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PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
| 8 |
iclr
| 698 | 357 |
2023-06-18 09:43:58.109000
|
https://github.com/awslabs/gluon-ts
| 3,623 |
PSA-GAN: Progressive self attention GANs for synthetic time series
|
https://scholar.google.com/scholar?cluster=18377991298418272065&hl=en&as_sdt=0,5
| 70 | 2,022 |
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
| 15 |
iclr
| 3 | 0 |
2023-06-18 09:43:58.313000
|
https://github.com/unrealtracking/tom2c
| 31 |
Tom2c: Target-oriented multi-agent communication and cooperation with theory of mind
|
https://scholar.google.com/scholar?cluster=13700850065152438149&hl=en&as_sdt=0,41
| 2 | 2,022 |
Better Supervisory Signals by Observing Learning Paths
| 9 |
iclr
| 1 | 0 |
2023-06-18 09:43:58.518000
|
https://github.com/joshua-ren/better_supervisory_signal
| 3 |
Better supervisory signals by observing learning paths
|
https://scholar.google.com/scholar?cluster=4997668798655366002&hl=en&as_sdt=0,5
| 2 | 2,022 |
TAda! Temporally-Adaptive Convolutions for Video Understanding
| 19 |
iclr
| 0 | 0 |
2023-06-18 09:43:58.722000
|
https://github.com/alibaba-mmai-research/pytorch-video-understanding
| 0 |
Tada! temporally-adaptive convolutions for video understanding
|
https://scholar.google.com/scholar?cluster=1325383719378653431&hl=en&as_sdt=0,44
| 1 | 2,022 |
Learning a subspace of policies for online adaptation in Reinforcement Learning
| 7 |
iclr
| 42 | 0 |
2023-06-18 09:43:58.924000
|
https://github.com/facebookresearch/salina
| 424 |
Learning a subspace of policies for online adaptation in reinforcement learning
|
https://scholar.google.com/scholar?cluster=8112991031910355476&hl=en&as_sdt=0,5
| 12 | 2,022 |
Gaussian Mixture Convolution Networks
| 1 |
iclr
| 0 | 0 |
2023-06-18 09:43:59.128000
|
https://github.com/cg-tuwien/gaussian-mixture-convolution-networks
| 0 |
Gaussian Mixture Convolution Networks
|
https://scholar.google.com/scholar?cluster=3285204199081775267&hl=en&as_sdt=0,33
| 3 | 2,022 |
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior
| 44 |
iclr
| 133 | 24 |
2023-06-18 09:43:59.330000
|
https://github.com/microsoft/NeuralSpeech
| 1,007 |
Priorgrad: Improving conditional denoising diffusion models with data-driven adaptive prior
|
https://scholar.google.com/scholar?cluster=15402049708647149308&hl=en&as_sdt=0,33
| 30 | 2,022 |
UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning
| 78 |
iclr
| 99 | 4 |
2023-06-18 09:43:59.534000
|
https://github.com/sense-x/uniformer
| 656 |
Uniformer: Unified transformer for efficient spatiotemporal representation learning
|
https://scholar.google.com/scholar?cluster=13061863280402646662&hl=en&as_sdt=0,18
| 11 | 2,022 |
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
| 1 |
iclr
| 0 | 0 |
2023-06-18 09:43:59.737000
|
https://github.com/leejaehoon2016/lord
| 1 |
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
|
https://scholar.google.com/scholar?cluster=9583526015589000772&hl=en&as_sdt=0,43
| 1 | 2,022 |
Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
| 9 |
iclr
| 1 | 0 |
2023-06-18 09:43:59.940000
|
https://github.com/thanhnguyentang/offline_neural_bandits
| 7 |
Offline neural contextual bandits: Pessimism, optimization and generalization
|
https://scholar.google.com/scholar?cluster=11879917374324366970&hl=en&as_sdt=0,33
| 1 | 2,022 |
CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability
| 19 |
iclr
| 2 | 0 |
2023-06-18 09:44:00.143000
|
https://github.com/ml-research/CLEVA-Compass
| 17 |
CLEVA-compass: A continual learning evaluation assessment compass to promote research transparency and comparability
|
https://scholar.google.com/scholar?cluster=16474577021609169344&hl=en&as_sdt=0,10
| 3 | 2,022 |
Learning to Extend Molecular Scaffolds with Structural Motifs
| 35 |
iclr
| 30 | 6 |
2023-06-18 09:44:00.352000
|
https://github.com/microsoft/molecule-generation
| 182 |
Learning to extend molecular scaffolds with structural motifs
|
https://scholar.google.com/scholar?cluster=16834575414277010470&hl=en&as_sdt=0,33
| 11 | 2,022 |
Gradient Matching for Domain Generalization
| 127 |
iclr
| 7 | 4 |
2023-06-18 09:44:00.583000
|
https://github.com/YugeTen/fish
| 100 |
Gradient matching for domain generalization
|
