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Improving Generalization and Stability of Generative Adversarial Networks
| 126 |
iclr
| 7 | 0 |
2023-06-18 08:57:54.295000
|
https://github.com/htt210/GeneralizationAndStabilityInGANs
| 36 |
Improving generalization and stability of generative adversarial networks
|
https://scholar.google.com/scholar?cluster=13499019185526283919&hl=en&as_sdt=0,15
| 3 | 2,019 |
Adaptive Input Representations for Neural Language Modeling
| 317 |
iclr
| 5,883 | 1,031 |
2023-06-18 08:57:54.497000
|
https://github.com/pytorch/fairseq
| 26,500 |
Adaptive input representations for neural language modeling
|
https://scholar.google.com/scholar?cluster=9932684582274973195&hl=en&as_sdt=0,32
| 411 | 2,019 |
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
| 102 |
iclr
| 6 | 0 |
2023-06-18 08:57:54.698000
|
https://github.com/BorgwardtLab/Neural-Persistence
| 24 |
Neural persistence: A complexity measure for deep neural networks using algebraic topology
|
https://scholar.google.com/scholar?cluster=12286997751595249495&hl=en&as_sdt=0,44
| 9 | 2,019 |
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model
| 10 |
iclr
| 6 | 1 |
2023-06-18 08:57:54.900000
|
https://github.com/florianmai/word2mat
| 20 |
CBOW is not all you need: Combining CBOW with the compositional matrix space model
|
https://scholar.google.com/scholar?cluster=6038502138949255694&hl=en&as_sdt=0,43
| 1 | 2,019 |
Stochastic Optimization of Sorting Networks via Continuous Relaxations
| 110 |
iclr
| 24 | 6 |
2023-06-18 08:57:55.101000
|
https://github.com/ermongroup/neuralsort
| 119 |
Stochastic optimization of sorting networks via continuous relaxations
|
https://scholar.google.com/scholar?cluster=10619362619006891050&hl=en&as_sdt=0,44
| 10 | 2,019 |
Generating Multiple Objects at Spatially Distinct Locations
| 105 |
iclr
| 14 | 7 |
2023-06-18 08:57:55.303000
|
https://github.com/tohinz/multiple-objects-gan
| 111 |
Generating multiple objects at spatially distinct locations
|
https://scholar.google.com/scholar?cluster=13574885695794039292&hl=en&as_sdt=0,14
| 7 | 2,019 |
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
| 176 |
iclr
| 46,278 | 1,207 |
2023-06-18 08:57:55.504000
|
https://github.com/tensorflow/models
| 75,928 |
Near-optimal representation learning for hierarchical reinforcement learning
|
https://scholar.google.com/scholar?cluster=17682749665983906973&hl=en&as_sdt=0,14
| 2,774 | 2,019 |
Understanding Composition of Word Embeddings via Tensor Decomposition
| 6 |
iclr
| 1 | 0 |
2023-06-18 08:57:55.708000
|
https://github.com/abefrandsen/syntactic-rand-walk
| 5 |
Understanding composition of word embeddings via tensor decomposition
|
https://scholar.google.com/scholar?cluster=9072436238425463642&hl=en&as_sdt=0,34
| 4 | 2,019 |
Structured Neural Summarization
| 204 |
iclr
| 26 | 11 |
2023-06-18 08:57:55.910000
|
https://github.com/CoderPat/structured-neural-summarization
| 74 |
Structured neural summarization
|
https://scholar.google.com/scholar?cluster=5961913139611201410&hl=en&as_sdt=0,5
| 3 | 2,019 |
Supervised Community Detection with Line Graph Neural Networks
| 271 |
iclr
| 18 | 0 |
2023-06-18 08:57:56.111000
|
https://github.com/zhengdao-chen/GNN4CD
| 76 |
Supervised community detection with line graph neural networks
|
https://scholar.google.com/scholar?cluster=5008209229610559765&hl=en&as_sdt=0,5
| 3 | 2,019 |
code2seq: Generating Sequences from Structured Representations of Code
| 605 |
iclr
| 152 | 11 |
2023-06-18 08:57:56.312000
|
https://github.com/tech-srl/code2seq
| 494 |
code2seq: Generating sequences from structured representations of code
|
https://scholar.google.com/scholar?cluster=14844338714783082531&hl=en&as_sdt=0,5
| 16 | 2,019 |
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
| 1,085 |
iclr
| 53 | 0 |
2023-06-18 08:57:56.513000
|
https://github.com/klicperajo/ppnp
| 298 |
Predict then propagate: Graph neural networks meet personalized pagerank
|
https://scholar.google.com/scholar?cluster=12842465886565513517&hl=en&as_sdt=0,4
