Weiran Wang

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Amazon ∙ University of California, Merced ∙ Toyota Technological Institute at Chicago

Recently Graduated at Toyota Technological Institute at Chicago

Representation Learning for Sequence Data with Deep Autoencoding Predictive Components

We propose Deep Autoencoding Predictive Components (DAPC) – a self-super...
10/07/2020 ∙ by Junwen Bai, et al. ∙ 0 ∙ share

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A Comparison of Pooling Methods on LSTM Models for Rare Acoustic Event Classification

Acoustic event classification (AEC) and acoustic event detection (AED) r...
02/14/2020 ∙ by Chieh-Chi Kao, et al. ∙ 0 ∙ share

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Unsupervised Pre-training of Bidirectional Speech Encoders via Masked Reconstruction

We propose an approach for pre-training speech representations via a mas...
01/28/2020 ∙ by Weiran Wang, et al. ∙ 0 ∙ share

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Data Techniques For Online End-to-end Speech Recognition

Practitioners often need to build ASR systems for new use cases in a sho...
01/24/2020 ∙ by Yang Chen, et al. ∙ 0 ∙ share

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Semi-supervised ASR by End-to-end Self-training

While deep learning based end-to-end automatic speech recognition (ASR) ...
01/24/2020 ∙ by Yang Chen, et al. ∙ 0 ∙ share

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Acoustic scene analysis with multi-head attention networks

Acoustic Scene Classification (ASC) is a challenging task, as a single s...
09/16/2019 ∙ by Weimin Wang, et al. ∙ 9 ∙ share

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Multimodal and Multi-view Models for Emotion Recognition

Studies on emotion recognition (ER) show that combining lexical and acou...
06/24/2019 ∙ by Gustavo Aguilar, et al. ∙ 0 ∙ share

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Everything old is new again: A multi-view learning approach to learning using privileged information and distillation

We adopt a multi-view approach for analyzing two knowledge transfer sett...
03/08/2019 ∙ by Weiran Wang, et al. ∙ 0 ∙ share

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A simple model for detection of rare sound events

We propose a simple recurrent model for detecting rare sound events, whe...
08/20/2018 ∙ by Weiran Wang, et al. ∙ 0 ∙ share

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R-CRNN: Region-based Convolutional Recurrent Neural Network for Audio Event Detection

This paper proposes a Region-based Convolutional Recurrent Neural Networ...
08/20/2018 ∙ by Chieh-Chi Kao, et al. ∙ 0 ∙ share

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Acoustic feature learning using cross-domain articulatory measurements

Previous work has shown that it is possible to improve speech recognitio...
03/19/2018 ∙ by Qingming Tang, et al. ∙ 0 ∙ share

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Acoustic feature learning cross-domain articulatory measurements

Previous work has shown that it is possible to improve speech recognitio...
03/19/2018 ∙ by Qingming Tang, et al. ∙ 0 ∙ share

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Distributed Stochastic Multi-Task Learning with Graph Regularization

We propose methods for distributed graph-based multi-task learning that ...
02/11/2018 ∙ by Weiran Wang, et al. ∙ 0 ∙ share

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Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis

We study the problem of acoustic feature learning in the setting where w...
08/11/2017 ∙ by Qingming Tang, et al. ∙ 0 ∙ share

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Efficient coordinate-wise leading eigenvector computation

We develop and analyze efficient "coordinate-wise" methods for finding t...
02/25/2017 ∙ by Jialei Wang, et al. ∙ 0 ∙ share

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Stochastic Canonical Correlation Analysis

We tightly analyze the sample complexity of CCA, provide a learning algo...
02/21/2017 ∙ by Chao Gao, et al. ∙ 0 ∙ share

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Multi-view Recurrent Neural Acoustic Word Embeddings

Recent work has begun exploring neural acoustic word embeddings---fixed-...
11/14/2016 ∙ by Wanjia He, et al. ∙ 0 ∙ share

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End-to-End Training Approaches for Discriminative Segmental Models

Recent work on discriminative segmental models has shown that they can a...
10/21/2016 ∙ by Hao Tang, et al. ∙ 0 ∙ share

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Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation

We study the problem of recognizing video sequences of fingerspelled let...
09/26/2016 ∙ by Taehwan Kim, et al. ∙ 0 ∙ share

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Efficient Segmental Cascades for Speech Recognition

Discriminative segmental models offer a way to incorporate flexible feat...
08/02/2016 ∙ by Hao Tang, et al. ∙ 0 ∙ share

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Signer-independent Fingerspelling Recognition with Deep Neural Network Adaptation

We study the problem of recognition of fingerspelled letter sequences in...
02/13/2016 ∙ by Taehwan Kim, et al. ∙ 0 ∙ share

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Network Inference by Learned Node-Specific Degree Prior

We propose a novel method for network inference from partially observed ...
02/07/2016 ∙ by Qingming Tang, et al. ∙ 0 ∙ share

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On Column Selection in Approximate Kernel Canonical Correlation Analysis

We study the problem of column selection in large-scale kernel canonical...
02/05/2016 ∙ by Weiran Wang, et al. ∙ 0 ∙ share

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Nonparametric Canonical Correlation Analysis

Canonical correlation analysis (CCA) is a classical representation learn...
11/16/2015 ∙ by Tomer Michaeli, et al. ∙ 0 ∙ share

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Deep convolutional acoustic word embeddings using word-pair side information

Recent studies have been revisiting whole words as the basic modelling u...
10/05/2015 ∙ by Herman Kamper, et al. ∙ 0 ∙ share

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Discriminative Segmental Cascades for Feature-Rich Phone Recognition

Discriminative segmental models, such as segmental conditional random fi...
07/22/2015 ∙ by Hao Tang, et al. ∙ 0 ∙ share

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The role of dimensionality reduction in linear classification

Dimensionality reduction (DR) is often used as a preprocessing step in c...
05/26/2014 ∙ by Weiran Wang, et al. ∙ 0 ∙ share

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LASS: a simple assignment model with Laplacian smoothing

We consider the problem of learning soft assignments of N items to K cat...
05/23/2014 ∙ by Miguel Á. Carreira-Perpiñán, et al. ∙ 0 ∙ share

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Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application

We provide an elementary proof of a simple, efficient algorithm for comp...
09/06/2013 ∙ by Weiran Wang, et al. ∙ 0 ∙ share

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The K-modes algorithm for clustering

Many clustering algorithms exist that estimate a cluster centroid, such ...
04/24/2013 ∙ by Miguel Á. Carreira-Perpiñán, et al. ∙ 0 ∙ share

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Distributed optimization of deeply nested systems

In science and engineering, intelligent processing of complex signals su...
12/24/2012 ∙ by Miguel Á. Carreira-Perpiñán, et al. ∙ 0 ∙ share

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Weiran Wang

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