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RLlib: Abstractions for Distributed Reinforcement Learning | 642 | icml | 4,893 | 2,935 | 2023-06-17 02:59:36.226000 | https://github.com/ray-project/ray | 26,195 | RLlib: Abstractions for distributed reinforcement learning | https://scholar.google.com/scholar?cluster=9535249560181579239&hl=en&as_sdt=0,11 | 450 | 2,018 |
On the Spectrum of Random Features Maps of High Dimensional Data | 48 | icml | 5 | 0 | 2023-06-17 02:59:36.440000 | https://github.com/Zhenyu-LIAO/RMT4RFM | 7 | On the spectrum of random features maps of high dimensional data | https://scholar.google.com/scholar?cluster=4838372697610829936&hl=en&as_sdt=0,44 | 1 | 2,018 |
Reviving and Improving Recurrent Back-Propagation | 95 | icml | 4 | 2 | 2023-06-17 02:59:36.654000 | https://github.com/lrjconan/RBP | 36 | Reviving and improving recurrent back-propagation | https://scholar.google.com/scholar?cluster=8778638717316926195&hl=en&as_sdt=0,5 | 3 | 2,018 |
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression | 79 | icml | 4 | 1 | 2023-06-17 02:59:36.869000 | https://github.com/LiuHaiTao01/GRBCM | 9 | Generalized robust Bayesian committee machine for large-scale Gaussian process regression | https://scholar.google.com/scholar?cluster=8338496144713791124&hl=en&as_sdt=0,33 | 3 | 2,018 |
Delayed Impact of Fair Machine Learning | 410 | icml | 8 | 1 | 2023-06-17 02:59:37.082000 | https://github.com/lydiatliu/delayedimpact | 12 | Delayed impact of fair machine learning | https://scholar.google.com/scholar?cluster=5181623229195224544&hl=en&as_sdt=0,5 | 6 | 2,018 |
Open Category Detection with PAC Guarantees | 81 | icml | 1 | 0 | 2023-06-17 02:59:37.295000 | https://github.com/liusi2019/ocd | 6 | Open category detection with PAC guarantees | https://scholar.google.com/scholar?cluster=16442088261883676309&hl=en&as_sdt=0,3 | 1 | 2,018 |
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design | 101 | icml | 7 | 1 | 2023-06-17 02:59:37.510000 | https://github.com/Alaya-in-Matrix/MACE | 18 | Batch Bayesian optimization via multi-objective acquisition ensemble for automated analog circuit design | https://scholar.google.com/scholar?cluster=18060661280078108001&hl=en&as_sdt=0,41 | 2 | 2,018 |
Celer: a Fast Solver for the Lasso with Dual Extrapolation | 71 | icml | 29 | 23 | 2023-06-17 02:59:37.724000 | https://github.com/mathurinm/celer | 172 | Celer: a fast solver for the lasso with dual extrapolation | https://scholar.google.com/scholar?cluster=5377261088300700033&hl=en&as_sdt=0,5 | 11 | 2,018 |
Dimensionality-Driven Learning with Noisy Labels | 347 | icml | 13 | 7 | 2023-06-17 02:59:37.938000 | https://github.com/xingjunm/dimensionality-driven-learning | 54 | Dimensionality-driven learning with noisy labels | https://scholar.google.com/scholar?cluster=13671594748199391279&hl=en&as_sdt=0,5 | 6 | 2,018 |
Orthogonal Machine Learning: Power and Limitations | 32 | icml | 1 | 0 | 2023-06-17 02:59:38.152000 | https://github.com/IliasZadik/double_orthogonal_ml | 9 | Orthogonal machine learning: Power and limitations | https://scholar.google.com/scholar?cluster=5809392484253895551&hl=en&as_sdt=0,14 | 3 | 2,018 |
Learning Adversarially Fair and Transferable Representations | 552 | icml | 12 | 0 | 2023-06-17 02:59:38.366000 | https://github.com/VectorInstitute/laftr | 50 | Learning adversarially fair and transferable representations | https://scholar.google.com/scholar?cluster=6932272369084023440&hl=en&as_sdt=0,47 | 6 | 2,018 |
Iterative Amortized Inference | 139 | icml | 9 | 1 | 2023-06-17 02:59:38.579000 | https://github.com/joelouismarino/iterative_inference | 43 | Iterative amortized inference | https://scholar.google.com/scholar?cluster=11655024897433506011&hl=en&as_sdt=0,43 | 3 | 2,018 |
Optimization, fast and slow: optimally switching between local and Bayesian optimization | 33 | icml | 9 | 2 | 2023-06-17 02:59:38.792000 | https://github.com/markm541374/gpbo | 25 | Optimization, fast and slow: optimally switching between local and Bayesian optimization | https://scholar.google.com/scholar?cluster=6241493477815440111&hl=en&as_sdt=0,5 | 2 | 2,018 |