| 9 | 2,019 |
Slimmable Neural Networks
| 477 |
iclr
| 131 | 11 |
2023-06-18 08:57:56.716000
|
https://github.com/JiahuiYu/slimmable_networks
| 883 |
Slimmable neural networks
|
https://scholar.google.com/scholar?cluster=15212173000600372424&hl=en&as_sdt=0,14
| 30 | 2,019 |
Exploration by random network distillation
| 982 |
iclr
| 153 | 17 |
2023-06-18 08:57:56.917000
|
https://github.com/openai/random-network-distillation
| 811 |
Exploration by random network distillation
|
https://scholar.google.com/scholar?cluster=126098205768710278&hl=en&as_sdt=0,10
| 26 | 2,019 |
Latent Convolutional Models
| 30 |
iclr
| 5 | 2 |
2023-06-18 08:57:57.118000
|
https://github.com/srxdev0619/Latent_Convolutional_Models
| 17 |
Latent convolutional models
|
https://scholar.google.com/scholar?cluster=1201013501878383620&hl=en&as_sdt=0,5
| 5 | 2,019 |
A Universal Music Translation Network
| 137 |
iclr
| 73 | 9 |
2023-06-18 08:57:57.318000
|
https://github.com/facebookresearch/music-translation
| 446 |
A universal music translation network
|
https://scholar.google.com/scholar?cluster=6168332349111008894&hl=en&as_sdt=0,3
| 21 | 2,019 |
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
| 79 |
iclr
| 13 | 1 |
2023-06-18 08:57:57.519000
|
https://github.com/IBM/BigLittleNet
| 55 |
Big-little net: An efficient multi-scale feature representation for visual and speech recognition
|
https://scholar.google.com/scholar?cluster=555905086227832192&hl=en&as_sdt=0,38
| 9 | 2,019 |
Active Learning with Partial Feedback
| 55 |
iclr
| 4 | 1 |
2023-06-18 08:57:57.720000
|
https://github.com/peiyunh/alpf
| 11 |
Active learning with partial feedback
|
https://scholar.google.com/scholar?cluster=2828167692054854631&hl=en&as_sdt=0,34
| 2 | 2,019 |
DOM-Q-NET: Grounded RL on Structured Language
| 20 |
iclr
| 10 | 1 |
2023-06-18 08:57:57.922000
|
https://github.com/Sheng-J/DOM-Q-NET
| 44 |
Dom-q-net: Grounded rl on structured language
|
https://scholar.google.com/scholar?cluster=10126688324952353090&hl=en&as_sdt=0,47
| 2 | 2,019 |
Predicting the Generalization Gap in Deep Networks with Margin Distributions
| 169 |
iclr
| 7,332 | 1,026 |
2023-06-18 08:57:58.123000
|
https://github.com/google-research/google-research
| 29,803 |
Predicting the generalization gap in deep networks with margin distributions
|
https://scholar.google.com/scholar?cluster=13633337648471293543&hl=en&as_sdt=0,14
| 728 | 2,019 |
Measuring Compositionality in Representation Learning
| 119 |
iclr
| 6 | 3 |
2023-06-18 08:57:58.324000
|
https://github.com/jacobandreas/tre
| 67 |
Measuring compositionality in representation learning
|
https://scholar.google.com/scholar?cluster=36884338001216785&hl=en&as_sdt=0,5
| 2 | 2,019 |
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
| 2,199 |
iclr
| 138 | 9 |
2023-06-18 08:57:58.526000
|
https://github.com/hendrycks/robustness
| 846 |
Benchmarking neural network robustness to common corruptions and perturbations
|
https://scholar.google.com/scholar?cluster=4440880036617273374&hl=en&as_sdt=0,24
| 12 | 2,019 |
Learning Recurrent Binary/Ternary Weights
| 30 |
iclr
| 2 | 2 |
2023-06-18 08:57:58.727000
|
https://github.com/arashardakani/Learning-Recurrent-Binary-Ternary-Weights
| 12 |
Learning recurrent binary/ternary weights
|
https://scholar.google.com/scholar?cluster=14324986620118227094&hl=en&as_sdt=0,5
| 1 | 2,019 |
Residual Non-local Attention Networks for Image Restoration
| 573 |
iclr
| 55 | 17 |
2023-06-18 08:57:58.929000
|
https://github.com/yulunzhang/RNAN
| 327 |
Residual non-local attention networks for image restoration
|
https://scholar.google.com/scholar?cluster=5425381515618577679&hl=en&as_sdt=0,10
| 15 | 2,019 |
Meta-Learning For Stochastic Gradient MCMC
| 43 |
iclr
| 4 | 0 |
2023-06-18 08:57:59.130000
|
https://github.com/WenboGong/MetaSGMCMC
| 23 |
Meta-learning for stochastic gradient MCMC
|
https://scholar.google.com/scholar?cluster=5266885862075190072&hl=en&as_sdt=0,11
| 7 | 2,019 |
Systematic Generalization: What Is Required and Can It Be Learned?
