1.2k citations · 9.1k across the 205 of their papers we have counts for
9 papers · 1 filter
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
Ziniu Hu, Weiqing Liu, Jiang Bian +2
Stock trend prediction plays a critical role in seeking maximized profit from stock investment. However, precise trend prediction is very difficult since the highly volatile and no…
Convergence Analysis of Distributed Stochastic Gradient Descent with Shuffling
Qi Meng, Wei Chen, Yue Wang +2
When using stochastic gradient descent to solve large-scale machine learning problems, a common practice of data processing is to shuffle the training data, partition the data acro…
Slim-DP: A Light Communication Data Parallelism for DNN
Shizhao Sun, Wei Chen, Jiang Bian +2
Data parallelism has emerged as a necessary technique to accelerate the training of deep neural networks (DNN). In a typical data parallelism approach, the local workers push the l…
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers
Quanming Yao, James T. Kwok, Taifeng Wang +1
Low-rank modeling has many important applications in computer vision and machine learning. While the matrix rank is often approximated by the convex nuclear norm, the use of noncon…
Dual Supervised Learning
Yingce Xia, Tao Qin, Wei Chen +3
Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image cl…
Word-Entity Duet Representations for Document Ranking
Chenyan Xiong, Jamie Callan, Tie-Yan Liu
This paper presents a word-entity duet framework for utilizing knowledge bases in ad-hoc retrieval. In this work, the query and documents are modeled by word-based representations…
Reinforcement Learning for Learning Rate Control
Chang Xu, Tao Qin, Gang Wang +1
Stochastic gradient descent (SGD), which updates the model parameters by adding a local gradient times a learning rate at each step, is widely used in model training of machine lea…
Adversarial Neural Machine Translation
Lijun Wu, Yingce Xia, Li Zhao +4
In this paper, we study a new learning paradigm for Neural Machine Translation (NMT). Instead of maximizing the likelihood of the human translation as in previous works, we minimiz…
Learning What Data to Learn
Yang Fan, Fei Tian, Tao Qin +2
Machine learning is essentially the sciences of playing with data. An adaptive data selection strategy, enabling to dynamically choose different data at various training stages, ca…