Timezone: »
Meta-learning improves generalization of machine learning models when faced with previously unseen tasks by leveraging experiences from different, yet related prior tasks. To allow for better generalization, we propose a novel task representation called model-aware task embedding (MATE) that incorporates not only the data distributions of different tasks, but also the complexity of the tasks through the models used. The task complexity is taken into account by a novel variant of kernel mean embedding, combined with an instance-adaptive attention mechanism inspired by an SVM-based feature selection algorithm. Together with conditioning layers in deep neural networks, MATE can be easily incorporated into existing meta learners as a plug-and-play module. While MATE is widely applicable to general tasks where the concept of task/environment is involved, we demonstrate its effectiveness in few-shot learning by improving a state-of-the-art model consistently on two benchmarks. Source codes for this paper are available at https://github.com/VITA-Group/MATE.
Author Information
Xiaohan Chen (University of Texas at Austin)
Zhangyang Wang (University of Texas at Austin)
Siyu Tang (ETH Zurich)
Krikamol Muandet (Max Planck Institute for Intelligent Systems)
More from the Same Authors
-
2021 Workshop: Machine Learning Meets Econometrics (MLECON) »
David Bruns-Smith · Arthur Gretton · Limor Gultchin · Niki Kilbertus · Krikamol Muandet · Evan Munro · Angela Zhou -
2021 Poster: Sparse Training via Boosting Pruning Plasticity with Neuroregeneration »
Shiwei Liu · Tianlong Chen · Xiaohan Chen · Zahra Atashgahi · Lu Yin · Huanyu Kou · Li Shen · Mykola Pechenizkiy · Zhangyang Wang · Decebal Constantin Mocanu -
2021 Poster: Hyperparameter Tuning is All You Need for LISTA »
Xiaohan Chen · Jialin Liu · Zhangyang Wang · Wotao Yin -
2021 Poster: The Elastic Lottery Ticket Hypothesis »
Xiaohan Chen · Yu Cheng · Shuohang Wang · Zhe Gan · Jingjing Liu · Zhangyang Wang -
2021 Poster: Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? »
Xiaolong Ma · Geng Yuan · Xuan Shen · Tianlong Chen · Xuxi Chen · Xiaohan Chen · Ning Liu · Minghai Qin · Sijia Liu · Zhangyang Wang · Yanzhi Wang -
2021 Poster: MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images »
Shaofei Wang · Marko Mihajlovic · Qianli Ma · Andreas Geiger · Siyu Tang -
2020 Workshop: Second Workshop on AI for Humanitarian Assistance and Disaster Response »
Ritwik Gupta · Robin Murphy · Eric Heim · Zhangyang Wang · Bryce Goodman · Nirav Patel · Piotr Bilinski · Edoardo Nemni -
2020 Poster: Graph Contrastive Learning with Augmentations »
Yuning You · Tianlong Chen · Yongduo Sui · Ting Chen · Zhangyang Wang · Yang Shen -
2020 Poster: Dual Instrumental Variable Regression »
Krikamol Muandet · Arash Mehrjou · Si Kai Lee · Anant Raj -
2020 Poster: Learning Kernel Tests Without Data Splitting »
Jonas Kübler · Wittawat Jitkrittum · Bernhard Schölkopf · Krikamol Muandet -
2020 Poster: A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings »
Junhyung Park · Krikamol Muandet -
2020 Poster: Robust Pre-Training by Adversarial Contrastive Learning »
Ziyu Jiang · Tianlong Chen · Ting Chen · Zhangyang Wang -
2020 Poster: Training Stronger Baselines for Learning to Optimize »
Tianlong Chen · Weiyi Zhang · Zhou Jingyang · Shiyu Chang · Sijia Liu · Lisa Amini · Zhangyang Wang -
2020 Spotlight: Training Stronger Baselines for Learning to Optimize »
Tianlong Chen · Weiyi Zhang · Zhou Jingyang · Shiyu Chang · Sijia Liu · Lisa Amini · Zhangyang Wang -
2020 Poster: Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free »
Haotao Wang · Tianlong Chen · Shupeng Gui · TingKuei Hu · Ji Liu · Zhangyang Wang -
2020 Poster: FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training »
Yonggan Fu · Haoran You · Yang Zhao · Yue Wang · Chaojian Li · Kailash Gopalakrishnan · Zhangyang Wang · Yingyan Lin -
2020 Poster: The Lottery Ticket Hypothesis for Pre-trained BERT Networks »
Tianlong Chen · Jonathan Frankle · Shiyu Chang · Sijia Liu · Yang Zhang · Zhangyang Wang · Michael Carbin -
2020 Poster: ShiftAddNet: A Hardware-Inspired Deep Network »
Haoran You · Xiaohan Chen · Yongan Zhang · Chaojian Li · Sicheng Li · Zihao Liu · Zhangyang Wang · Yingyan Lin -
2019 Poster: E2-Train: Training State-of-the-art CNNs with Over 80% Less Energy »
Ziyu Jiang · Yue Wang · Xiaohan Chen · Pengfei Xu · Yang Zhao · Yingyan Lin · Zhangyang Wang -
2018 Poster: Can We Gain More from Orthogonality Regularizations in Training Deep Networks? »
Nitin Bansal · Xiaohan Chen · Zhangyang Wang -
2018 Poster: Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds »
Xiaohan Chen · Jialin Liu · Zhangyang Wang · Wotao Yin -
2018 Spotlight: Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds »
Xiaohan Chen · Jialin Liu · Zhangyang Wang · Wotao Yin -
2014 Poster: Kernel Mean Estimation via Spectral Filtering »
Krikamol Muandet · Bharath Sriperumbudur · Bernhard Schölkopf -
2012 Poster: Learning from Distributions via Support Measure Machines »
Krikamol Muandet · Kenji Fukumizu · Francesco Dinuzzo · Bernhard Schölkopf -
2012 Spotlight: Learning from Distributions via Support Measure Machines »
Krikamol Muandet · Kenji Fukumizu · Francesco Dinuzzo · Bernhard Schölkopf