204 citations · 784 across the 42 of their papers we have counts for
10 papers · 1 filter
Multi-view Recurrent Neural Acoustic Word Embeddings
Wanjia He, Weiran Wang, Karen Livescu
Recent work has begun exploring neural acoustic word embeddings---fixed-dimensional vector representations of arbitrary-length speech segments corresponding to words. Such embeddin…
End-to-End Training Approaches for Discriminative Segmental Models
Hao Tang, Weiran Wang, Kevin Gimpel +1
Recent work on discriminative segmental models has shown that they can achieve competitive speech recognition performance, using features based on deep neural frame classifiers. Ho…
Deep Variational Canonical Correlation Analysis
Weiran Wang, Xinchen Yan, Honglak Lee +1
We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear…
Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation
Taehwan Kim, Jonathan Keane, Weiran Wang +5
We study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises a significant but relatively understudied par…
Efficient Segmental Cascades for Speech Recognition
Hao Tang, Weiran Wang, Kevin Gimpel +1
Discriminative segmental models offer a way to incorporate flexible feature functions into speech recognition. However, their appeal has been limited by their computational require…
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
Weiran Wang, Jialei Wang, Dan Garber +1
We study the stochastic optimization of canonical correlation analysis (CCA), whose objective is nonconvex and does not decouple over training samples. Although several stochastic…
Signer-independent Fingerspelling Recognition with Deep Neural Network Adaptation
Taehwan Kim, Weiran Wang, Hao Tang +1
We study the problem of recognition of fingerspelled letter sequences in American Sign Language in a signer-independent setting. Fingerspelled sequences are both challenging and im…
Network Inference by Learned Node-Specific Degree Prior
Qingming Tang, Lifu Tu, Weiran Wang +1
We propose a novel method for network inference from partially observed edges using a node-specific degree prior. The degree prior is derived from observed edges in the network to…
On Column Selection in Approximate Kernel Canonical Correlation Analysis
Weiran Wang
We study the problem of column selection in large-scale kernel canonical correlation analysis (KCCA) using the Nyström approximation, where one approximates two positive semi-defin…
On Deep Multi-View Representation Learning: Objectives and Optimization
Weiran Wang, Raman Arora, Karen Livescu +1
We consider learning representations (features) in the setting in which we have access to multiple unlabeled views of the data for learning while only one view is available for dow…