459239&hl=en&as_sdt=0,44 | 3 | 2,020 |
On Variational Learning of Controllable Representations for Text without Supervision | 42 | icml | 7 | 2 | 2023-06-17 03:57:50.825000 | https://github.com/BorealisAI/CP-VAE | 26 | On variational learning of controllable representations for text without supervision | https://scholar.google.com/scholar?cluster=2089630781496630830&hl=en&as_sdt=0,7 | 5 | 2,020 |
Class-Weighted Classification: Trade-offs and Robust Approaches | 27 | icml | 1 | 0 | 2023-06-17 03:57:51.027000 | https://github.com/neilzxu/robust_weighted_classification | 6 | Class-weighted classification: Trade-offs and robust approaches | https://scholar.google.com/scholar?cluster=11254113557179327347&hl=en&as_sdt=0,33 | 3 | 2,020 |
Learning Autoencoders with Relational Regularization | 42 | icml | 5 | 1 | 2023-06-17 03:57:51.230000 | https://github.com/HongtengXu/Relational-AutoEncoders | 39 | Learning autoencoders with relational regularization | https://scholar.google.com/scholar?cluster=12327328629265717488&hl=en&as_sdt=0,5 | 3 | 2,020 |
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control | 61 | icml | 22 | 2 | 2023-06-17 03:57:51.434000 | https://github.com/mit-gfx/PGMORL | 75 | Prediction-guided multi-objective reinforcement learning for continuous robot control | https://scholar.google.com/scholar?cluster=7336223321111703903&hl=en&as_sdt=0,21 | 18 | 2,020 |
MetaFun: Meta-Learning with Iterative Functional Updates | 53 | icml | 1 | 0 | 2023-06-17 03:57:51.637000 | https://github.com/jinxu06/metafun-tensorflow | 15 | Metafun: Meta-learning with iterative functional updates | https://scholar.google.com/scholar?cluster=4986964761080027704&hl=en&as_sdt=0,5 | 3 | 2,020 |
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization | 29 | icml | 3 | 1 | 2023-06-17 03:57:51.839000 | https://github.com/tianjuxue/AmorFEA | 10 | Amortized finite element analysis for fast pde-constrained optimization | https://scholar.google.com/scholar?cluster=14411842717926650131&hl=en&as_sdt=0,44 | 3 | 2,020 |
Feature Selection using Stochastic Gates | 83 | icml | 20 | 4 | 2023-06-17 03:57:52.041000 | https://github.com/runopti/stg | 74 | Feature selection using stochastic gates | https://scholar.google.com/scholar?cluster=3895875359750859329&hl=en&as_sdt=0,34 | 4 | 2,020 |
Energy-Based Processes for Exchangeable Data | 8 | icml | 7,322 | 1,026 | 2023-06-17 03:57:52.244000 | https://github.com/google-research/google-research | 29,791 | Energy-based processes for exchangeable data | https://scholar.google.com/scholar?cluster=11717820488260195326&hl=en&as_sdt=0,5 | 727 | 2,020 |
Randomized Smoothing of All Shapes and Sizes | 141 | icml | 6 | 1 | 2023-06-17 03:57:52.446000 | https://github.com/tonyduan/rs4a | 48 | Randomized smoothing of all shapes and sizes | https://scholar.google.com/scholar?cluster=4321255830555154678&hl=en&as_sdt=0,21 | 2 | 2,020 |
Improving Molecular Design by Stochastic Iterative Target Augmentation | 14 | icml | 4 | 0 | 2023-06-17 03:57:52.648000 | https://github.com/yangkevin2/icml2020-stochastic-iterative-target-augmentation | 8 | Improving molecular design by stochastic iterative target augmentation | https://scholar.google.com/scholar?cluster=13262578872318506866&hl=en&as_sdt=0,5 | 3 | 2,020 |
Multi-Agent Determinantal Q-Learning | 60 | icml | 7 | 12 | 2023-06-17 03:57:52.850000 | https://github.com/QDPP-GitHub/QDPP | 40 | Multi-agent determinantal q-learning | https://scholar.google.com/scholar?cluster=15130986787127087305&hl=en&as_sdt=0,33 | 2 | 2,020 |