| 169 |
iclr
| 11 | 2 |
2023-06-18 08:57:59.331000
|
https://github.com/rizar/systematic-generalization-sqoop
| 38 |
Systematic generalization: What is required and can it be learned?
|
https://scholar.google.com/scholar?cluster=376953749686735892&hl=en&as_sdt=0,5
| 6 | 2,019 |
Efficient Lifelong Learning with A-GEM
| 920 |
iclr
| 41 | 6 |
2023-06-18 08:57:59.531000
|
https://github.com/facebookresearch/agem
| 188 |
Efficient lifelong learning with a-gem
|
https://scholar.google.com/scholar?cluster=14191909055509326948&hl=en&as_sdt=0,33
| 11 | 2,019 |
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
| 175 |
iclr
| 15 | 5 |
2023-06-18 08:57:59.733000
|
https://github.com/rajarshd/Multi-Step-Reasoning
| 118 |
Multi-step retriever-reader interaction for scalable open-domain question answering
|
https://scholar.google.com/scholar?cluster=17865791345794061973&hl=en&as_sdt=0,5
| 7 | 2,019 |
Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision
| 27 |
iclr
| 0 | 1 |
2023-06-18 08:57:59.934000
|
https://github.com/jlezama/disentangling-jacobian
| 24 |
Overcoming the disentanglement vs reconstruction trade-off via Jacobian supervision
|
https://scholar.google.com/scholar?cluster=72617481773116679&hl=en&as_sdt=0,23
| 3 | 2,019 |
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
| 1,517 |
iclr
| 250 | 7 |
2023-06-18 08:58:00.136000
|
https://github.com/DeepGraphLearning/KnowledgeGraphEmbedding
| 1,051 |
Rotate: Knowledge graph embedding by relational rotation in complex space
|
https://scholar.google.com/scholar?cluster=9820389801132772086&hl=en&as_sdt=0,5
| 24 | 2,019 |
Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications
| 11 |
iclr
| 0 | 0 |
2023-06-18 08:58:00.336000
|
https://github.com/ceisenach/MPG
| 3 |
Marginal policy gradients: A unified family of estimators for bounded action spaces with applications
|
https://scholar.google.com/scholar?cluster=14825352687327812567&hl=en&as_sdt=0,36
| 4 | 2,019 |
On Self Modulation for Generative Adversarial Networks
| 104 |
iclr
| 322 | 16 |
2023-06-18 08:58:00.537000
|
https://github.com/google/compare_gan
| 1,814 |
On self modulation for generative adversarial networks
|
https://scholar.google.com/scholar?cluster=14481067201346722037&hl=en&as_sdt=0,44
| 52 | 2,019 |
Subgradient Descent Learns Orthogonal Dictionaries
| 49 |
iclr
| 1 | 0 |
2023-06-18 08:58:00.738000
|
https://github.com/sunju/ODL_L1
| 1 |
Subgradient descent learns orthogonal dictionaries
|
https://scholar.google.com/scholar?cluster=3757427846147866582&hl=en&as_sdt=0,26
| 4 | 2,019 |
A Closer Look at Few-shot Classification
| 1,513 |
iclr
| 271 | 60 |
2023-06-18 08:58:00.939000
|
https://github.com/wyharveychen/CloserLookFewShot
| 1,064 |
A closer look at few-shot classification
|
https://scholar.google.com/scholar?cluster=10436738309048088927&hl=en&as_sdt=0,4
| 20 | 2,019 |
Meta-Learning Probabilistic Inference for Prediction
| 230 |
iclr
| 13 | 0 |
2023-06-18 08:58:01.140000
|
https://github.com/Gordonjo/versa
| 68 |
Meta-learning probabilistic inference for prediction
|
https://scholar.google.com/scholar?cluster=18291407046711557858&hl=en&as_sdt=0,5
| 7 | 2,019 |
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
| 65 |
iclr
| 6 | 0 |
2023-06-18 08:58:01.341000
|
https://github.com/catniplab/tree_structured_rslds
| 30 |
Tree-structured recurrent switching linear dynamical systems for multi-scale modeling
|
https://scholar.google.com/scholar?cluster=10945679458649765039&hl=en&as_sdt=0,6
| 4 | 2,019 |
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control
| 35 |
iclr
| 7 | 1 |
2023-06-18 08:58:01.542000
|
https://github.com/robertcsordas/dnc
| 25 |
Improving differentiable neural computers through memory masking, de-allocation, and link distribution sharpness control
|
https://scholar.google.com/scholar?cluster=9465849868631633208&hl=en&as_sdt=0,45
| 1 | 2,019 |
Evaluating Robustness of Neural Networks with Mixed Integer Programming
| 665 |
iclr
| 30 | 7 |
2023-06-18 08:58:01.743000
|
