Jianmin Wang

papers

6,420

total citations

papers (28)

Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

NEURIPS 2021arXiv

3,906

citations

Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting

NEURIPS 2022arXiv

699

citations

Jianmin Wang

papers (28)

Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting

Cycle Self-Training for Domain Adaptation

Open Domain Generalization with Domain-Augmented Meta-Learning

Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors

Transolver: A Fast Transformer Solver for PDEs on General Geometries

MotionRNN: A Flexible Model for Video Prediction With Spacetime-Varying Motions

Timer: Generative Pre-trained Transformers Are Large Time Series Models

SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

Debiased Self-Training for Semi-Supervised Learning

Supported Policy Optimization for Offline Reinforcement Learning

Sundial: A Family of Highly Capable Time Series Foundation Models

Regressive Domain Adaptation for Unsupervised Keypoint Detection

Transferable Calibration with Lower Bias and Variance in Domain Adaptation

MetaSets: Meta-Learning on Point Sets for Generalizable Representations

Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries

TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling

HarmonyDream: Task Harmonization Inside World Models

HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction

Minimum Class Confusion for Versatile Domain Adaptation

Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models

CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding

Learning to Adapt to Evolving Domains

Learning to Detect Open Classes for Universal Domain Adaptation

Progressive Adversarial Networks for Fine-Grained Domain Adaptation

Transferable Query Selection for Active Domain Adaptation

Co-Tuning for Transfer Learning

Stochastic Normalization

papers (28)

Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting

Cycle Self-Training for Domain Adaptation

Open Domain Generalization with Domain-Augmented Meta-Learning

Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors

Transolver: A Fast Transformer Solver for PDEs on General Geometries

MotionRNN: A Flexible Model for Video Prediction With Spacetime-Varying Motions

Timer: Generative Pre-trained Transformers Are Large Time Series Models

SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

Debiased Self-Training for Semi-Supervised Learning

Supported Policy Optimization for Offline Reinforcement Learning

Sundial: A Family of Highly Capable Time Series Foundation Models

Regressive Domain Adaptation for Unsupervised Keypoint Detection

Transferable Calibration with Lower Bias and Variance in Domain Adaptation

MetaSets: Meta-Learning on Point Sets for Generalizable Representations

Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries

TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling

HarmonyDream: Task Harmonization Inside World Models

HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction

Minimum Class Confusion for Versatile Domain Adaptation

Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models

CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding

Learning to Adapt to Evolving Domains

Learning to Detect Open Classes for Universal Domain Adaptation

Progressive Adversarial Networks for Fine-Grained Domain Adaptation

Transferable Query Selection for Active Domain Adaptation

Co-Tuning for Transfer Learning

Stochastic Normalization