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks | 135 | icml | 7 | 2 | 2023-06-17 03:57:53.052000 | https://github.com/yaodongyu/Rethink-BiasVariance-Tradeoff | 51 | Rethinking bias-variance trade-off for generalization of neural networks | https://scholar.google.com/scholar?cluster=7345683172232852767&hl=en&as_sdt=0,25 | 4 | 2,020 |
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks | 20 | icml | 4 | 1 | 2023-06-17 03:57:53.254000 | https://github.com/thuml/transferable-memory | 20 | Unsupervised transfer learning for spatiotemporal predictive networks | https://scholar.google.com/scholar?cluster=11334443058124456085&hl=en&as_sdt=0,21 | 4 | 2,020 |
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification | 30 | icml | 2 | 3 | 2023-06-17 03:57:53.457000 | https://github.com/huiyegit/APLC_XLNet | 14 | Pretrained generalized autoregressive model with adaptive probabilistic label clusters for extreme multi-label text classification | https://scholar.google.com/scholar?cluster=11309810770103233080&hl=en&as_sdt=0,5 | 1 | 2,020 |
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection | 81 | icml | 7 | 1 | 2023-06-17 03:57:53.660000 | https://github.com/lushleaf/Network-Pruning-Greedy-Forward-Selection | 20 | Good subnetworks provably exist: Pruning via greedy forward selection | https://scholar.google.com/scholar?cluster=9077539701453917687&hl=en&as_sdt=0,5 | 2 | 2,020 |
Data Valuation using Reinforcement Learning | 109 | icml | 7,322 | 1,026 | 2023-06-17 03:57:53.862000 | https://github.com/google-research/google-research | 29,791 | Data valuation using reinforcement learning | https://scholar.google.com/scholar?cluster=12792068149668296468&hl=en&as_sdt=0,5 | 727 | 2,020 |
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning | 40 | icml | 8 | 2 | 2023-06-17 03:57:54.063000 | https://github.com/EdwinKim3069/XtarNet | 27 | Xtarnet: Learning to extract task-adaptive representation for incremental few-shot learning | https://scholar.google.com/scholar?cluster=14540039022540446073&hl=en&as_sdt=0,5 | 3 | 2,020 |
When Does Self-Supervision Help Graph Convolutional Networks? | 161 | icml | 26 | 0 | 2023-06-17 03:57:54.266000 | https://github.com/Shen-Lab/SS-GCNs | 105 | When does self-supervision help graph convolutional networks? | https://scholar.google.com/scholar?cluster=8359089573172587095&hl=en&as_sdt=0,33 | 4 | 2,020 |
Graph Structure of Neural Networks | 108 | icml | 33 | 0 | 2023-06-17 03:57:54.469000 | https://github.com/facebookresearch/graph2nn | 142 | Graph structure of neural networks | https://scholar.google.com/scholar?cluster=4649234253279793186&hl=en&as_sdt=0,5 | 15 | 2,020 |
Intrinsic Reward Driven Imitation Learning via Generative Model | 33 | icml | 4 | 0 | 2023-06-17 03:57:54.671000 | https://github.com/xingruiyu/GIRIL | 12 | Intrinsic reward driven imitation learning via generative model | https://scholar.google.com/scholar?cluster=3469994683333919574&hl=en&as_sdt=0,16 | 3 | 2,020 |
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters | 63 | icml | 22 | 5 | 2023-06-17 03:57:54.873000 | https://github.com/Wenhui-Yu/LCFN | 67 | Graph convolutional network for recommendation with low-pass collaborative filters | https://scholar.google.com/scholar?cluster=1889227241401545976&hl=en&as_sdt=0,44 | 1 | 2,020 |
Training Deep Energy-Based Models with f-Divergence Minimization | 34 | icml | 6 | 4 | 2023-06-17 03:57:55.093000 | https://github.com/ermongroup/f-EBM | 35 | Training deep energy-based models with f-divergence minimization | https://scholar.google.com/scholar?cluster=2539049001962282394&hl=en&as_sdt=0,45 | 7 | 2,020 |
Graph Random Neural Features for Distance-Preserving Graph Representations | 11 | icml | 0 | 0 | 2023-06-17 03:57:55.295000 | https://github.com/dzambon/graph-random-neural-features | 6 | Graph random neural features for distance-preserving graph representations | https://scholar.google.com/scholar?cluster=2137393059005426125&hl=en&as_sdt=0,34 | 2 | 2,020 |
