1 code implementation • 26 May 2021 • Chun-Ta Lu, Yun Zeng, Da-Cheng Juan, Yicheng Fan, Zhe Li, Jan Dlabal, Yi-Ting Chen, Arjun Gopalan, Allan Heydon, Chun-Sung Ferng, Reah Miyara, Ariel Fuxman, Futang Peng, Zhen Li, Tom Duerig, Andrew Tomkins
In this work, we propose CARLS, a novel framework for augmenting the capacity of existing deep learning frameworks by enabling multiple components -- model trainers, knowledge makers and knowledge banks -- to concertedly work together in an asynchronous fashion across hardware platforms.
no code implementations • 17 Apr 2020 • Yun Zeng, Siqi Zuo, Dongcai Shen
One of the limitations of deep learning models with sparse features today stems from the predefined nature of their input, which requires a dictionary be defined prior to the training.
no code implementations • 10 Jan 2019 • Yun Zeng
We propose a principle for exploring context in machine learning models.
no code implementations • CVPR 2013 • Yun Zeng, Chaohui Wang, Stefano Soatto, Shing-Tung Yau
This paper introduces an efficient approach to integrating non-local statistics into the higher-order Markov Random Fields (MRFs) framework.