https://github.com/vtjeng/MIPVerify.jl
| 106 |
Evaluating robustness of neural networks with mixed integer programming
|
https://scholar.google.com/scholar?cluster=18154476008132424293&hl=en&as_sdt=0,48
| 4 | 2,019 |
Random mesh projectors for inverse problems
| 7 |
iclr
| 4 | 0 |
2023-06-18 08:58:01.945000
|
https://github.com/swing-research/deepmesh
| 23 |
Random mesh projectors for inverse problems
|
https://scholar.google.com/scholar?cluster=1149610136001098856&hl=en&as_sdt=0,5
| 9 | 2,019 |
Complement Objective Training
| 49 |
iclr
| 9 | 2 |
2023-06-18 08:58:02.146000
|
https://github.com/henry8527/COT
| 74 |
Complement objective training
|
https://scholar.google.com/scholar?cluster=63949908447902569&hl=en&as_sdt=0,2
| 7 | 2,019 |
Trellis Networks for Sequence Modeling
| 125 |
iclr
| 63 | 1 |
2023-06-18 08:58:02.347000
|
https://github.com/locuslab/trellisnet
| 464 |
Trellis networks for sequence modeling
|
https://scholar.google.com/scholar?cluster=13782940196634240151&hl=en&as_sdt=0,5
| 24 | 2,019 |
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
| 49 |
iclr
| 0 | 0 |
2023-06-18 08:58:02.549000
|
https://github.com/uhlerlab/unbalanced_ot
| 1 |
Scalable unbalanced optimal transport using generative adversarial networks
|
https://scholar.google.com/scholar?cluster=14112773597586866494&hl=en&as_sdt=0,21
| 3 | 2,019 |
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
| 118 |
iclr
| 56 | 6 |
2023-06-18 08:58:02.750000
|
https://github.com/Atcold/pytorch-PPUU
| 188 |
Model-predictive policy learning with uncertainty regularization for driving in dense traffic
|
https://scholar.google.com/scholar?cluster=5048415252406845644&hl=en&as_sdt=0,5
| 25 | 2,019 |
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
| 472 |
iclr
| 286 | 15 |
2023-06-18 08:58:02.952000
|
https://github.com/CSAILVision/gandissect
| 1,749 |
Gan dissection: Visualizing and understanding generative adversarial networks
|
https://scholar.google.com/scholar?cluster=197925763027882731&hl=en&as_sdt=0,5
| 75 | 2,019 |
Improving MMD-GAN Training with Repulsive Loss Function
| 60 |
iclr
| 19 | 2 |
2023-06-18 08:58:03.154000
|
https://github.com/richardwth/MMD-GAN
| 82 |
Improving MMD-GAN training with repulsive loss function
|
https://scholar.google.com/scholar?cluster=5981776109708607840&hl=en&as_sdt=0,49
| 5 | 2,019 |
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
| 1,665 |
iclr
| 281 | 2 |
2023-06-18 08:58:03.354000
|
https://github.com/MIT-HAN-LAB/ProxylessNAS
| 1,379 |
Proxylessnas: Direct neural architecture search on target task and hardware
|
https://scholar.google.com/scholar?cluster=18033301425061747520&hl=en&as_sdt=0,5
| 73 | 2,019 |
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
| 34 |
iclr
| 5 | 0 |
2023-06-18 08:58:03.555000
|
https://github.com/TakaOsa/adInfoHRL
| 6 |
Hierarchical reinforcement learning via advantage-weighted information maximization
|
https://scholar.google.com/scholar?cluster=8371143208721459013&hl=en&as_sdt=0,15
| 2 | 2,019 |
Generalizable Adversarial Training via Spectral Normalization
| 122 |
iclr
| 4 | 0 |
2023-06-18 08:58:03.757000
|
https://github.com/jessemzhang/dl_spectral_normalization
| 13 |
Generalizable adversarial training via spectral normalization
|
https://scholar.google.com/scholar?cluster=16959420457208400665&hl=en&as_sdt=0,14
| 3 | 2,019 |
Deep Anomaly Detection with Outlier Exposure
| 1,070 |
iclr
| 102 | 3 |
2023-06-18 08:58:03.960000
|
https://github.com/hendrycks/outlier-exposure
| 498 |
Deep anomaly detection with outlier exposure
|
https://scholar.google.com/scholar?cluster=13915279318347653817&hl=en&as_sdt=0,5
| 19 | 2,019 |
Context-adaptive Entropy Model for End-to-end Optimized Image Compression
| 318 |
iclr
| 29 | 1 |
2023-06-18 08:58:04.161000
|
https://github.com/JooyoungLeeETRI/CA_Entropy_Model
| 134 |
Context-adaptive entropy model for end-to-end optimized image compression
|
https://scholar.google.com/scholar?cluster=17458297235582784877&hl=en&as_sdt=0,5
| 3 | 2,019 |
