1.2k citations · 8.7k across the 141 of their papers we have counts for
21 papers · 1 filter
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung +2
Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel techn…
Progressive Neural Architecture Search
Chenxi Liu, Barret Zoph, Maxim Neumann +7
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learni…
Graph Distillation for Action Detection with Privileged Modalities
Zelun Luo, Jun-Ting Hsieh, Lu Jiang +2
We propose a technique that tackles action detection in multimodal videos under a realistic and challenging condition in which only limited training data and partially observed mod…
Label Efficient Learning of Transferable Representations across Domains and Tasks
Zelun Luo, Yuliang Zou, Judy Hoffman +1
We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner. Our approach battles domain shift with a domain adv…
Thoracic Disease Identification and Localization with Limited Supervision
Zhe Li, Chong Wang, Mei Han +4
Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly accurate predi…
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
Danfei Xu, Suraj Nair, Yuke Zhu +4
In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program in…