https://scholar.google.com/scholar?cluster=2851826454893571179&hl=en&as_sdt=0,48
| 3 | 2,022 |
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
| 1 |
iclr
| 2 | 0 |
2023-06-18 09:44:00.786000
|
https://github.com/ALRhub/HiP-RSSM
| 4 |
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
|
https://scholar.google.com/scholar?cluster=11250070216520072781&hl=en&as_sdt=0,5
| 6 | 2,022 |
Graph Neural Network Guided Local Search for the Traveling Salesperson Problem
| 22 |
iclr
| 6 | 1 |
2023-06-18 09:44:00.989000
|
https://github.com/proroklab/gnngls
| 17 |
Graph neural network guided local search for the traveling salesperson problem
|
https://scholar.google.com/scholar?cluster=7438825804269654854&hl=en&as_sdt=0,33
| 4 | 2,022 |
On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks
| 28 |
iclr
| 2 | 0 |
2023-06-18 09:44:01.192000
|
https://github.com/martius-lab/beta-nll
| 17 |
On the pitfalls of heteroscedastic uncertainty estimation with probabilistic neural networks
|
https://scholar.google.com/scholar?cluster=12019013391257516150&hl=en&as_sdt=0,44
| 4 | 2,022 |
Label-Efficient Semantic Segmentation with Diffusion Models
| 99 |
iclr
| 48 | 3 |
2023-06-18 09:44:01.394000
|
https://github.com/yandex-research/ddpm-segmentation
| 501 |
Label-efficient semantic segmentation with diffusion models
|
https://scholar.google.com/scholar?cluster=15536080386381166237&hl=en&as_sdt=0,5
| 7 | 2,022 |
Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization
| 14 |
iclr
| 4 | 0 |
2023-06-18 09:44:01.598000
|
https://github.com/xi-l/pmoco
| 28 |
Pareto set learning for neural multi-objective combinatorial optimization
|
https://scholar.google.com/scholar?cluster=10853796196468498279&hl=en&as_sdt=0,21
| 1 | 2,022 |
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
| 20 |
iclr
| 2 | 0 |
2023-06-18 09:44:01.802000
|
https://github.com/lfhase/gia-hao
| 23 |
Understanding and improving graph injection attack by promoting unnoticeability
|
https://scholar.google.com/scholar?cluster=11546054136768832920&hl=en&as_sdt=0,10
| 3 | 2,022 |
Learning to Guide and to be Guided in the Architect-Builder Problem
| 2 |
iclr
| 0 | 0 |
2023-06-18 09:44:02.005000
|
https://github.com/flowersteam/architect-builder-abig
| 5 |
Learning to guide and to be guided in the architect-builder problem
|
https://scholar.google.com/scholar?cluster=8756083495202115468&hl=en&as_sdt=0,43
| 7 | 2,022 |
Phase Collapse in Neural Networks
| 3 |
iclr
| 0 | 0 |
2023-06-18 09:44:02.209000
|
https://github.com/florentinguth/phasecollapse
| 6 |
Phase Collapse in Neural Networks
|
https://scholar.google.com/scholar?cluster=2536829200192569115&hl=en&as_sdt=0,5
| 1 | 2,022 |
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training
| 9 |
iclr
| 93 | 15 |
2023-06-18 09:44:02.414000
|
https://github.com/huawei-noah/Speech-Backbones
| 396 |
SPIRAL: Self-supervised perturbation-invariant representation learning for speech pre-training
|
https://scholar.google.com/scholar?cluster=7704368190007822312&hl=en&as_sdt=0,49
| 26 | 2,022 |
Enhancing Cross-lingual Transfer by Manifold Mixup
| 15 |
iclr
| 2 | 1 |
2023-06-18 09:44:02.621000
|
https://github.com/yhy1117/x-mixup
| 17 |
Enhancing cross-lingual transfer by manifold mixup
|
https://scholar.google.com/scholar?cluster=13560869660966503554&hl=en&as_sdt=0,5
| 1 | 2,022 |
Curvature-Guided Dynamic Scale Networks for Multi-View Stereo
| 7 |
iclr
| 6 | 0 |
2023-06-18 09:44:02.825000
|
https://github.com/truongkhang/cds-mvsnet
| 95 |
Curvature-guided dynamic scale networks for multi-view stereo
|
https://scholar.google.com/scholar?cluster=4920966031938804836&hl=en&as_sdt=0,5
| 6 | 2,022 |
Exploring extreme parameter compression for pre-trained language models
| 5 |
iclr
| 0 | 0 |
2023-06-18 09:44:03.029000
|
https://github.com/twinkle0331/xcompression
| 17 |
Exploring extreme parameter compression for pre-trained language models
|
https://scholar.google.com/scholar?cluster=10120048061999340751&hl=en&as_sdt=0,7
| 3 | 2,022 |
Scale Mixtures of Neural Network Gaussian Processes
| 1 |
iclr
| 1 | 0 |
2023-06-18 09:44:03.232000
|
https://github.com/Hyungi-Lee/Scale-Mixtures-of-Neural-Network-Gaussian-Processes
| 1 |
Scale mixtures of neural network Gaussian processes
|
https://scholar.google.com/scholar?cluster=1361989022651133185&hl=en&as_sdt=0,46
