7.1k citations · 7.1k across the 11 of their papers we have counts for
11 papers
Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis
Eldon Schoop, Xin Zhou, Gang Li +3
We use a deep learning based approach to predict whether a selected element in a mobile UI screenshot will be perceived by users as tappable, based on pixels only instead of view h…
Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning
Bryan Wang, Gang Li, Xin Zhou +3
Mobile User Interface Summarization generates succinct language descriptions of mobile screens for conveying important contents and functionalities of the screen, which can be usef…
You Look Twice: GaterNet for Dynamic Filter Selection in CNNs
Zhourong Chen, Yang Li, Samy Bengio +1
The concept of conditional computation for deep nets has been proposed previously to improve model performance by selectively using only parts of the model conditioned on the sampl…
Learning Sparse Deep Feedforward Networks via Tree Skeleton Expansion
Zhourong Chen, Xiaopeng Li, Nevin L. Zhang
Despite the popularity of deep learning, structure learning for deep models remains a relatively under-explored area. In contrast, structure learning has been studied extensively f…
Building Sparse Deep Feedforward Networks using Tree Receptive Fields
Xiaopeng Li, Zhourong Chen, Nevin L. Zhang
Sparse connectivity is an important factor behind the success of convolutional neural networks and recurrent neural networks. In this paper, we consider the problem of learning spa…
Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
Xiaopeng Li, Zhourong Chen, Leonard K. M. Poon +1
We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features. In general, our superstructure is a…
A Novel Document Generation Process for Topic Detection based on Hierarchical Latent Tree Models
Peixian Chen, Zhourong Chen, Nevin L. Zhang
We propose a novel document generation process based on hierarchical latent tree models (HLTMs) learned from data. An HLTM has a layer of observed word variables at the bottom and…
Sparse Boltzmann Machines with Structure Learning as Applied to Text Analysis
Zhourong Chen, Nevin L. Zhang, Dit-Yan Yeung +1
We are interested in exploring the possibility and benefits of structure learning for deep models. As the first step, this paper investigates the matter for Restricted Boltzmann Ma…
Latent Tree Models for Hierarchical Topic Detection
Peixian Chen, Nevin L. Zhang, Tengfei Liu +3
We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a c…
Progressive EM for Latent Tree Models and Hierarchical Topic Detection
Peixian Chen, Nevin L. Zhang, Leonard K. M. Poon +1
Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic definition,…
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang +3
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined t…