Which Training Methods for GANs do actually Converge? | 1,241 | icml | 115 | 12 | 2023-06-17 02:59:39.006000 | https://github.com/LMescheder/GAN_stability | 900 | Which training methods for GANs do actually converge? | https://scholar.google.com/scholar?cluster=11334901664651510839&hl=en&as_sdt=0,5 | 22 | 2,018 |
prDeep: Robust Phase Retrieval with a Flexible Deep Network | 151 | icml | 12 | 1 | 2023-06-17 02:59:39.219000 | https://github.com/ricedsp/prDeep | 37 | prDeep: Robust phase retrieval with a flexible deep network | https://scholar.google.com/scholar?cluster=13840213498750434607&hl=en&as_sdt=0,44 | 3 | 2,018 |
One-Shot Segmentation in Clutter | 39 | icml | 11 | 0 | 2023-06-17 02:59:39.433000 | https://github.com/michaelisc/cluttered-omniglot | 47 | One-shot segmentation in clutter | https://scholar.google.com/scholar?cluster=14253967975584352267&hl=en&as_sdt=0,5 | 4 | 2,018 |
Differentiable plasticity: training plastic neural networks with backpropagation | 151 | icml | 71 | 3 | 2023-06-17 02:59:39.646000 | https://github.com/uber-common/differentiable-plasticity | 389 | Differentiable plasticity: training plastic neural networks with backpropagation | https://scholar.google.com/scholar?cluster=16849084099727983459&hl=en&as_sdt=0,5 | 27 | 2,018 |
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding | 21 | icml | 1 | 1 | 2023-06-17 02:59:39.860000 | https://github.com/tomMoral/dicod | 12 | Dicod: Distributed convolutional coordinate descent for convolutional sparse coding | https://scholar.google.com/scholar?cluster=6841370809688839469&hl=en&as_sdt=0,25 | 3 | 2,018 |
Nearly Optimal Robust Subspace Tracking | 30 | icml | 4 | 0 | 2023-06-17 02:59:40.073000 | https://github.com/praneethmurthy/NORST | 7 | Nearly optimal robust subspace tracking | https://scholar.google.com/scholar?cluster=11197141106222317789&hl=en&as_sdt=0,14 | 3 | 2,018 |
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry | 341 | icml | 221 | 29 | 2023-06-17 02:59:40.286000 | https://github.com/facebookresearch/poincare-embeddings | 1,592 | Learning continuous hierarchies in the lorentz model of hyperbolic geometry | https://scholar.google.com/scholar?cluster=5235601311596588081&hl=en&as_sdt=0,10 | 52 | 2,018 |
SparseMAP: Differentiable Sparse Structured Inference | 119 | icml | 9 | 3 | 2023-06-17 02:59:40.501000 | https://github.com/vene/sparsemap | 109 | Sparsemap: Differentiable sparse structured inference | https://scholar.google.com/scholar?cluster=16676407380618945031&hl=en&as_sdt=0,24 | 9 | 2,018 |
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations | 144 | icml | 0 | 0 | 2023-06-17 02:59:40.714000 | https://github.com/weilinie/BackpropVis | 5 | A theoretical explanation for perplexing behaviors of backpropagation-based visualizations | https://scholar.google.com/scholar?cluster=7254168770426119962&hl=en&as_sdt=0,47 | 2 | 2,018 |
Self-Imitation Learning | 274 | icml | 40 | 4 | 2023-06-17 02:59:40.928000 | https://github.com/junhyukoh/self-imitation-learning | 269 | Self-imitation learning | https://scholar.google.com/scholar?cluster=6282132634766578030&hl=en&as_sdt=0,31 | 16 | 2,018 |
Learning Localized Spatio-Temporal Models From Streaming Data | 1 | icml | 0 | 3 | 2023-06-17 02:59:41.142000 | https://github.com/Muhammad-Osama/Localized-Spatio-temporal-Models | 7 | Learning localized spatio-temporal models from streaming data | https://scholar.google.com/scholar?cluster=6273621603567429406&hl=en&as_sdt=0,33 | 1 | 2,018 |
Efficient First-Order Algorithms for Adaptive Signal Denoising | 5 | icml | 1 | 0 | 2023-06-17 02:59:41.355000 | https://github.com/ostrodmit/AlgoRec | 6 | Efficient first-order algorithms for adaptive signal denoising | https://scholar.google.com/scholar?cluster=16164313069281185033&hl=en&as_sdt=0,10 | 3 | 2,018 |