| 1 | 2,022 |
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
| 25 |
iclr
| 32 | 4 |
2023-06-18 09:44:03.435000
|
https://github.com/tsy935/eeg-gnn-ssl
| 74 |
Self-supervised graph neural networks for improved electroencephalographic seizure analysis
|
https://scholar.google.com/scholar?cluster=12685516138349084049&hl=en&as_sdt=0,5
| 2 | 2,022 |
MonoDistill: Learning Spatial Features for Monocular 3D Object Detection
| 33 |
iclr
| 4 | 4 |
2023-06-18 09:44:03.639000
|
https://github.com/monster-ghost/monodistill
| 55 |
Monodistill: Learning spatial features for monocular 3d object detection
|
https://scholar.google.com/scholar?cluster=14851758558366902605&hl=en&as_sdt=0,10
| 6 | 2,022 |
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
| 87 |
iclr
| 119 | 35 |
2023-06-18 09:44:03.844000
|
https://github.com/mhamilton723/STEGO
| 598 |
Unsupervised semantic segmentation by distilling feature correspondences
|
https://scholar.google.com/scholar?cluster=8638628527714032897&hl=en&as_sdt=0,5
| 13 | 2,022 |
Graph-Relational Domain Adaptation
| 12 |
iclr
| 2 | 0 |
2023-06-18 09:44:04.047000
|
https://github.com/wang-ml-lab/grda
| 35 |
Graph-relational domain adaptation
|
https://scholar.google.com/scholar?cluster=14268209839215754091&hl=en&as_sdt=0,47
| 3 | 2,022 |
Generalized Kernel Thinning
| 13 |
iclr
| 2 | 0 |
2023-06-18 09:44:04.251000
|
https://github.com/microsoft/goodpoints
| 31 |
Generalized kernel thinning
|
https://scholar.google.com/scholar?cluster=11005160819787759649&hl=en&as_sdt=0,23
| 9 | 2,022 |
How Much Can CLIP Benefit Vision-and-Language Tasks?
| 205 |
iclr
| 30 | 5 |
2023-06-18 09:44:04.454000
|
https://github.com/clip-vil/CLIP-ViL
| 344 |
How much can clip benefit vision-and-language tasks?
|
https://scholar.google.com/scholar?cluster=6434466912782408523&hl=en&as_sdt=0,22
| 9 | 2,022 |
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication
| 20 |
iclr
| 5 | 0 |
2023-06-18 09:44:04.658000
|
https://github.com/RICE-EIC/PipeGCN
| 23 |
PipeGCN: Efficient full-graph training of graph convolutional networks with pipelined feature communication
|
https://scholar.google.com/scholar?cluster=5927794723979100407&hl=en&as_sdt=0,11
| 3 | 2,022 |
Adversarial Unlearning of Backdoors via Implicit Hypergradient
| 56 |
iclr
| 11 | 0 |
2023-06-18 09:44:04.862000
|
https://github.com/yizeng623/i-bau_adversarial_unlearning_of-backdoors_via_implicit_hypergradient
| 33 |
Adversarial unlearning of backdoors via implicit hypergradient
|
https://scholar.google.com/scholar?cluster=4522682349845084821&hl=en&as_sdt=0,5
| 2 | 2,022 |
Graph Neural Networks with Learnable Structural and Positional Representations
| 89 |
iclr
| 27 | 1 |
2023-06-18 09:44:05.065000
|
https://github.com/vijaydwivedi75/gnn-lspe
| 200 |
Graph neural networks with learnable structural and positional representations
|
https://scholar.google.com/scholar?cluster=6297596382755615056&hl=en&as_sdt=0,44
| 4 | 2,022 |
Zero-Shot Self-Supervised Learning for MRI Reconstruction
| 22 |
iclr
| 3 | 0 |
2023-06-18 09:44:05.268000
|
https://github.com/byaman14/ZS-SSL
| 23 |
Zero-shot self-supervised learning for MRI reconstruction
|
https://scholar.google.com/scholar?cluster=8560658023776593054&hl=en&as_sdt=0,44
| 1 | 2,022 |
Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction
| 17 |
iclr
| 8 | 1 |
2023-06-18 09:44:05.471000
|
https://github.com/mtang724/nwr-gae
| 30 |
Graph auto-encoder via neighborhood wasserstein reconstruction
|
https://scholar.google.com/scholar?cluster=13928177988336343335&hl=en&as_sdt=0,36
| 1 | 2,022 |
On Redundancy and Diversity in Cell-based Neural Architecture Search
| 11 |
iclr
| 1 | 0 |
2023-06-18 09:44:05.675000
|
https://github.com/xingchenwan/cell-based-nas-analysis
| 4 |
On redundancy and diversity in cell-based neural architecture search
|
https://scholar.google.com/scholar?cluster=1908527067267342822&hl=en&as_sdt=0,19
| 1 | 2,022 |
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers
| 11 |
iclr
| 2 | 0 |
2023-06-18 09:44:05.881000
|
https://github.com/deepmind/dks
| 47 |
Deep learning without shortcuts: Shaping the kernel with tailored rectifiers
|
https://scholar.google.com/scholar?cluster=3445605992837467130&hl=en&as_sdt=0,5
| 5 | 2,022 |