ProxQuant: Quantized Neural Networks via Proximal Operators
| 96 |
iclr
| 3 | 3 |
2023-06-18 08:58:04.363000
|
https://github.com/allenbai01/ProxQuant
| 23 |
Proxquant: Quantized neural networks via proximal operators
|
https://scholar.google.com/scholar?cluster=13740367040689029941&hl=en&as_sdt=0,47
| 3 | 2,019 |
Universal Transformers
| 698 |
iclr
| 3,290 | 589 |
2023-06-18 08:58:04.564000
|
https://github.com/tensorflow/tensor2tensor
| 13,768 |
Universal transformers
|
https://scholar.google.com/scholar?cluster=8443376534582904234&hl=en&as_sdt=0,44
| 461 | 2,019 |
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data
| 170 |
iclr
| 4 | 1 |
2023-06-18 08:58:04.766000
|
https://github.com/Jianbo-Lab/LCShapley
| 15 |
L-shapley and c-shapley: Efficient model interpretation for structured data
|
https://scholar.google.com/scholar?cluster=13478206087371335896&hl=en&as_sdt=0,14
| 8 | 2,019 |
Discovery of Natural Language Concepts in Individual Units of CNNs
| 19 |
iclr
| 1 | 0 |
2023-06-18 08:58:04.971000
|
https://github.com/seilna/CNN-Units-in-NLP
| 27 |
Discovery of natural language concepts in individual units of cnns
|
https://scholar.google.com/scholar?cluster=16647657304104807726&hl=en&as_sdt=0,10
| 3 | 2,019 |
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
| 31 |
iclr
| 16 | 0 |
2023-06-18 08:58:05.172000
|
https://github.com/mkachuee/Opportunistic
| 10 |
Opportunistic learning: Budgeted cost-sensitive learning from data streams
|
https://scholar.google.com/scholar?cluster=926797319762361897&hl=en&as_sdt=0,5
| 3 | 2,019 |
DARTS: Differentiable Architecture Search
| 3,734 |
iclr
| 831 | 92 |
2023-06-18 08:58:05.374000
|
https://github.com/quark0/darts
| 3,757 |
Darts: Differentiable architecture search
|
https://scholar.google.com/scholar?cluster=895422516420751823&hl=en&as_sdt=0,22
| 92 | 2,019 |
The relativistic discriminator: a key element missing from standard GAN
| 966 |
iclr
| 106 | 1 |
2023-06-18 08:58:05.575000
|
https://github.com/AlexiaJM/RelativisticGAN
| 706 |
The relativistic discriminator: a key element missing from standard GAN
|
https://scholar.google.com/scholar?cluster=9348243398459465041&hl=en&as_sdt=0,6
| 26 | 2,019 |
Quasi-hyperbolic momentum and Adam for deep learning
| 118 |
iclr
| 15 | 2 |
2023-06-18 08:58:05.776000
|
https://github.com/facebookresearch/qhoptim
| 99 |
Quasi-hyperbolic momentum and Adam for deep learning
|
https://scholar.google.com/scholar?cluster=4018448922538302075&hl=en&as_sdt=0,5
| 10 | 2,019 |
Multilingual Neural Machine Translation with Knowledge Distillation
| 205 |
iclr
| 18 | 5 |
2023-06-18 08:58:05.978000
|
https://github.com/RayeRen/multilingual-kd-pytorch
| 69 |
Multilingual neural machine translation with knowledge distillation
|
https://scholar.google.com/scholar?cluster=5753623392275205285&hl=en&as_sdt=0,33
| 4 | 2,019 |
MisGAN: Learning from Incomplete Data with Generative Adversarial Networks
| 177 |
iclr
| 18 | 2 |
2023-06-18 08:58:06.180000
|
https://github.com/steveli/misgan
| 77 |
Misgan: Learning from incomplete data with generative adversarial networks
|
https://scholar.google.com/scholar?cluster=4415656656646533426&hl=en&as_sdt=0,47
| 3 | 2,019 |
A Direct Approach to Robust Deep Learning Using Adversarial Networks
| 62 |
iclr
| 3 | 3 |
2023-06-18 08:58:06.380000
|
https://github.com/whxbergkamp/RobustDL_GAN
| 20 |
A direct approach to robust deep learning using adversarial networks
|
https://scholar.google.com/scholar?cluster=2332293430655643076&hl=en&as_sdt=0,5
| 2 | 2,019 |
ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks
| 60 |
iclr
| 9 | 0 |
2023-06-18 08:58:06.582000
|
https://github.com/mingzhang-yin/ARM-gradient
| 28 |
ARM: Augment-REINFORCE-merge gradient for stochastic binary networks
|
https://scholar.google.com/scholar?cluster=1199474822347449770&hl=en&as_sdt=0,34
| 2 | 2,019 |
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer
| 100 |
iclr
| 0 | 3 |
2023-06-18 08:58:06.783000
|
https://github.com/huangsicong/TimbreTron