Analyzing Uncertainty in Neural Machine Translation | 748 | icml | 8 | 0 | 2023-06-17 02:59:41.569000 | https://github.com/facebookresearch/analyzing-uncertainty-nmt | 32 | Analyzing uncertainty in neural machine translation | https://scholar.google.com/scholar?cluster=1522001537063991105&hl=en&as_sdt=0,5 | 56 | 2,018 |
Max-Mahalanobis Linear Discriminant Analysis Networks | 46 | icml | 19 | 1 | 2023-06-17 02:59:41.783000 | https://github.com/P2333/Max-Mahalanobis-Training | 87 | Max-mahalanobis linear discriminant analysis networks | https://scholar.google.com/scholar?cluster=1310490945606447616&hl=en&as_sdt=0,33 | 4 | 2,018 |
Stochastic Variance-Reduced Policy Gradient | 145 | icml | 4 | 0 | 2023-06-17 02:59:41.997000 | https://github.com/Dam930/rllab | 3 | Stochastic variance-reduced policy gradient | https://scholar.google.com/scholar?cluster=10229080169981298445&hl=en&as_sdt=0,38 | 3 | 2,018 |
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos | 56 | icml | 2 | 0 | 2023-06-17 02:59:42.211000 | https://github.com/proppo/pipps_demo | 0 | PIPPS: Flexible model-based policy search robust to the curse of chaos | https://scholar.google.com/scholar?cluster=8640168252000745898&hl=en&as_sdt=0,5 | 2 | 2,018 |
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration | 38 | icml | 2 | 0 | 2023-06-17 02:59:42.426000 | https://github.com/jliang993/Local-VRSGD | 2 | Local convergence properties of SAGA/Prox-SVRG and acceleration | https://scholar.google.com/scholar?cluster=12517501002751750903&hl=en&as_sdt=0,33 | 3 | 2,018 |
Learning Dynamics of Linear Denoising Autoencoders | 24 | icml | 3 | 1 | 2023-06-17 02:59:42.640000 | https://github.com/arnupretorius/lindaedynamics_icml2018 | 12 | Learning dynamics of linear denoising autoencoders | https://scholar.google.com/scholar?cluster=11573052296697932394&hl=en&as_sdt=0,6 | 3 | 2,018 |
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets | 40 | icml | 8 | 1 | 2023-06-17 02:59:42.856000 | https://github.com/sdai654416/Joint-GAN | 19 | Jointgan: Multi-domain joint distribution learning with generative adversarial nets | https://scholar.google.com/scholar?cluster=17442133177721066359&hl=en&as_sdt=0,25 | 1 | 2,018 |
Selecting Representative Examples for Program Synthesis | 30 | icml | 3 | 1 | 2023-06-17 02:59:43.071000 | https://github.com/evanthebouncy/icml2018_selecting_representative_examples | 11 | Selecting representative examples for program synthesis | https://scholar.google.com/scholar?cluster=18419281465561462811&hl=en&as_sdt=0,22 | 3 | 2,018 |
DCFNet: Deep Neural Network with Decomposed Convolutional Filters | 58 | icml | 1 | 0 | 2023-06-17 02:59:43.284000 | https://github.com/xycheng/DCFNet | 11 | DCFNet: Deep neural network with decomposed convolutional filters | https://scholar.google.com/scholar?cluster=6785841352849465563&hl=en&as_sdt=0,5 | 2 | 2,018 |
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? | 31 | icml | 0 | 0 | 2023-06-17 02:59:43.498000 | https://github.com/rubai5/ESS_Game | 7 | Can deep reinforcement learning solve Erdos-Selfridge-Spencer games? | https://scholar.google.com/scholar?cluster=5045759722516886464&hl=en&as_sdt=0,5 | 1 | 2,018 |
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate | 46 | icml | 3 | 0 | 2023-06-17 02:59:43.713000 | https://github.com/tijana-zrnic/SAFFRONcode | 7 | SAFFRON: an adaptive algorithm for online control of the false discovery rate | https://scholar.google.com/scholar?cluster=3162538214212248602&hl=en&as_sdt=0,5 | 0 | 2,018 |
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning | 1,465 | icml | 360 | 54 | 2023-06-17 02:59:43.927000 | https://github.com/oxwhirl/pymarl | 1,474 | Monotonic value function factorisation for deep multi-agent reinforcement learning | https://scholar.google.com/scholar?cluster=3975132673723125155&hl=en&as_sdt=0,33 | 32 | 2,018 |
Learning to Reweight Examples for Robust Deep Learning | 1,199 | icml | 53 | 8 | 2023-06-17 02:59:44.141000 | https://github.com/uber-research/learning-to-reweight-examples | 266 | Learning to reweight examples for robust deep learning | https://scholar.google.com/scholar?cluster=17871432661582272860&hl=en&as_sdt=0,5 | 11 | 2,018 |