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias
| 5 |
iclr
| 0 | 0 |
2023-06-18 09:44:06.084000
|
https://github.com/virajmehta/vae-training
| 0 |
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias
|
https://scholar.google.com/scholar?cluster=12373917463845389421&hl=en&as_sdt=0,10
| 2 | 2,022 |
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models
| 9 |
iclr
| 2 | 1 |
2023-06-18 09:44:06.287000
|
https://github.com/cliang1453/sage
| 23 |
No parameters left behind: Sensitivity guided adaptive learning rate for training large transformer models
|
https://scholar.google.com/scholar?cluster=17779998406940212088&hl=en&as_sdt=0,44
| 1 | 2,022 |
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
| 185 |
iclr
| 72 | 12 |
2023-06-18 09:44:06.491000
|
https://github.com/ermongroup/SDEdit
| 665 |
Sdedit: Guided image synthesis and editing with stochastic differential equations
|
https://scholar.google.com/scholar?cluster=2574908324079451158&hl=en&as_sdt=0,5
| 20 | 2,022 |
Generalizing Few-Shot NAS with Gradient Matching
| 9 |
iclr
| 1 | 2 |
2023-06-18 09:44:06.695000
|
https://github.com/skhu101/GM-NAS
| 17 |
Generalizing few-shot nas with gradient matching
|
https://scholar.google.com/scholar?cluster=10558207332757804678&hl=en&as_sdt=0,5
| 2 | 2,022 |
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
| 30 |
iclr
| 9 | 0 |
2023-06-18 09:44:06.898000
|
https://github.com/vita-group/random_pruning
| 61 |
The unreasonable effectiveness of random pruning: Return of the most naive baseline for sparse training
|
https://scholar.google.com/scholar?cluster=15333598630551716586&hl=en&as_sdt=0,42
| 2 | 2,022 |
Training Transition Policies via Distribution Matching for Complex Tasks
| 1 |
iclr
| 1 | 0 |
2023-06-18 09:44:07.101000
|
https://github.com/shashacks/irl_transition
| 4 |
Training Transition Policies via Distribution Matching for Complex Tasks
|
https://scholar.google.com/scholar?cluster=11055212331250216883&hl=en&as_sdt=0,44
| 1 | 2,022 |
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?
| 1 |
iclr
| 0 | 0 |
2023-06-18 09:44:07.304000
|
https://github.com/msf235/group-invariant-perceptron-capacity
| 1 |
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?
|
https://scholar.google.com/scholar?cluster=14636029131782047015&hl=en&as_sdt=0,5
| 1 | 2,022 |
Learning Weakly-supervised Contrastive Representations
| 8 |
iclr
| 3 | 1 |
2023-06-18 09:44:07.508000
|
https://github.com/crazy-jack/cl-infonce
| 12 |
Learning weakly-supervised contrastive representations
|
https://scholar.google.com/scholar?cluster=16658448865785997630&hl=en&as_sdt=0,5
| 2 | 2,022 |
Conditional Contrastive Learning with Kernel
| 13 |
iclr
| 0 | 0 |
2023-06-18 09:44:07.713000
|
https://github.com/crazy-jack/cclk-release
| 7 |
Conditional contrastive learning with kernel
|
https://scholar.google.com/scholar?cluster=14273339449801655874&hl=en&as_sdt=0,5
| 2 | 2,022 |
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations
| 13 |
iclr
| 15 | 1 |
2023-06-18 09:44:07.916000
|
https://github.com/amzn/trans-encoder
| 119 |
Trans-Encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations
|
https://scholar.google.com/scholar?cluster=14228123078269305039&hl=en&as_sdt=0,5
| 7 | 2,022 |
Path Integral Sampler: A Stochastic Control Approach For Sampling
| 12 |
iclr
| 4 | 0 |
2023-06-18 09:44:08.120000
|
https://github.com/qsh-zh/pis
| 30 |
Path integral sampler: a stochastic control approach for sampling
|
https://scholar.google.com/scholar?cluster=17129588743049853976&hl=en&as_sdt=0,14
| 2 | 2,022 |
Optimizer Amalgamation
| 1 |
iclr
| 0 | 0 |
2023-06-18 09:44:08.323000
|
https://github.com/vita-group/optimizeramalgamation
| 4 |
Optimizer Amalgamation
|
https://scholar.google.com/scholar?cluster=12945586216189211719&hl=en&as_sdt=0,44
| 8 | 2,022 |
P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts
| 11 |
iclr
| 439 | 67 |
2023-06-18 09:44:08.530000
|
https://github.com/makcedward/nlpaug
| 3,994 |
P-adapters: Robustly extracting factual information from language models with diverse prompts
|
https://scholar.google.com/scholar?cluster=1866558597598479566&hl=en&as_sdt=0,10
| 41 | 2,022 |
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming
| 15 |
iclr
| 2 | 0 |
2023-06-18 09:44:08.733000
|
https://github.com/core-robotics-lab/infopg
