Mingsheng Long

Orcid: 0000-0001-9421-463X

According to our database1, Mingsheng Long authored at least 151 papers between 2010 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Editorial: Learning With Fewer Labels in Computer Vision.
IEEE Trans. Pattern Anal. Mach. Intell., March, 2024

depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers.
CoRR, 2024

TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables.
CoRR, 2024

TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling.
CoRR, 2024

EuLagNet: Eulerian Fluid Prediction with Lagrangian Dynamics.
CoRR, 2024

AutoTimes: Autoregressive Time Series Forecasters via Large Language Models.
CoRR, 2024

Timer: Transformers for Time Series Analysis at Scale.
CoRR, 2024

Transolver: A Fast Transformer Solver for PDEs on General Geometries.
CoRR, 2024

2023
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning.
IEEE Trans. Pattern Anal. Mach. Intell., December, 2023

ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning.
IEEE Trans. Pattern Anal. Mach. Intell., November, 2023

Interpretable weather forecasting for worldwide stations with a unified deep model.
Nat. Mac. Intell., June, 2023

PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning.
IEEE Trans. Pattern Anal. Mach. Intell., 2023

From Big to Small: Adaptive Learning to Partial-Set Domains.
IEEE Trans. Pattern Anal. Mach. Intell., 2023

Skilful nowcasting of extreme precipitation with NowcastNet.
Nat., 2023

HelmSim: Learning Helmholtz Dynamics for Interpretable Fluid Simulation.
CoRR, 2023

iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.
CoRR, 2023

On the Embedding Collapse when Scaling up Recommendation Models.
CoRR, 2023

Harmony World Models: Boosting Sample Efficiency for Model-based Reinforcement Learning.
CoRR, 2023

Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors.
CoRR, 2023

Tune-Mode ConvBN Blocks For Efficient Transfer Learning.
CoRR, 2023

SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling.
CoRR, 2023

ForkMerge: Overcoming Negative Transfer in Multi-Task Learning.
CoRR, 2023

Bi-tuning: Efficient Transfer from Pre-trained Models.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Solving High-Dimensional PDEs with Latent Spectral Models.
Proceedings of the International Conference on Machine Learning, 2023

CLIPood: Generalizing CLIP to Out-of-Distributions.
Proceedings of the International Conference on Machine Learning, 2023

Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms.
Proceedings of the International Conference on Machine Learning, 2023

TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
VideoDG: Generalizing Temporal Relations in Videos to Novel Domains.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs.
J. Mach. Learn. Res., 2022

Recommender Transformers with Behavior Pathways.
CoRR, 2022

Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting.
CoRR, 2022

Debiased Pseudo Labeling in Self-Training.
CoRR, 2022

Transferability in Deep Learning: A Survey.
CoRR, 2022

Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Debiased Self-Training for Semi-Supervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Supported Policy Optimization for Offline Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Flowformer: Linearizing Transformers with Conservation Flows.
Proceedings of the International Conference on Machine Learning, 2022

Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy.
Proceedings of the Tenth International Conference on Learning Representations, 2022

X-model: Improving Data Efficiency in Deep Learning with A Minimax Model.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Decoupled Adaptation for Cross-Domain Object Detection.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Continual Predictive Learning from Videos.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Out-of-Dynamics Imitation Learning from Multimodal Demonstrations.
Proceedings of the Conference on Robot Learning, 2022

2021
Preface.
J. Comput. Sci. Technol., 2021

Ranking and Tuning Pre-trained Models: A New Paradigm of Exploiting Model Hubs.
CoRR, 2021

Omni-Training for Data-Efficient Deep Learning.
CoRR, 2021

Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Cycle Self-Training for Domain Adaptation.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

LogME: Practical Assessment of Pre-trained Models for Transfer Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

Self-Tuning for Data-Efficient Deep Learning.
Proceedings of the 38th International Conference on Machine Learning, 2021

Zoo-Tuning: Adaptive Transfer from A Zoo of Models.
Proceedings of the 38th International Conference on Machine Learning, 2021

