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05/30/2023
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Real-world time series is characterized by intrinsic non-stationarity th...
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05/19/2023
Tune-Mode ConvBN Blocks For Efficient Transfer Learning
Convolution-BatchNorm (ConvBN) blocks are integral components in various...
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02/02/2023
CLIPood: Generalizing CLIP to Out-of-Distributions
Out-of-distribution (OOD) generalization, where the model needs to handl...
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02/02/2023
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
Time series analysis is widely used in extensive areas. Recently, to red...
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01/30/2023
Solving High-Dimensional PDEs with Latent Spectral Models
Deep models have achieved impressive progress in solving partial differe...
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01/30/2023
ForkMerge: Overcoming Negative Transfer in Multi-Task Learning
The goal of multi-task learning is to utilize useful knowledge from mult...
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11/13/2022
Out-of-Dynamics Imitation Learning from Multimodal Demonstrations
Existing imitation learning works mainly assume that the demonstrator wh...
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10/05/2022
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
Time series analysis is of immense importance in extensive applications,...
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06/13/2022
Recommender Transformers with Behavior Pathways
Sequential recommendation requires the recommender to capture the evolvi...
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06/08/2022
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models
Transfer learning aims to leverage knowledge from pre-trained models to ...
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05/28/2022
Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting
Transformers have shown great power in time series forecasting due to th...
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04/15/2022
MetaSets: Meta-Learning on Point Sets for Generalizable Representations
Deep learning techniques for point clouds have achieved strong performan...
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04/12/2022
Continual Predictive Learning from Videos
Predictive learning ideally builds the world model of physical processes...
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03/14/2022
From Big to Small: Adaptive Learning to Partial-Set Domains
Domain adaptation targets at knowledge acquisition and dissemination fro...
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02/15/2022
Debiased Pseudo Labeling in Self-Training
Deep neural networks achieve remarkable performances on a wide range of ...
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02/13/2022
Flowformer: Linearizing Transformers with Conservation Flows
Transformers based on the attention mechanism have achieved impressive s...
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02/13/2022
Supported Policy Optimization for Offline Reinforcement Learning
Policy constraint methods to offline reinforcement learning (RL) typical...
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01/15/2022
Transferability in Deep Learning: A Survey
The success of deep learning algorithms generally depends on large-scale...
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10/20/2021
Ranking and Tuning Pre-trained Models: A New Paradigm of Exploiting Model Hubs
Pre-trained model hubs with many pre-trained models (PTMs) have been a c...
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10/14/2021
Omni-Training for Data-Efficient Deep Learning
Learning a generalizable deep model from a few examples in a short time ...
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10/09/2021
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
To mitigate the burden of data labeling, we aim at improving data effici...
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10/08/2021
ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning
Learning predictive models for unlabeled spatiotemporal data is challeng...
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10/06/2021
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Unsupervisedly detecting anomaly points in time series is challenging, w...
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10/06/2021
Decoupled Adaptation for Cross-Domain Object Detection
Cross-domain object detection is more challenging than object classifica...
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06/29/2021
Zoo-Tuning: Adaptive Transfer from a Zoo of Models
With the development of deep networks on various large-scale datasets, a...
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06/24/2021
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Extending the forecasting time is a critical demand for real application...
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04/08/2021
Open Domain Generalization with Domain-Augmented Meta-Learning
Leveraging datasets available to learn a model with high generalization ...
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03/17/2021
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
The predictive learning of spatiotemporal sequences aims to generate fut...
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03/10/2021
Regressive Domain Adaptation for Unsupervised Keypoint Detection
Domain adaptation (DA) aims at transferring knowledge from a labeled sou...
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03/05/2021
Cycle Self-Training for Domain Adaptation
Mainstream approaches for unsupervised domain adaptation (UDA) learn dom...
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03/03/2021
MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions
This paper tackles video prediction from a new dimension of predicting s...
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02/25/2021
Self-Tuning for Data-Efficient Deep Learning
Deep learning has made revolutionary advances to diverse applications in...
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02/22/2021
LogME: Practical Assessment of Pre-trained Models for Transfer Learning
This paper studies task adaptive pre-trained model selection, an underex...
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11/12/2020
Bi-tuning of Pre-trained Representations
It is common within the deep learning community to first pre-train a dee...
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09/24/2020
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks
This paper explores a new research problem of unsupervised transfer lear...
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08/14/2020
On Localized Discrepancy for Domain Adaptation
We propose the discrepancy-based generalization theories for unsupervise...
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07/16/2020
Transferable Calibration with Lower Bias and Variance in Domain Adaptation
Domain Adaptation (DA) enables transferring a learning machine from a la...
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03/26/2020
Negative Margin Matters: Understanding Margin in Few-shot Classification
This paper introduces a negative margin loss to metric learning based fe...
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12/08/2019
Adversarial Pyramid Network for Video Domain Generalization
This paper introduces a new research problem of video domain generalizat...
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12/08/2019
Less Confusion More Transferable: Minimum Class Confusion for Versatile Domain Adaptation
Domain Adaptation (DA) transfers a learning model from a labeled source ...
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09/26/2019
Towards Understanding the Transferability of Deep Representations
Deep neural networks trained on a wide range of datasets demonstrate imp...
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08/05/2019
Learning Stages: Phenomenon, Root Cause, Mechanism Hypothesis, and Implications
Under StepDecay learning rate strategy (decaying the learning rate after...
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04/11/2019
Bridging Theory and Algorithm for Domain Adaptation
This paper addresses the problem of unsupervised domain adaption from th...
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03/28/2019
Learning to Transfer Examples for Partial Domain Adaptation
Domain adaptation is critical for learning in new and unseen environment...
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03/04/2019
Spatiotemporal Pyramid Network for Video Action Recognition
Two-stream convolutional networks have shown strong performance in video...
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02/14/2019
WaveletFCNN: A Deep Time Series Classification Model for Wind Turbine Blade Icing Detection
Wind power, as an alternative to burning fossil fuels, is plentiful and ...
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02/01/2019
Deep Triplet Quantization
Deep hashing establishes efficient and effective image retrieval by end-...
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11/20/2018
Reversing Two-Stream Networks with Decoding Discrepancy Penalty for Robust Action Recognition
We discuss the robustness and generalization ability in the realm of act...
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11/19/2018
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
Natural spatiotemporal processes can be highly non-stationary in many wa...
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09/04/2018