| 3 |
Iterated reasoning with mutual information in cooperative and byzantine decentralized teaming
|
https://scholar.google.com/scholar?cluster=15432684675510350837&hl=en&as_sdt=0,5
| 1 | 2,022 |
Hindsight Foresight Relabeling for Meta-Reinforcement Learning
| 3 |
iclr
| 1 | 0 |
2023-06-18 09:44:08.936000
|
https://github.com/michaelwan11/hfr
| 6 |
Hindsight foresight relabeling for meta-reinforcement learning
|
https://scholar.google.com/scholar?cluster=4008449180583505870&hl=en&as_sdt=0,44
| 2 | 2,022 |
LoRA: Low-Rank Adaptation of Large Language Models
| 437 |
iclr
| 260 | 54 |
2023-06-18 09:44:09.140000
|
https://github.com/microsoft/LoRA
| 4,922 |
Lora: Low-rank adaptation of large language models
|
https://scholar.google.com/scholar?cluster=12933070321040047372&hl=en&as_sdt=0,5
| 42 | 2,022 |
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data
| 24 |
iclr
| 7,332 | 1,026 |
2023-06-18 09:44:09.344000
|
https://github.com/google-research/google-research
| 29,803 |
Trail: Near-optimal imitation learning with suboptimal data
|
https://scholar.google.com/scholar?cluster=13031874054704232682&hl=en&as_sdt=0,5
| 728 | 2,022 |
Conditional Image Generation by Conditioning Variational Auto-Encoders
| 4 |
iclr
| 0 | 0 |
2023-06-18 09:44:09.558000
|
https://github.com/plai-group/ipa
| 9 |
Conditional image generation by conditioning variational auto-encoders
|
https://scholar.google.com/scholar?cluster=2137944836024750208&hl=en&as_sdt=0,11
| 3 | 2,022 |
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
| 16 |
iclr
| 7 | 1 |
2023-06-18 09:44:09.762000
|
https://github.com/keiradams/chiro
| 42 |
Learning 3d representations of molecular chirality with invariance to bond rotations
|
https://scholar.google.com/scholar?cluster=12423983501128658396&hl=en&as_sdt=0,10
| 1 | 2,022 |
Neural Methods for Logical Reasoning over Knowledge Graphs
| 10 |
iclr
| 0 | 0 |
2023-06-18 09:44:09.967000
|
https://github.com/amayuelas/NNKGReasoning
| 8 |
Neural methods for logical reasoning over knowledge graphs
|
https://scholar.google.com/scholar?cluster=11327310359192902619&hl=en&as_sdt=0,33
| 2 | 2,022 |
Unified Visual Transformer Compression
| 34 |
iclr
| 3 | 4 |
2023-06-18 09:44:10.170000
|
https://github.com/VITA-Group/UVC
| 33 |
Unified visual transformer compression
|
https://scholar.google.com/scholar?cluster=1947517498926990042&hl=en&as_sdt=0,31
| 8 | 2,022 |
PAC Prediction Sets Under Covariate Shift
| 14 |
iclr
| 1 | 0 |
2023-06-18 09:44:10.377000
|
https://github.com/sangdon/pac-ps-w
| 3 |
PAC prediction sets under covariate shift
|
https://scholar.google.com/scholar?cluster=15533837197233330118&hl=en&as_sdt=0,10
| 2 | 2,022 |
One After Another: Learning Incremental Skills for a Changing World
| 3 |
iclr
| 0 | 0 |
2023-06-18 09:44:10.580000
|
https://github.com/notmahi/disk
| 14 |
One After Another: Learning Incremental Skills for a Changing World
|
https://scholar.google.com/scholar?cluster=7328413134619288217&hl=en&as_sdt=0,50
| 1 | 2,022 |
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
| 23 |
iclr
| 26 | 4 |
2023-06-18 09:44:10.784000
|
https://github.com/mims-harvard/raindrop
| 94 |
Graph-guided network for irregularly sampled multivariate time series
|
https://scholar.google.com/scholar?cluster=1644836195235087871&hl=en&as_sdt=0,43
| 6 | 2,022 |
FILM: Following Instructions in Language with Modular Methods
| 59 |
iclr
| 25 | 21 |
2023-06-18 09:44:10.986000
|
https://github.com/soyeonm/film
| 82 |
Film: Following instructions in language with modular methods
|
https://scholar.google.com/scholar?cluster=5571461167414719963&hl=en&as_sdt=0,33
| 3 | 2,022 |
Monotonic Differentiable Sorting Networks
| 8 |
iclr
| 2 | 2 |
2023-06-18 09:44:11.189000
|
https://github.com/Felix-Petersen/diffsort
| 82 |
Monotonic differentiable sorting networks
|
https://scholar.google.com/scholar?cluster=11509121699002053809&hl=en&as_sdt=0,5
| 3 | 2,022 |
Model Agnostic Interpretability for Multiple Instance Learning
| 1 |
iclr
| 1 | 0 |
2023-06-18 09:44:11.393000
|
https://github.com/jaearly/milli
| 10 |
Model Agnostic Interpretability for Multiple Instance Learning
|
https://scholar.google.com/scholar?cluster=4511846077135181099&hl=en&as_sdt=0,5
| 2 | 2,022 |
When, Why, and Which Pretrained GANs Are Useful?
