318 citations · 825 across the 71 of their papers we have counts for
6 papers · 1 filter
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
Ting-Yao Hu, Ashish Shrivastava, Jen-Hao Rick Chang +5
Data augmentation methods usually apply the same augmentation (or a mix of them) to all the training samples. For example, to perturb data with noise, the noise is sampled from a N…
Optimize what matters: Training DNN-HMM Keyword Spotting Model Using End Metric
Ashish Shrivastava, Arnav Kundu, Chandra Dhir +2
Deep Neural Network--Hidden Markov Model (DNN-HMM) based methods have been successfully used for many always-on keyword spotting algorithms that detect a wake word to trigger a dev…
Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution
Hadi Pouransari, Mojan Javaheripi, Vinay Sharma +1
Knowledge distillation has been used to transfer knowledge learned by a sophisticated model (teacher) to a simpler model (student). This technique is widely used to compress model…
Subject-Aware Contrastive Learning for Biosignals
Joseph Y. Cheng, Hanlin Goh, Kaan Dogrusoz +2
Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100). To handle these challen…
Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis
Ting-Yao Hu, Ashish Shrivastava, Oncel Tuzel +1
We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i.e., no style annotation, suc…
Least squares binary quantization of neural networks
Hadi Pouransari, Zhucheng Tu, Oncel Tuzel
Quantizing weights and activations of deep neural networks results in significant improvement in inference efficiency at the cost of lower accuracy. A source of the accuracy gap be…