Fast Information-theoretic Bayesian Optimisation | 48 | icml | 2 | 0 | 2023-06-17 02:59:44.355000 | https://github.com/rubinxin/FITBO | 16 | Fast information-theoretic Bayesian optimisation | https://scholar.google.com/scholar?cluster=12232335065092117172&hl=en&as_sdt=0,14 | 2 | 2,018 |
Probabilistic Boolean Tensor Decomposition | 18 | icml | 5 | 0 | 2023-06-17 02:59:44.570000 | https://github.com/TammoR/LogicalFactorisationMachines | 20 | Probabilistic boolean tensor decomposition | https://scholar.google.com/scholar?cluster=11732429422199282970&hl=en&as_sdt=0,5 | 3 | 2,018 |
Black-Box Variational Inference for Stochastic Differential Equations | 62 | icml | 10 | 0 | 2023-06-17 02:59:44.787000 | https://github.com/Tom-Ryder/VIforSDEs | 41 | Black-box variational inference for stochastic differential equations | https://scholar.google.com/scholar?cluster=771102464723698631&hl=en&as_sdt=0,33 | 6 | 2,018 |
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks | 254 | icml | 3 | 0 | 2023-06-17 02:59:45.001000 | https://github.com/ItaySafran/OneLayerGDconvergence | 1 | Spurious local minima are common in two-layer relu neural networks | https://scholar.google.com/scholar?cluster=2602196713819367782&hl=en&as_sdt=0,18 | 0 | 2,018 |
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service | 113 | icml | 6 | 0 | 2023-06-17 02:59:45.217000 | https://github.com/amartya18x/tapas | 16 | TAPAS: Tricks to accelerate (encrypted) prediction as a service | https://scholar.google.com/scholar?cluster=13862835131458070168&hl=en&as_sdt=0,5 | 8 | 2,018 |
Learning with Abandonment | 9 | icml | 0 | 0 | 2023-06-17 02:59:45.432000 | https://github.com/schmit/learning-abandonment | 1 | Learning with abandonment | https://scholar.google.com/scholar?cluster=5599696763306308098&hl=en&as_sdt=0,48 | 2 | 2,018 |
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care | 24 | icml | 3 | 1 | 2023-06-17 02:59:45.647000 | https://github.com/d909b/DSMT-Nets | 10 | Not to cry wolf: Distantly supervised multitask learning in critical care | https://scholar.google.com/scholar?cluster=13897538253893011334&hl=en&as_sdt=0,5 | 4 | 2,018 |
Overcoming Catastrophic Forgetting with Hard Attention to the Task | 699 | icml | 49 | 1 | 2023-06-17 02:59:45.862000 | https://github.com/joansj/hat | 174 | Overcoming catastrophic forgetting with hard attention to the task | https://scholar.google.com/scholar?cluster=11086231050694477723&hl=en&as_sdt=0,36 | 10 | 2,018 |
First Order Generative Adversarial Networks | 6 | icml | 12 | 1 | 2023-06-17 02:59:46.076000 | https://github.com/zalandoresearch/first_order_gan | 35 | First order generative adversarial networks | https://scholar.google.com/scholar?cluster=4229294235141796493&hl=en&as_sdt=0,5 | 7 | 2,018 |
Finding Influential Training Samples for Gradient Boosted Decision Trees | 38 | icml | 18 | 0 | 2023-06-17 02:59:46.294000 | https://github.com/bsharchilev/influence_boosting | 63 | Finding influential training samples for gradient boosted decision trees | https://scholar.google.com/scholar?cluster=16436473119957517587&hl=en&as_sdt=0,33 | 7 | 2,018 |
Solving Partial Assignment Problems using Random Clique Complexes | 1 | icml | 3 | 0 | 2023-06-17 02:59:46.513000 | https://github.com/charusharma1991/RandomCliqueComplexes_ICML2018 | 2 | Solving partial assignment problems using random clique complexes | https://scholar.google.com/scholar?cluster=8378028426453482804&hl=en&as_sdt=0,36 | 2 | 2,018 |
A Spectral Approach to Gradient Estimation for Implicit Distributions | 78 | icml | 9 | 2 | 2023-06-17 02:59:46.734000 | https://github.com/thjashin/spectral-stein-grad | 33 | A spectral approach to gradient estimation for implicit distributions | https://scholar.google.com/scholar?cluster=34252178022681098&hl=en&as_sdt=0,9 | 4 | 2,018 |