| 9 |
iclr
| 2 | 2 |
2023-06-18 09:44:11.596000
|
https://github.com/yandex-research/gan-transfer
| 20 |
When, Why, and Which Pretrained GANs Are Useful?
|
https://scholar.google.com/scholar?cluster=1749765519247284522&hl=en&as_sdt=0,5
| 0 | 2,022 |
Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients
| 19 |
iclr
| 2 | 2 |
2023-06-18 09:44:11.799000
|
https://github.com/lunanbit/fedul
| 25 |
Federated learning from only unlabeled data with class-conditional-sharing clients
|
https://scholar.google.com/scholar?cluster=10078372194856107683&hl=en&as_sdt=0,31
| 2 | 2,022 |
Transformer Embeddings of Irregularly Spaced Events and Their Participants
| 10 |
iclr
| 7 | 0 |
2023-06-18 09:44:12.002000
|
https://github.com/yangalan123/anhp-andtt
| 35 |
Transformer embeddings of irregularly spaced events and their participants
|
https://scholar.google.com/scholar?cluster=901539587982376122&hl=en&as_sdt=0,34
| 3 | 2,022 |
Fast Model Editing at Scale
| 85 |
iclr
| 23 | 2 |
2023-06-18 09:44:12.205000
|
https://github.com/eric-mitchell/mend
| 165 |
Fast model editing at scale
|
https://scholar.google.com/scholar?cluster=16012977472608893653&hl=en&as_sdt=0,5
| 6 | 2,022 |
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums
| 1 |
iclr
| 0 | 0 |
2023-06-18 09:44:12.408000
|
https://github.com/opensource12345678/why_cosine_works
| 1 |
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums
|
https://scholar.google.com/scholar?cluster=11376476558105496833&hl=en&as_sdt=0,34
| 1 | 2,022 |
On Incorporating Inductive Biases into VAEs
| 6 |
iclr
| 0 | 0 |
2023-06-18 09:44:12.611000
|
https://github.com/ningmiao/intel-vae
| 2 |
On incorporating inductive biases into VAEs
|
https://scholar.google.com/scholar?cluster=15494277357139593439&hl=en&as_sdt=0,5
| 1 | 2,022 |
On the Existence of Universal Lottery Tickets
| 17 |
iclr
| 0 | 0 |
2023-06-18 09:44:12.814000
|
https://github.com/relationalml/universallt
| 1 |
On the existence of universal lottery tickets
|
https://scholar.google.com/scholar?cluster=4071511330404748656&hl=en&as_sdt=0,48
| 0 | 2,022 |
Pre-training Molecular Graph Representation with 3D Geometry
| 94 |
iclr
| 16 | 3 |
2023-06-18 09:44:13.018000
|
https://github.com/chao1224/graphmvp
| 114 |
Pre-training molecular graph representation with 3d geometry
|
https://scholar.google.com/scholar?cluster=12269574784453036678&hl=en&as_sdt=0,5
| 5 | 2,022 |
Taming Sparsely Activated Transformer with Stochastic Experts
| 28 |
iclr
| 5 | 2 |
2023-06-18 09:44:13.220000
|
https://github.com/microsoft/stochastic-mixture-of-experts
| 45 |
Taming sparsely activated transformer with stochastic experts
|
https://scholar.google.com/scholar?cluster=2351258339090586276&hl=en&as_sdt=0,44
| 7 | 2,022 |
Hierarchical Variational Memory for Few-shot Learning Across Domains
| 10 |
iclr
| 0 | 1 |
2023-06-18 09:44:13.424000
|
https://github.com/ydu-uva/hiermemory
| 2 |
Hierarchical variational memory for few-shot learning across domains
|
https://scholar.google.com/scholar?cluster=1702336741267321422&hl=en&as_sdt=0,47
| 1 | 2,022 |
Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction
| 91 |
iclr
| 94 | 39 |
2023-06-18 09:44:13.627000
|
https://github.com/facebookresearch/av_hubert
| 563 |
Learning audio-visual speech representation by masked multimodal cluster prediction
|
https://scholar.google.com/scholar?cluster=10092601406427600448&hl=en&as_sdt=0,5
| 14 | 2,022 |
An Explanation of In-context Learning as Implicit Bayesian Inference
| 116 |
iclr
| 12 | 1 |
2023-06-18 09:44:13.831000
|
https://github.com/p-lambda/incontext-learning
| 56 |
An explanation of in-context learning as implicit bayesian inference
|