Representation Subspace Distance for Domain Adaptation Regression.
Proceedings of the 38th International Conference on Machine Learning, 2021

MotionRNN: A Flexible Model for Video Prediction With Spacetime-Varying Motions.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Open Domain Generalization with Domain-Augmented Meta-Learning.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Regressive Domain Adaptation for Unsupervised Keypoint Detection.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

MetaSets: Meta-Learning on Point Sets for Generalizable Representations.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Transferable Query Selection for Active Domain Adaptation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Bi-tuning of Pre-trained Representations.
CoRR, 2020

On Localized Discrepancy for Domain Adaptation.
CoRR, 2020

Co-Tuning for Transfer Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Transferable Calibration with Lower Bias and Variance in Domain Adaptation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning to Adapt to Evolving Domains.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stochastic Normalization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Resource Efficient Domain Adaptation.
Proceedings of the MM '20: The 28th ACM International Conference on Multimedia, 2020

Unsupervised Transfer Learning for Spatiotemporal Predictive Networks.
Proceedings of the 37th International Conference on Machine Learning, 2020

Multi-Task Learning of Generalizable Representations for Video Action Recognition.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2020

A Multi-Player Minimax Game for Generative Adversarial Networks.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2020

Negative Margin Matters: Understanding Margin in Few-Shot Classification.
Proceedings of the Computer Vision - ECCV 2020, 2020

Minimum Class Confusion for Versatile Domain Adaptation.
Proceedings of the Computer Vision - ECCV 2020, 2020

Learning to Detect Open Classes for Universal Domain Adaptation.
Proceedings of the Computer Vision - ECCV 2020, 2020

Progressive Adversarial Networks for Fine-Grained Domain Adaptation.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Probabilistic Video Prediction From Noisy Data With a Posterior Confidence.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Transferring Pretrained Networks to Small Data via Category Decorrelation.
Proceedings of the 31st British Machine Vision Conference 2020, 2020

Simultaneous Learning of Pivots and Representations for Cross-Domain Sentiment Classification.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Transferable Representation Learning with Deep Adaptation Networks.
IEEE Trans. Pattern Anal. Mach. Intell., 2019

Adversarial Pyramid Network for Video Domain Generalization.
CoRR, 2019

Less Confusion More Transferable: Minimum Class Confusion for Versatile Domain Adaptation.
CoRR, 2019

Towards Understanding the Transferability of Deep Representations.
CoRR, 2019

Learning Stages: Phenomenon, Root Cause, Mechanism Hypothesis, and Implications.
CoRR, 2019

Process Extraction from Texts via Multi-Task Architecture.
CoRR, 2019

WaveletFCNN: A Deep Time Series Classification Model for Wind Turbine Blade Icing Detection.
CoRR, 2019

Transferable Normalization: Towards Improving Transferability of Deep Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation.
Proceedings of the 36th International Conference on Machine Learning, 2019

Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers.
Proceedings of the 36th International Conference on Machine Learning, 2019

Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation.
Proceedings of the 36th International Conference on Machine Learning, 2019

Bridging Theory and Algorithm for Domain Adaptation.
Proceedings of the 36th International Conference on Machine Learning, 2019

Z-Order Recurrent Neural Networks for Video Prediction.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2019

Eidetic 3D LSTM: A Model for Video Prediction and Beyond.
Proceedings of the 7th International Conference on Learning Representations, 2019

Maximum-Margin Hamming Hashing.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Universal Domain Adaptation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Memory in Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity From Spatiotemporal Dynamics.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Separate to Adapt: Open Set Domain Adaptation via Progressive Separation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Learning to Transfer Examples for Partial Domain Adaptation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Transferable Attention for Domain Adaptation.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Transferable Curriculum for Weakly-Supervised Domain Adaptation.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Reversing Two-Stream Networks with Decoding Discrepancy Penalty for Robust Action Recognition.
CoRR, 2018

Conditional Adversarial Domain Adaptation.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Generalized Zero-Shot Learning with Deep Calibration Network.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Deep Triplet Quantization.
Proceedings of the 2018 ACM Multimedia Conference on Multimedia Conference, 2018