Accelerating Natural Gradient with Higher-Order Invariance | 13 | icml | 8 | 0 | 2023-06-17 02:59:46.955000 | https://github.com/ermongroup/higher_order_invariance | 30 | Accelerating natural gradient with higher-order invariance | https://scholar.google.com/scholar?cluster=17686115985744822983&hl=en&as_sdt=0,33 | 6 | 2,018 |
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search | 92 | icml | 21 | 3 | 2023-06-17 02:59:47.175000 | https://github.com/sg-nm/Evolutionary-Autoencoders | 67 | Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search | https://scholar.google.com/scholar?cluster=4118394325034454915&hl=en&as_sdt=0,41 | 3 | 2,018 |
Scalable approximate Bayesian inference for particle tracking data | 12 | icml | 0 | 2 | 2023-06-17 02:59:47.390000 | https://github.com/SunRuoxi/Single_Particle_Tracking | 1 | Scalable approximate Bayesian inference for particle tracking data | https://scholar.google.com/scholar?cluster=8017063234741228178&hl=en&as_sdt=0,8 | 3 | 2,018 |
Learning the Reward Function for a Misspecified Model | 12 | icml | 1 | 0 | 2023-06-17 02:59:47.606000 | https://github.com/etalvitie/hdaggermc | 8 | Learning the reward function for a misspecified model | https://scholar.google.com/scholar?cluster=16036091820545871049&hl=en&as_sdt=0,5 | 1 | 2,018 |
Chi-square Generative Adversarial Network | 40 | icml | 0 | 3 | 2023-06-17 02:59:47.820000 | https://github.com/chenyang-tao/chi2gan | 6 | Chi-square generative adversarial network | https://scholar.google.com/scholar?cluster=3560140041128352974&hl=en&as_sdt=0,14 | 4 | 2,018 |
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees | 47 | icml | 1 | 0 | 2023-06-17 02:59:48.036000 | https://github.com/QCGroup/quad-lyap-first-order | 5 | Lyapunov functions for first-order methods: Tight automated convergence guarantees | https://scholar.google.com/scholar?cluster=1395570422835062279&hl=en&as_sdt=0,5 | 3 | 2,018 |
Adversarial Regression with Multiple Learners | 33 | icml | 1 | 0 | 2023-06-17 02:59:48.259000 | https://github.com/marsplus/Adversarial-Regression-with-Multiple-Learners | 2 | Adversarial regression with multiple learners | https://scholar.google.com/scholar?cluster=11851981725937878010&hl=en&as_sdt=0,33 | 2 | 2,018 |
StrassenNets: Deep Learning with a Multiplication Budget | 30 | icml | 10 | 0 | 2023-06-17 02:59:48.473000 | https://github.com/mitscha/strassennets | 45 | StrassenNets: Deep learning with a multiplication budget | https://scholar.google.com/scholar?cluster=9065345888211174353&hl=en&as_sdt=0,44 | 4 | 2,018 |
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning | 350 | icml | 85 | 6 | 2023-06-17 02:59:48.687000 | https://github.com/Yunbo426/predrnn-pp | 221 | Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning | https://scholar.google.com/scholar?cluster=16975551372418150051&hl=en&as_sdt=0,5 | 10 | 2,018 |
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples | 145 | icml | 1 | 0 | 2023-06-17 02:59:48.902000 | https://github.com/EricYizhenWang/robust_nn_icml | 6 | Analyzing the robustness of nearest neighbors to adversarial examples | https://scholar.google.com/scholar?cluster=15228068536645268692&hl=en&as_sdt=0,5 | 3 | 2,018 |
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models | 4 | icml | 0 | 1 | 2023-06-17 02:59:49.117000 | https://github.com/QData/JEEK | 1 | A fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian graphical models | https://scholar.google.com/scholar?cluster=12183443188962650844&hl=en&as_sdt=0,6 | 4 | 2,018 |
Adversarial Distillation of Bayesian Neural Network Posteriors | 59 | icml | 2 | 1 | 2023-06-17 02:59:49.330000 | https://github.com/wangkua1/apd_public | 14 | Adversarial distillation of bayesian neural network posteriors | https://scholar.google.com/scholar?cluster=8595967760145130464&hl=en&as_sdt=0,47 | 6 | 2,018 |
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions | 20 | icml | 2 | 1 | 2023-06-17 02:59:49.546000 | https://github.com/wendazhou/alocv-package | 6 | Approximate leave-one-out for fast parameter tuning in high dimensions | https://scholar.google.com/scholar?cluster=7517160253492394187&hl=en&as_sdt=0,7 | 4 | 2,018 |