https://scholar.google.com/scholar?cluster=15144987797628396832&hl=en&as_sdt=0,5
| 13 | 2,022 |
Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System
| 33 |
iclr
| 4 | 1 |
2023-06-18 09:44:14.035000
|
https://github.com/NeurAI-Lab/CLS-ER
| 25 |
Learning fast, learning slow: A general continual learning method based on complementary learning system
|
https://scholar.google.com/scholar?cluster=2178714881439527742&hl=en&as_sdt=0,5
| 2 | 2,022 |
What Do We Mean by Generalization in Federated Learning?
| 26 |
iclr
| 177 | 12 |
2023-06-18 09:44:14.238000
|
https://github.com/google-research/federated
| 555 |
What do we mean by generalization in federated learning?
|
https://scholar.google.com/scholar?cluster=7455517891491181404&hl=en&as_sdt=0,43
| 26 | 2,022 |
Autonomous Reinforcement Learning: Formalism and Benchmarking
| 14 |
iclr
| 4 | 0 |
2023-06-18 09:44:14.442000
|
https://github.com/architsharma97/earl_benchmark
| 33 |
Autonomous reinforcement learning: Formalism and benchmarking
|
https://scholar.google.com/scholar?cluster=9771677506162307722&hl=en&as_sdt=0,5
| 7 | 2,022 |
Label Leakage and Protection in Two-party Split Learning
| 67 |
iclr
| 172 | 72 |
2023-06-18 09:44:14.645000
|
https://github.com/bytedance/fedlearner
| 844 |
Label leakage and protection in two-party split learning
|
https://scholar.google.com/scholar?cluster=4111278201202932828&hl=en&as_sdt=0,33
| 28 | 2,022 |
CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation
| 8 |
iclr
| 3 | 0 |
2023-06-18 09:44:14.850000
|
https://github.com/ppashakhanloo/CodeTrek
| 22 |
Codetrek: Flexible modeling of code using an extensible relational representation
|
https://scholar.google.com/scholar?cluster=10059664661976088389&hl=en&as_sdt=0,5
| 2 | 2,022 |
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
| 97 |
iclr
| 21 | 8 |
2023-06-18 09:44:15.052000
|
https://github.com/yang-song/score_inverse_problems
| 142 |
Solving inverse problems in medical imaging with score-based generative models
|
https://scholar.google.com/scholar?cluster=16734106149627333689&hl=en&as_sdt=0,47
| 5 | 2,022 |
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis
| 34 |
iclr
| 26 | 1 |
2023-06-18 09:44:15.255000
|
https://github.com/tencent-ailab/bddm
| 188 |
BDDM: Bilateral denoising diffusion models for fast and high-quality speech synthesis
|
https://scholar.google.com/scholar?cluster=9819196866307115344&hl=en&as_sdt=0,5
| 8 | 2,022 |
The Uncanny Similarity of Recurrence and Depth
| 7 |
iclr
| 1 | 0 |
2023-06-18 09:44:15.458000
|
https://github.com/Arjung27/DeepThinking
| 8 |
The uncanny similarity of recurrence and depth
|
https://scholar.google.com/scholar?cluster=15030809144030367999&hl=en&as_sdt=0,5
| 3 | 2,022 |
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
| 118 |
iclr
| 68 | 11 |
2023-06-18 09:44:15.662000
|
https://github.com/facebookresearch/drqv2
| 269 |
Mastering visual continuous control: Improved data-augmented reinforcement learning
|
https://scholar.google.com/scholar?cluster=6421326850849903033&hl=en&as_sdt=0,5
| 9 | 2,022 |
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting
| 33 |
iclr
| 35 | 5 |
2023-06-18 09:44:15.865000
|
https://github.com/salesforce/CoST
| 164 |
CoST: Contrastive learning of disentangled seasonal-trend representations for time series forecasting
|
https://scholar.google.com/scholar?cluster=10071706504793887642&hl=en&as_sdt=0,11
| 6 | 2,022 |
The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs
| 5 |
iclr
| 0 | 0 |
2023-06-18 09:44:16.068000
|
https://github.com/muellerjohannes/geometry-pomdps-iclr-2022
| 0 |
The geometry of memoryless stochastic policy optimization in infinite-horizon POMDPs
|