| 43 |
Timbretron: A wavenet (cyclegan (cqt (audio))) pipeline for musical timbre transfer
|
https://scholar.google.com/scholar?cluster=11196022310662002190&hl=en&as_sdt=0,33
| 19 | 2,019 |
Whitening and Coloring Batch Transform for GANs
| 51 |
iclr
| 10 | 0 |
2023-06-18 08:58:06.985000
|
https://github.com/AliaksandrSiarohin/wc-gan
| 34 |
Whitening and coloring batch transform for gans
|
https://scholar.google.com/scholar?cluster=8343777033924906329&hl=en&as_sdt=0,39
| 5 | 2,019 |
Learnable Embedding Space for Efficient Neural Architecture Compression
| 46 |
iclr
| 3 | 1 |
2023-06-18 08:58:07.189000
|
https://github.com/Friedrich1006/ESNAC
| 28 |
Learnable embedding space for efficient neural architecture compression
|
https://scholar.google.com/scholar?cluster=117198627951999316&hl=en&as_sdt=0,33
| 4 | 2,019 |
A Statistical Approach to Assessing Neural Network Robustness
| 68 |
iclr
| 9 | 0 |
2023-06-18 08:58:07.390000
|
https://github.com/oval-group/statistical-robustness
| 8 |
A statistical approach to assessing neural network robustness
|
https://scholar.google.com/scholar?cluster=7897732150648450452&hl=en&as_sdt=0,5
| 12 | 2,019 |
Supervised Policy Update for Deep Reinforcement Learning
| 20 |
iclr
| 2 | 21 |
2023-06-18 08:58:07.592000
|
https://github.com/quanvuong/Supervised_Policy_Update
| 17 |
Supervised policy update for deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=9669638111330201224&hl=en&as_sdt=0,3
| 3 | 2,019 |
Learning to Schedule Communication in Multi-agent Reinforcement Learning
| 154 |
iclr
| 27 | 2 |
2023-06-18 08:58:07.794000
|
https://github.com/rhoowd/sched_net
| 68 |
Learning to schedule communication in multi-agent reinforcement learning
|
https://scholar.google.com/scholar?cluster=2430706253185717368&hl=en&as_sdt=0,10
| 5 | 2,019 |
Multi-class classification without multi-class labels
| 118 |
iclr
| 49 | 3 |
2023-06-18 08:58:07.994000
|
https://github.com/GT-RIPL/L2C
| 306 |
Multi-class classification without multi-class labels
|
https://scholar.google.com/scholar?cluster=15660059153270341215&hl=en&as_sdt=0,18
| 20 | 2,019 |
Spectral Inference Networks: Unifying Deep and Spectral Learning
| 30 |
iclr
| 27 | 2 |
2023-06-18 08:58:08.195000
|
https://github.com/deepmind/spectral_inference_networks
| 165 |
Spectral inference networks: Unifying deep and spectral learning
|
https://scholar.google.com/scholar?cluster=16660579419089969631&hl=en&as_sdt=0,10
| 14 | 2,019 |
Attentive Neural Processes
| 312 |
iclr
| 146 | 8 |
2023-06-18 08:58:08.397000
|
https://github.com/deepmind/neural-processes
| 929 |
Attentive neural processes
|
https://scholar.google.com/scholar?cluster=6519833436864425356&hl=en&as_sdt=0,5
| 42 | 2,019 |
Hierarchical interpretations for neural network predictions
| 126 |
iclr
| 21 | 2 |
2023-06-18 08:58:08.598000
|
https://github.com/csinva/hierarchical-dnn-interpretations
| 114 |
Hierarchical interpretations for neural network predictions
|
https://scholar.google.com/scholar?cluster=14523630218994203463&hl=en&as_sdt=0,33
| 10 | 2,019 |
Spreading vectors for similarity search
| 70 |
iclr
| 37 | 0 |
2023-06-18 08:58:08.799000
|
https://github.com/facebookresearch/spreadingvectors
| 308 |
Spreading vectors for similarity search
|
https://scholar.google.com/scholar?cluster=7912762574684423820&hl=en&as_sdt=0,5
| 15 | 2,019 |
Episodic Curiosity through Reachability
| 249 |
iclr
| 34 | 6 |
2023-06-18 08:58:09.001000
|
https://github.com/google-research/episodic-curiosity
| 188 |
Episodic curiosity through reachability
|
https://scholar.google.com/scholar?cluster=3202653392377789217&hl=en&as_sdt=0,34
| 12 | 2,019 |
Multilingual Neural Machine Translation With Soft Decoupled Encoding
| 53 |
iclr
| 3 | 0 |
2023-06-18 08:58:09.202000
|
https://github.com/cindyxinyiwang/SDE
| 28 |
Multilingual neural machine translation with soft decoupled encoding
|
https://scholar.google.com/scholar?cluster=1841872742547049658&hl=en&as_sdt=0,5
| 2 | 2,019 |
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