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples | 179 | icml | 19 | 0 | 2023-06-17 02:59:49.797000 | https://github.com/tech-srl/lstar_extraction | 63 | Extracting automata from recurrent neural networks using queries and counterexamples | https://scholar.google.com/scholar?cluster=3812692831904479239&hl=en&as_sdt=0,31 | 7 | 2,018 |
Towards Fast Computation of Certified Robustness for ReLU Networks | 641 | icml | 5 | 1 | 2023-06-17 02:59:50.011000 | https://github.com/huanzhang12/CertifiedReLURobustness | 29 | Towards fast computation of certified robustness for relu networks | https://scholar.google.com/scholar?cluster=13154362274812885800&hl=en&as_sdt=0,39 | 5 | 2,018 |
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope | 1,370 | icml | 83 | 8 | 2023-06-17 02:59:50.226000 | https://github.com/locuslab/convex_adversarial | 357 | Provable defenses against adversarial examples via the convex outer adversarial polytope | https://scholar.google.com/scholar?cluster=2593701021867797885&hl=en&as_sdt=0,47 | 16 | 2,018 |
SQL-Rank: A Listwise Approach to Collaborative Ranking | 42 | icml | 8 | 0 | 2023-06-17 02:59:50.441000 | https://github.com/wuliwei9278/SQL-Rank | 16 | Sql-rank: A listwise approach to collaborative ranking | https://scholar.google.com/scholar?cluster=3011153619955791541&hl=en&as_sdt=0,10 | 3 | 2,018 |
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization | 15 | icml | 0 | 1 | 2023-06-17 02:59:50.655000 | https://github.com/hang-wu/VRCRM | 2 | Variance regularized counterfactual risk minimization via variational divergence minimization | https://scholar.google.com/scholar?cluster=16906275230514657049&hl=en&as_sdt=0,10 | 2 | 2,018 |
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions | 122 | icml | 34 | 0 | 2023-06-17 02:59:50.870000 | https://github.com/Sandbox3aster/Deep-K-Means | 146 | Deep k-means: Re-training and parameter sharing with harder cluster assignments for compressing deep convolutions | https://scholar.google.com/scholar?cluster=5421215697510972919&hl=en&as_sdt=0,44 | 13 | 2,018 |
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks | 293 | icml | 8 | 0 | 2023-06-17 02:59:51.084000 | https://github.com/brain-research/mean-field-cnns | 35 | Dynamical isometry and a mean field theory of cnns: How to train 10,000-layer vanilla convolutional neural networks | https://scholar.google.com/scholar?cluster=4327553153293253435&hl=en&as_sdt=0,47 | 7 | 2,018 |
Learning Semantic Representations for Unsupervised Domain Adaptation | 455 | icml | 38 | 3 | 2023-06-17 02:59:51.299000 | https://github.com/Mid-Push/Moving-Semantic-Transfer-Network | 105 | Learning semantic representations for unsupervised domain adaptation | https://scholar.google.com/scholar?cluster=3795243851386744123&hl=en&as_sdt=0,47 | 5 | 2,018 |
A Semantic Loss Function for Deep Learning with Symbolic Knowledge | 362 | icml | 11 | 1 | 2023-06-17 02:59:51.513000 | https://github.com/UCLA-StarAI/Semantic-Loss | 52 | A semantic loss function for deep learning with symbolic knowledge | https://scholar.google.com/scholar?cluster=2687938736648965063&hl=en&as_sdt=0,44 | 11 | 2,018 |
Mean Field Multi-Agent Reinforcement Learning | 564 | icml | 92 | 20 | 2023-06-17 02:59:51.727000 | https://github.com/mlii/mfrl | 323 | Mean field multi-agent reinforcement learning | https://scholar.google.com/scholar?cluster=18365585657208114611&hl=en&as_sdt=0,23 | 10 | 2,018 |
Yes, but Did It Work?: Evaluating Variational Inference | 138 | icml | 2 | 1 | 2023-06-17 02:59:51.941000 | https://github.com/yao-yl/Evaluating-Variational-Inference | 12 | Yes, but did it work?: Evaluating variational inference | https://scholar.google.com/scholar?cluster=16612262779014542273&hl=en&as_sdt=0,31 | 3 | 2,018 |
Semi-Implicit Variational Inference | 122 | icml | 13 | 1 | 2023-06-17 02:59:52.156000 | https://github.com/mingzhang-yin/SIVI | 49 | Semi-implicit variational inference | https://scholar.google.com/scholar?cluster=952314383686625023&hl=en&as_sdt=0,5 | 5 | 2,018 |