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing | 9 | icml | 0 | 0 | 2023-06-17 03:57:55.497000 | https://github.com/UCLA-StarAI/mpwmi | 4 | Scaling up hybrid probabilistic inference with logical and arithmetic constraints via message passing | https://scholar.google.com/scholar?cluster=11266053605918005936&hl=en&as_sdt=0,5 | 5 | 2,020 |
Learning Calibratable Policies using Programmatic Style-Consistency | 12 | icml | 3 | 0 | 2023-06-17 03:57:55.702000 | https://github.com/ezhan94/calibratable-style-consistency | 7 | Learning calibratable policies using programmatic style-consistency | https://scholar.google.com/scholar?cluster=14384068625001787252&hl=en&as_sdt=0,14 | 3 | 2,020 |
Robustness to Programmable String Transformations via Augmented Abstract Training | 12 | icml | 1 | 0 | 2023-06-17 03:57:55.905000 | https://github.com/ForeverZyh/A3T | 2 | Robustness to programmable string transformations via augmented abstract training | https://scholar.google.com/scholar?cluster=8464081788378179758&hl=en&as_sdt=0,5 | 2 | 2,020 |
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning | 119 | icml | 4 | 2 | 2023-06-17 03:57:56.107000 | https://github.com/zhang64-llnl/Mix-n-Match-Calibration | 28 | Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning | https://scholar.google.com/scholar?cluster=11733441465519935785&hl=en&as_sdt=0,5 | 4 | 2,020 |
Self-Attentive Hawkes Process | 135 | icml | 13 | 4 | 2023-06-17 03:57:56.310000 | https://github.com/QiangAIResearcher/sahp_repo | 41 | Self-attentive Hawkes process | https://scholar.google.com/scholar?cluster=10015751221024050727&hl=en&as_sdt=0,47 | 2 | 2,020 |
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values | 69 | icml | 658 | 6 | 2023-06-17 03:57:56.512000 | https://github.com/ShangtongZhang/DeepRL | 2,943 | Gradientdice: Rethinking generalized offline estimation of stationary values | https://scholar.google.com/scholar?cluster=13399124962585883315&hl=en&as_sdt=0,5 | 93 | 2,020 |
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation | 39 | icml | 658 | 6 | 2023-06-17 03:57:56.714000 | https://github.com/ShangtongZhang/DeepRL | 2,943 | Provably convergent two-timescale off-policy actor-critic with function approximation | https://scholar.google.com/scholar?cluster=13566441396966994806&hl=en&as_sdt=0,44 | 93 | 2,020 |
Invariant Causal Prediction for Block MDPs | 82 | icml | 9 | 0 | 2023-06-17 03:57:56.916000 | https://github.com/facebookresearch/icp-block-mdp | 43 | Invariant causal prediction for block mdps | https://scholar.google.com/scholar?cluster=18252595177085256687&hl=en&as_sdt=0,5 | 8 | 2,020 |
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods | 28 | icml | 8 | 4 | 2023-06-17 03:57:57.119000 | https://github.com/razhangwei/CAUSE | 22 | Cause: Learning granger causality from event sequences using attribution methods | https://scholar.google.com/scholar?cluster=1620742205028282603&hl=en&as_sdt=0,5 | 1 | 2,020 |
Perceptual Generative Autoencoders | 28 | icml | 1 | 0 | 2023-06-17 03:57:57.321000 | https://github.com/zj10/PGA | 23 | Perceptual generative autoencoders | https://scholar.google.com/scholar?cluster=8244017166037108075&hl=en&as_sdt=0,5 | 2 | 2,020 |
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization | 1,245 | icml | 309 | 101 | 2023-06-17 03:57:57.524000 | https://github.com/google-research/pegasus | 1,505 | Pegasus: Pre-training with extracted gap-sentences for abstractive summarization | https://scholar.google.com/scholar?cluster=6497734628006555281&hl=en&as_sdt=0,23 | 49 | 2,020 |