https://scholar.google.com/scholar?cluster=2565752681531472620&hl=en&as_sdt=0,5
| 1 | 2,022 |
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks
| 52 |
iclr
| 4 | 6 |
2023-06-18 09:44:16.272000
|
https://github.com/dydjw9/efficient_sam
| 43 |
Efficient sharpness-aware minimization for improved training of neural networks
|
https://scholar.google.com/scholar?cluster=15803669707023220896&hl=en&as_sdt=0,34
| 1 | 2,022 |
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
| 2 |
iclr
| 0 | 0 |
2023-06-18 09:44:16.476000
|
https://github.com/jianda-chen/ambs
| 5 |
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
|
https://scholar.google.com/scholar?cluster=3185167988129804114&hl=en&as_sdt=0,5
| 1 | 2,022 |
Effective Model Sparsification by Scheduled Grow-and-Prune Methods
| 13 |
iclr
| 2 | 1 |
2023-06-18 09:44:16.679000
|
https://github.com/boone891214/gap
| 8 |
Effective model sparsification by scheduled grow-and-prune methods
|
https://scholar.google.com/scholar?cluster=14488112763252453275&hl=en&as_sdt=0,46
| 2 | 2,022 |
Efficient Active Search for Combinatorial Optimization Problems
| 28 |
iclr
| 6 | 0 |
2023-06-18 09:44:16.882000
|
https://github.com/ahottung/EAS
| 30 |
Efficient active search for combinatorial optimization problems
|
https://scholar.google.com/scholar?cluster=13404693543769371304&hl=en&as_sdt=0,47
| 2 | 2,022 |
Training Structured Neural Networks Through Manifold Identification and Variance Reduction
| 2 |
iclr
| 0 | 0 |
2023-06-18 09:44:17.085000
|
https://github.com/zihsyuan1214/rmda
| 0 |
Training Structured Neural Networks Through Manifold Identification and Variance Reduction
|
https://scholar.google.com/scholar?cluster=3809096100711986966&hl=en&as_sdt=0,5
| 2 | 2,022 |
The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization
| 18 |
iclr
| 4 | 0 |
2023-06-18 09:44:17.288000
|
https://github.com/robertcsordas/ndr
| 24 |
The neural data router: Adaptive control flow in transformers improves systematic generalization
|
https://scholar.google.com/scholar?cluster=10423367816942956879&hl=en&as_sdt=0,37
| 1 | 2,022 |
Distributionally Robust Models with Parametric Likelihood Ratios
| 9 |
iclr
| 7 | 0 |
2023-06-18 09:44:17.492000
|
https://github.com/pmichel31415/P-DRO
| 18 |
Distributionally robust models with parametric likelihood ratios
|
https://scholar.google.com/scholar?cluster=2606416541563470801&hl=en&as_sdt=0,10
| 2 | 2,022 |
Understanding approximate and unrolled dictionary learning for pattern recovery
| 6 |
iclr
| 0 | 0 |
2023-06-18 09:44:17.696000
|
https://github.com/bmalezieux/unrolled_dl
| 1 |
Understanding approximate and unrolled dictionary learning for pattern recovery
|
https://scholar.google.com/scholar?cluster=14808406536807467476&hl=en&as_sdt=0,10
| 3 | 2,022 |
Constraining Linear-chain CRFs to Regular Languages
| 3 |
iclr
| 0 | 2 |
2023-06-18 09:44:17.899000
|
https://github.com/person594/regccrf-experiments
| 4 |
Constraining linear-chain crfs to regular languages
|
https://scholar.google.com/scholar?cluster=14062623964127434433&hl=en&as_sdt=0,5
| 3 | 2,022 |
Noisy Feature Mixup
| 15 |
iclr
| 1 | 0 |
2023-06-18 09:44:18.103000
|
https://github.com/erichson/noisy_mixup
| 17 |
Noisy feature mixup
|
https://scholar.google.com/scholar?cluster=6823398693894797523&hl=en&as_sdt=0,24
| 5 | 2,022 |
Subspace Regularizers for Few-Shot Class Incremental Learning
| 17 |
iclr
| 1 | 1 |
2023-06-18 09:44:18.306000
|
https://github.com/feyzaakyurek/subspace-reg
| 20 |
Subspace regularizers for few-shot class incremental learning
|
https://scholar.google.com/scholar?cluster=740038996677769193&hl=en&as_sdt=0,22
| 4 | 2,022 |
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