GAIN: Missing Data Imputation using Generative Adversarial Nets | 784 | icml | 141 | 0 | 2023-06-17 02:59:52.371000 | https://github.com/jsyoon0823/GAIN | 307 | Gain: Missing data imputation using generative adversarial nets | https://scholar.google.com/scholar?cluster=6024113526841994005&hl=en&as_sdt=0,6 | 11 | 2,018 |
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models | 753 | icml | 109 | 6 | 2023-06-17 02:59:52.586000 | https://github.com/snap-stanford/GraphRNN | 387 | Graphrnn: Generating realistic graphs with deep auto-regressive models | https://scholar.google.com/scholar?cluster=18334516615969196433&hl=en&as_sdt=0,5 | 60 | 2,018 |
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization | 106 | icml | 3 | 1 | 2023-06-17 02:59:52.801000 | https://github.com/zhangjiong724/spectral-RNN | 12 | Stabilizing gradients for deep neural networks via efficient svd parameterization | https://scholar.google.com/scholar?cluster=10623363336533108811&hl=en&as_sdt=0,9 | 2 | 2,018 |
Learning Long Term Dependencies via Fourier Recurrent Units | 31 | icml | 8 | 0 | 2023-06-17 02:59:53.014000 | https://github.com/limbo018/FRU | 36 | Learning long term dependencies via fourier recurrent units | https://scholar.google.com/scholar?cluster=16150244378271641439&hl=en&as_sdt=0,5 | 7 | 2,018 |
Inter and Intra Topic Structure Learning with Word Embeddings | 16 | icml | 3 | 4 | 2023-06-17 02:59:53.228000 | https://github.com/ethanhezhao/WEDTM | 6 | Inter and intra topic structure learning with word embeddings | https://scholar.google.com/scholar?cluster=11048244315815532986&hl=en&as_sdt=0,5 | 4 | 2,018 |
Adversarially Regularized Autoencoders | 291 | icml | 93 | 19 | 2023-06-17 02:59:53.442000 | https://github.com/jakezhaojb/ARAE | 400 | Adversarially regularized autoencoders | https://scholar.google.com/scholar?cluster=5024716526871945774&hl=en&as_sdt=0,11 | 20 | 2,018 |
Dynamic Weights in Multi-Objective Deep Reinforcement Learning | 97 | icml | 16 | 0 | 2023-06-17 03:09:58.308000 | https://github.com/axelabels/DynMORL | 64 | Dynamic weights in multi-objective deep reinforcement learning | https://scholar.google.com/scholar?cluster=12040121315464946458&hl=en&as_sdt=0,39 | 2 | 2,019 |
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing | 600 | icml | 34 | 4 | 2023-06-17 03:09:58.523000 | https://github.com/samihaija/mixhop | 113 | Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing | https://scholar.google.com/scholar?cluster=8927230189965016671&hl=en&as_sdt=0,5 | 7 | 2,019 |
Understanding the Impact of Entropy on Policy Optimization | 182 | icml | 14 | 4 | 2023-06-17 03:09:58.738000 | https://github.com/zafarali/emdp | 47 | Understanding the impact of entropy on policy optimization | https://scholar.google.com/scholar?cluster=8905478721868235472&hl=en&as_sdt=0,36 | 5 | 2,019 |
Fairwashing: the risk of rationalization | 118 | icml | 2 | 1 | 2023-06-17 03:09:58.954000 | https://github.com/aivodji/LaundryML | 15 | Fairwashing: the risk of rationalization | https://scholar.google.com/scholar?cluster=2523692918696533409&hl=en&as_sdt=0,23 | 2 | 2,019 |
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search | 74 | icml | 12 | 2 | 2023-06-17 03:09:59.200000 | https://github.com/shirakawas/ASNG-NAS | 86 | Adaptive stochastic natural gradient method for one-shot neural architecture search | https://scholar.google.com/scholar?cluster=8278729461791344602&hl=en&as_sdt=0,44 | 12 | 2,019 |
Graph Element Networks: adaptive, structured computation and memory | 73 | icml | 18 | 0 | 2023-06-17 03:09:59.417000 | https://github.com/FerranAlet/graph_element_networks | 54 | Graph element networks: adaptive, structured computation and memory | https://scholar.google.com/scholar?cluster=15635052566391015915&hl=en&as_sdt=0,47 | 4 | 2,019 |
