265 citations · 1.9k across the 79 of their papers we have counts for
11 papers · 1 filter
A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization
Bowen Cheng, Yunchao Wei, Jiahui Yu +5
While training on samples drawn from independent and identical distribution has been a de facto paradigm for optimizing image classification networks, humans learn new concepts in…
Improving Reinforcement Learning Based Image Captioning with Natural Language Prior
Tszhang Guo, Shiyu Chang, Mo Yu +1
Recently, Reinforcement Learning (RL) approaches have demonstrated advanced performance in image captioning by directly optimizing the metric used for testing. However, this shaped…
Deriving Machine Attention from Human Rationales
Yujia Bao, Shiyu Chang, Mo Yu +1
Attention-based models are successful when trained on large amounts of data. In this paper, we demonstrate that even in the low-resource scenario, attention can be learned effectiv…
One-Shot Relational Learning for Knowledge Graphs
Wenhan Xiong, Mo Yu, Shiyu Chang +2
Knowledge graphs (KGs) are the key components of various natural language processing applications. To further expand KGs' coverage, previous studies on knowledge graph completion u…
Matrix Factorization on GPUs with Memory Optimization and Approximate Computing
Wei Tan, Shiyu Chang, Liana Fong +3
Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative filtering, data compression, feature extraction…
Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents
Wenhan Xiong, Xiaoxiao Guo, Mo Yu +3
We investigate the task of learning to follow natural language instructions by jointly reasoning with visual observations and language inputs. In contrast to existing methods which…
A Co-Matching Model for Multi-choice Reading Comprehension
Shuohang Wang, Mo Yu, Shiyu Chang +1
Multi-choice reading comprehension is a challenging task, which involves the matching between a passage and a question-answer pair. This paper proposes a new co-matching approach t…
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
Sijia Liu, Bhavya Kailkhura, Pin-Yu Chen +3
As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying. This paper add…
Diverse Few-Shot Text Classification with Multiple Metrics
Mo Yu, Xiaoxiao Guo, Jinfeng Yi +6
We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low…
Image Super-Resolution via Dual-State Recurrent Networks
Wei Han, Shiyu Chang, Ding Liu +3
Advances in image super-resolution (SR) have recently benefited significantly from rapid developments in deep neural networks. Inspired by these recent discoveries, we note that ma…
Deep Learning Based Speech Beamforming
Kaizhi Qian, Yang Zhang, Shiyu Chang +3
Multi-channel speech enhancement with ad-hoc sensors has been a challenging task. Speech model guided beamforming algorithms are able to recover natural sounding speech, but the sp…