| 523 |
iclr
| 45 | 6 |
2023-06-18 08:58:09.403000
|
https://github.com/wielandbrendel/bag-of-local-features-models
| 304 |
Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
|
https://scholar.google.com/scholar?cluster=13421262728275736184&hl=en&as_sdt=0,5
| 11 | 2,019 |
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
| 82 |
iclr
| 2 | 8 |
2023-06-18 08:58:09.612000
|
https://github.com/kudkudak/dnn_sharpest_directions
| 11 |
On the relation between the sharpest directions of DNN loss and the SGD step length
|
https://scholar.google.com/scholar?cluster=3857357074541596262&hl=en&as_sdt=0,33
| 3 | 2,019 |
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
| 9 |
iclr
| 0 | 0 |
2023-06-18 08:58:09.813000
|
https://github.com/EKirschbaum/LeMoNADe
| 3 |
LeMoNADe: Learned motif and neuronal assembly detection in calcium imaging videos
|
https://scholar.google.com/scholar?cluster=16794354699308703573&hl=en&as_sdt=0,36
| 2 | 2,019 |
Multi-Domain Adversarial Learning
| 63 |
iclr
| 6 | 2 |
2023-06-18 08:58:10.015000
|
https://github.com/AltschulerWu-Lab/MuLANN
| 38 |
Multi-domain adversarial learning
|
https://scholar.google.com/scholar?cluster=12918642192245741417&hl=en&as_sdt=0,10
| 5 | 2,019 |
ProMP: Proximal Meta-Policy Search
| 193 |
iclr
| 50 | 8 |
2023-06-18 08:58:10.217000
|
https://github.com/jonasrothfuss/promp
| 222 |
Promp: Proximal meta-policy search
|
https://scholar.google.com/scholar?cluster=5271959514847376578&hl=en&as_sdt=0,33
| 14 | 2,019 |
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
| 47 |
iclr
| 4 | 1 |
2023-06-18 08:58:10.418000
|
https://github.com/Babylonpartners/fuzzymax
| 43 |
Don't settle for average, go for the max: fuzzy sets and max-pooled word vectors
|
https://scholar.google.com/scholar?cluster=17199150617564073243&hl=en&as_sdt=0,5
| 118 | 2,019 |
Learning Exploration Policies for Navigation
| 177 |
iclr
| 17 | 2 |
2023-06-18 08:58:10.620000
|
https://github.com/taochenshh/exp4nav
| 83 |
Learning exploration policies for navigation
|
https://scholar.google.com/scholar?cluster=1526633576375251578&hl=en&as_sdt=0,47
| 3 | 2,019 |
Deep Frank-Wolfe For Neural Network Optimization
| 40 |
iclr
| 10 | 0 |
2023-06-18 08:58:10.821000
|
https://github.com/oval-group/dfw
| 57 |
Deep Frank-Wolfe for neural network optimization
|
https://scholar.google.com/scholar?cluster=17584931574409094808&hl=en&as_sdt=0,33
| 12 | 2,019 |
Learning protein sequence embeddings using information from structure
| 242 |
iclr
| 72 | 3 |
2023-06-18 08:58:11.022000
|
https://github.com/tbepler/protein-sequence-embedding-iclr2019
| 239 |
Learning protein sequence embeddings using information from structure
|
https://scholar.google.com/scholar?cluster=15164585032422536283&hl=en&as_sdt=0,5
| 11 | 2,019 |
Biologically-Plausible Learning Algorithms Can Scale to Large Datasets
| 58 |
iclr
| 4 | 0 |
2023-06-18 08:58:11.224000
|
https://github.com/willwx/sign-symmetry
| 24 |
Biologically-plausible learning algorithms can scale to large datasets
|
https://scholar.google.com/scholar?cluster=10952740218459903429&hl=en&as_sdt=0,36
| 0 | 2,019 |
Learning to Make Analogies by Contrasting Abstract Relational Structure
| 78 |
iclr
| 36 | 5 |
2023-06-18 08:58:11.426000
|
https://github.com/deepmind/abstract-reasoning-matrices
| 162 |
Learning to make analogies by contrasting abstract relational structure
|
https://scholar.google.com/scholar?cluster=15521573039503233138&hl=en&as_sdt=0,5
| 24 | 2,019 |
Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion
| 28 |
iclr
| 2 | 0 |
2023-06-18 08:58:11.627000
|
https://github.com/ruiqigao/GridCell
| 16 |
Learning grid cells as vector representation of self-position coupled with matrix representation of self-motion
|
https://scholar.google.com/scholar?cluster=1267366913161335013&hl=en&as_sdt=0,33
| 4 | 2,019 |
Feature Intertwiner for Object Detection
| 19 |
iclr
| 15 | 5 |
2023-06-18 08:58:11.828000
|
https://github.com/hli2020/feature_intertwiner
| 106 |
