21 citations · 30 across the 9 of their papers we have counts for
9 papers
Test-Time Training for Speech
Sri Harsha Dumpala, Chandramouli Sastry, Sageev Oore
In this paper, we study the application of Test-Time Training (TTT) as a solution to handling distribution shifts in speech applications. In particular, we introduce distribution-s…
DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers
Chandramouli Sastry, Sri Harsha Dumpala, Sageev Oore
We introduce DiffAug, a simple and efficient diffusion-based augmentation technique to train image classifiers for the crucial yet challenging goal of improved classifier robustnes…
Benchmarking Neural Network Training Algorithms
George E. Dahl, Frank Schneider, Zachary Nado +22
Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workl…
Efficient CDF Approximations for Normalizing Flows
Chandramouli Shama Sastry, Andreas Lehrmann, Marcus Brubaker +1
Normalizing flows model a complex target distribution in terms of a bijective transform operating on a simple base distribution. As such, they enable tractable computation of a num…
Musical Speech: A Transformer-based Composition Tool
Jason d'Eon, Sri Harsha Dumpala, Chandramouli Shama Sastry +2
In this paper, we propose a new compositional tool that will generate a musical outline of speech recorded/provided by the user for use as a musical building block in their composi…
Local recovery bounds for prior support constrained Compressed Sensing
K. Z. Najiya, Munnu Sonkar, C. S. Sastry
Prior support constrained compressed sensing has of late become popular due to its potential for applications. The existing results on recovery guarantees provide global recovery b…
Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices
Chandramouli Shama Sastry, Sageev Oore
When presented with Out-of-Distribution (OOD) examples, deep neural networks yield confident, incorrect predictions. Detecting OOD examples is challenging, and the potential risks…
Novel Light Weight Compressed Data Aggregation Using Sparse Measurements for IoT Networks
Amarlingam M, Pradeep Kumar Mishra, P Rajalakshmi +2
Optimal data aggregation aimed at maximizing IoT network lifetime by minimizing constrained on-board resource utilization continues to be a challenging task. The existing data aggr…
Nullspace Property for Optimality of Minimum Frame Angle Under Invertible Linear Operators
Pradip Sasmal, Prasad Theeda, Phanindra Jampana +1
Orthogonal Matching Pursuit and Basis Pursuit are popular reconstruction algorithms for recovery of sparse signals. The exact recovery property of both the methods has a relation w…