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation | 43 | icml | 5 | 1 | 2023-06-17 03:09:59.631000 | https://github.com/a5a/asynchronous-BO | 9 | Asynchronous batch Bayesian optimisation with improved local penalisation | https://scholar.google.com/scholar?cluster=17891210137592442168&hl=en&as_sdt=0,11 | 2 | 2,019 |
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data | 9 | icml | 6 | 0 | 2023-06-17 03:09:59.848000 | https://github.com/sergulaydore/Feature-Grouping-Regularizer | 20 | Feature grouping as a stochastic regularizer for high-dimensional structured data | https://scholar.google.com/scholar?cluster=11613171711375782355&hl=en&as_sdt=0,47 | 3 | 2,019 |
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior | 11 | icml | 0 | 0 | 2023-06-17 03:10:00.065000 | https://github.com/OxCSML-BayesNP/doublepowerlaw | 0 | Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior | https://scholar.google.com/scholar?cluster=7805425707346893329&hl=en&as_sdt=0,5 | 4 | 2,019 |
Scalable Fair Clustering | 174 | icml | 6 | 0 | 2023-06-17 03:10:00.279000 | https://github.com/talwagner/fair_clustering | 16 | Scalable fair clustering | https://scholar.google.com/scholar?cluster=16665021693225941817&hl=en&as_sdt=0,14 | 2 | 2,019 |
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs | 18 | icml | 4 | 1 | 2023-06-17 03:10:00.494000 | https://github.com/yogeshbalaji/EntropicGANs_meet_VAEs | 8 | Entropic gans meet vaes: A statistical approach to compute sample likelihoods in gans | https://scholar.google.com/scholar?cluster=4502964466526434508&hl=en&as_sdt=0,10 | 2 | 2,019 |
Provable Guarantees for Gradient-Based Meta-Learning | 136 | icml | 2 | 0 | 2023-06-17 03:10:00.712000 | https://github.com/mkhodak/FMRL | 3 | Provable guarantees for gradient-based meta-learning | https://scholar.google.com/scholar?cluster=18333296959440727243&hl=en&as_sdt=0,33 | 3 | 2,019 |
Learning to Route in Similarity Graphs | 23 | icml | 15 | 3 | 2023-06-17 03:10:00.937000 | https://github.com/dbaranchuk/learning-to-route | 50 | Learning to route in similarity graphs | https://scholar.google.com/scholar?cluster=381431972230740194&hl=en&as_sdt=0,14 | 10 | 2,019 |
Noise2Self: Blind Denoising by Self-Supervision | 439 | icml | 67 | 5 | 2023-06-17 03:10:01.181000 | https://github.com/czbiohub/noise2self | 292 | Noise2self: Blind denoising by self-supervision | https://scholar.google.com/scholar?cluster=16484478987296907806&hl=en&as_sdt=0,43 | 16 | 2,019 |
Efficient optimization of loops and limits with randomized telescoping sums | 22 | icml | 4 | 1 | 2023-06-17 03:10:01.396000 | https://github.com/PrincetonLIPS/randomized_telescopes | 27 | Efficient optimization of loops and limits with randomized telescoping sums | https://scholar.google.com/scholar?cluster=3412668840791342029&hl=en&as_sdt=0,33 | 5 | 2,019 |
Greedy Layerwise Learning Can Scale To ImageNet | 136 | icml | 11 | 0 | 2023-06-17 03:10:01.612000 | https://github.com/eugenium/layerCNN | 17 | Greedy layerwise learning can scale to imagenet | https://scholar.google.com/scholar?cluster=17442726017389288785&hl=en&as_sdt=0,5 | 4 | 2,019 |
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning | 21 | icml | 1 | 0 | 2023-06-17 03:10:01.826000 | https://github.com/marcelomatheusgauy/optimal_kronecker_approximation | 5 | Optimal kronecker-sum approximation of real time recurrent learning | https://scholar.google.com/scholar?cluster=6902147836625554260&hl=en&as_sdt=0,50 | 3 | 2,019 |
Analyzing Federated Learning through an Adversarial Lens | 750 | icml | 34 | 4 | 2023-06-17 03:10:02.041000 | https://github.com/inspire-group/ModelPoisoning | 133 | Analyzing federated learning through an adversarial lens | https://scholar.google.com/scholar?cluster=16839948122426603319&hl=en&as_sdt=0,5 | 6 | 2,019 |
A Kernel Perspective for Regularizing Deep Neural Networks | 67 | icml | 6 | 0 | 2023-06-17 03:10:02.256000 | https://github.com/albietz/kernel_reg | 22 | A kernel perspective for regularizing deep neural networks | https://scholar.google.com/scholar?cluster=17149885341490741277&hl=en&as_sdt=0,5 | 3 | 2,019 |
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