Feature intertwiner for object detection
|
https://scholar.google.com/scholar?cluster=1331733591833237522&hl=en&as_sdt=0,5
| 8 | 2,019 |
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
| 220 |
iclr
| 17 | 9 |
2023-06-18 08:58:12.029000
|
https://github.com/chihyaoma/selfmonitoring-agent
| 113 |
Self-monitoring navigation agent via auxiliary progress estimation
|
https://scholar.google.com/scholar?cluster=5431855784757864150&hl=en&as_sdt=0,33
| 6 | 2,019 |
Kernel Change-point Detection with Auxiliary Deep Generative Models
| 65 |
iclr
| 13 | 3 |
2023-06-18 08:58:12.230000
|
https://github.com/OctoberChang/klcpd_code
| 46 |
Kernel change-point detection with auxiliary deep generative models
|
https://scholar.google.com/scholar?cluster=15362141737124631231&hl=en&as_sdt=0,46
| 2 | 2,019 |
Auxiliary Variational MCMC
| 26 |
iclr
| 2 | 0 |
2023-06-18 08:58:12.431000
|
https://github.com/AVMCMC/AuxiliaryVariationalMCMC
| 17 |
Auxiliary variational MCMC
|
https://scholar.google.com/scholar?cluster=16399175938915448128&hl=en&as_sdt=0,46
| 1 | 2,019 |
Interpolation-Prediction Networks for Irregularly Sampled Time Series
| 114 |
iclr
| 14 | 5 |
2023-06-18 08:58:12.632000
|
https://github.com/mlds-lab/interp-net
| 75 |
Interpolation-prediction networks for irregularly sampled time series
|
https://scholar.google.com/scholar?cluster=15477406781147246766&hl=en&as_sdt=0,5
| 9 | 2,019 |
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
| 49 |
iclr
| 4 | 0 |
2023-06-18 08:58:12.848000
|
https://github.com/cambridge-mlg/miracle
| 18 |
Minimal random code learning: Getting bits back from compressed model parameters
|
https://scholar.google.com/scholar?cluster=17962712491875468296&hl=en&as_sdt=0,5
| 3 | 2,019 |
Equi-normalization of Neural Networks
| 369 |
iclr
| 13 | 0 |
2023-06-18 08:58:13.050000
|
https://github.com/facebookresearch/enorm
| 114 |
Data-free quantization through weight equalization and bias correction
|
https://scholar.google.com/scholar?cluster=7650143789920544723&hl=en&as_sdt=0,33
| 10 | 2,019 |
A Variational Inequality Perspective on Generative Adversarial Networks
| 341 |
iclr
| 11 | 0 |
2023-06-18 08:58:13.251000
|
https://github.com/GauthierGidel/Variational-Inequality-GAN
| 36 |
A variational inequality perspective on generative adversarial networks
|
https://scholar.google.com/scholar?cluster=6445881932716952872&hl=en&as_sdt=0,24
| 5 | 2,019 |
GamePad: A Learning Environment for Theorem Proving
| 83 |
iclr
| 15 | 12 |
2023-06-18 08:58:13.453000
|
https://github.com/ml4tp/gamepad
| 66 |
Gamepad: A learning environment for theorem proving
|
https://scholar.google.com/scholar?cluster=10460600857870546205&hl=en&as_sdt=0,5
| 9 | 2,019 |
Large-Scale Study of Curiosity-Driven Learning
| 677 |
iclr
| 178 | 14 |
2023-06-18 08:58:13.661000
|
https://github.com/openai/large-scale-curiosity
| 783 |
Large-scale study of curiosity-driven learning
|
https://scholar.google.com/scholar?cluster=6931272873542879959&hl=en&as_sdt=0,5
| 63 | 2,019 |
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
| 138 |
iclr
| 135 | 9 |
2023-06-18 08:58:13.866000
|
https://github.com/mila-iqia/babyai
| 609 |
Babyai: A platform to study the sample efficiency of grounded language learning
|
https://scholar.google.com/scholar?cluster=16615836502291630253&hl=en&as_sdt=0,33
| 36 | 2,019 |
An Empirical study of Binary Neural Networks' Optimisation
| 72 |
iclr
| 10 | 0 |
2023-06-18 08:58:14.067000
|
https://github.com/mi-lad/studying-binary-neural-networks
| 51 |
An empirical study of binary neural networks' optimisation
|
https://scholar.google.com/scholar?cluster=9499204720789675846&hl=en&as_sdt=0,31
| 5 | 2,019 |
DeepOBS: A Deep Learning Optimizer Benchmark Suite
| 47 |
iclr
| 34 | 16 |
2023-06-18 08:58:14.269000
|
https://github.com/fsschneider/deepobs
| 97 |
DeepOBS: A deep learning optimizer benchmark suite
|
https://scholar.google.com/scholar?cluster=10657953635405668036&hl=en&as_sdt=0,5
| 4 | 2,019 |
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