Deep Priority Hashing.
Proceedings of the 2018 ACM Multimedia Conference on Multimedia Conference, 2018

PredCNN: Predictive Learning with Cascade Convolutions.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

Partial Adversarial Domain Adaptation.
Proceedings of the Computer Vision - ECCV 2018, 2018

Cross-Modal Hamming Hashing.
Proceedings of the Computer Vision - ECCV 2018, 2018

HashGAN: Deep Learning to Hash With Pair Conditional Wasserstein GAN.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Deep Cauchy Hashing for Hamming Space Retrieval.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Partial Transfer Learning With Selective Adversarial Networks.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Multi-Adversarial Domain Adaptation.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Unsupervised Domain Adaptation With Distribution Matching Machines.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Transfer Adversarial Hashing for Hamming Space Retrieval.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Domain Adaptation with Randomized Multilinear Adversarial Networks.
CoRR, 2017

PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learning Multiple Tasks with Multilinear Relationship Networks.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Deep Transfer Learning with Joint Adaptation Networks.
Proceedings of the 34th International Conference on Machine Learning, 2017

HashNet: Deep Learning to Hash by Continuation.
Proceedings of the IEEE International Conference on Computer Vision, 2017

Spatiotemporal Pyramid Network for Video Action Recognition.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Deep Visual-Semantic Quantization for Efficient Image Retrieval.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Correlation Hashing Network for Efficient Cross-Modal Retrieval.
Proceedings of the British Machine Vision Conference 2017, 2017

Collective Deep Quantization for Efficient Cross-Modal Retrieval.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Transitive Hashing Network for Heterogeneous Multimedia Retrieval.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Deep Learning of Transferable Representation for Scalable Domain Adaptation.
IEEE Trans. Knowl. Data Eng., 2016

Deep Transfer Learning with Joint Adaptation Networks.
CoRR, 2016

Unsupervised Domain Adaptation with Residual Transfer Networks.
CoRR, 2016

Transitive Hashing Network for Heterogeneous Multimedia Retrieval.
CoRR, 2016

Composite Correlation Quantization for Efficient Multimodal Retrieval.
Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016

Unsupervised Domain Adaptation with Residual Transfer Networks.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Correlation Autoencoder Hashing for Supervised Cross-Modal Search.
Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, 2016

Deep Visual-Semantic Hashing for Cross-Modal Retrieval.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Deep Hashing Network for Efficient Similarity Retrieval.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

Deep Quantization Network for Efficient Image Retrieval.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Domain Invariant Transfer Kernel Learning.
IEEE Trans. Knowl. Data Eng., 2015

Compositional Correlation Quantization for Large-Scale Multimodal Search.
CoRR, 2015

Learning Multiple Tasks with Deep Relationship Networks.
CoRR, 2015

Learning Transferable Features with Deep Adaptation Networks.
CoRR, 2015

Learning Transferable Features with Deep Adaptation Networks.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Adaptation Regularization: A General Framework for Transfer Learning.
IEEE Trans. Knowl. Data Eng., 2014

Transfer Learning with Graph Co-Regularization.
IEEE Trans. Knowl. Data Eng., 2014

Local Hybrid Coding for Image Classification.
Proceedings of the 22nd International Conference on Pattern Recognition, 2014

Transfer Joint Matching for Unsupervised Domain Adaptation.
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

Inherent Replica Inconsistency in Cassandra.
Proceedings of the 2014 IEEE International Congress on Big Data, Anchorage, AK, USA, June 27, 2014

2013
Twin Bridge Transfer Learning for Sparse Collaborative Filtering.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2013

Transfer Feature Learning with Joint Distribution Adaptation.
Proceedings of the IEEE International Conference on Computer Vision, 2013

Transfer Sparse Coding for Robust Image Representation.
Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012
Dual Transfer Learning.
Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

Topic Correlation Analysis for Cross-Domain Text Classification.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2010
Transfer Learning via Cluster Correspondence Inference.
Proceedings of the ICDM 2010, 2010


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