On Leveraging Pretrained GANs for Generation with Limited Data | 65 | icml | 6 | 2 | 2023-06-17 03:57:57.726000 | https://github.com/MiaoyunZhao/GANTransferLimitedData | 59 | On leveraging pretrained GANs for generation with limited data | https://scholar.google.com/scholar?cluster=16391058196447072580&hl=en&as_sdt=0,10 | 3 | 2,020 |
Feature Quantization Improves GAN Training | 33 | icml | 30 | 6 | 2023-06-17 03:57:57.930000 | https://github.com/YangNaruto/FQ-GAN | 169 | Feature quantization improves gan training | https://scholar.google.com/scholar?cluster=18271199409635968326&hl=en&as_sdt=0,31 | 11 | 2,020 |
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion | 11 | icml | 1 | 0 | 2023-06-17 03:57:58.132000 | https://github.com/enosair/gdp-edgeworth | 1 | Sharp composition bounds for Gaussian differential privacy via edgeworth expansion | https://scholar.google.com/scholar?cluster=9890314862207483858&hl=en&as_sdt=0,33 | 2 | 2,020 |
Error-Bounded Correction of Noisy Labels | 76 | icml | 5 | 3 | 2023-06-17 03:57:58.334000 | https://github.com/pingqingsheng/LRT | 15 | Error-bounded correction of noisy labels | https://scholar.google.com/scholar?cluster=16003512579511208211&hl=en&as_sdt=0,33 | 2 | 2,020 |
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time | 11 | icml | 6 | 3 | 2023-06-17 03:57:58.536000 | https://github.com/CQUlearningsystemgroup/YicongPeng | 35 | Monet3d: Towards accurate monocular 3d object localization in real time | https://scholar.google.com/scholar?cluster=16905032404731743832&hl=en&as_sdt=0,11 | 6 | 2,020 |
Nonparametric Score Estimators | 20 | icml | 1 | 0 | 2023-06-17 03:57:58.738000 | https://github.com/miskcoo/kscore | 34 | Nonparametric score estimators | https://scholar.google.com/scholar?cluster=497538758665413874&hl=en&as_sdt=0,14 | 5 | 2,020 |
Robust Outlier Arm Identification | 2 | icml | 0 | 0 | 2023-06-17 03:57:58.941000 | https://github.com/yinglunz/ROAI_ICML2020 | 1 | Robust outlier arm identification | https://scholar.google.com/scholar?cluster=11900711973456670658&hl=en&as_sdt=0,11 | 1 | 2,020 |
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health | 9 | icml | 1 | 0 | 2023-06-17 03:57:59.144000 | https://github.com/lz2379/Mhealth | 1 | Causal effect estimation and optimal dose suggestions in mobile health | https://scholar.google.com/scholar?cluster=15932963727789756281&hl=en&as_sdt=0,39 | 1 | 2,020 |
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization | 21 | icml | 4 | 1 | 2023-06-17 03:57:59.346000 | https://github.com/schzhu/learning-adversarially-robust-representations | 20 | Learning adversarially robust representations via worst-case mutual information maximization | https://scholar.google.com/scholar?cluster=16073902151794610018&hl=en&as_sdt=0,5 | 4 | 2,020 |
Laplacian Regularized Few-Shot Learning | 123 | icml | 8 | 2 | 2023-06-17 03:57:59.547000 | https://github.com/imtiazziko/LaplacianShot | 76 | Laplacian regularized few-shot learning | https://scholar.google.com/scholar?cluster=1752522898167620276&hl=en&as_sdt=0,5 | 4 | 2,020 |
Transformer Hawkes Process | 153 | icml | 43 | 14 | 2023-06-17 03:57:59.749000 | https://github.com/SimiaoZuo/Transformer-Hawkes-Process | 129 | Transformer hawkes process | https://scholar.google.com/scholar?cluster=16348815210194084709&hl=en&as_sdt=0,33 | 7 | 2,020 |
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling | 33 | icml | 2 | 2 | 2023-06-17 04:13:07.614000 | https://github.com/cair/PyTsetlinMachineCUDA | 37 | Massively parallel and asynchronous tsetlin machine architecture supporting almost constant-time scaling | https://scholar.google.com/scholar?cluster=14399815899714278833&hl=en&as_sdt=0,5 | 8 | 2,021 |
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