activity
20182021
most citedLearning Soft Labels via Meta Learning

18 citations · 24 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV20213 cited

Instance-Level Task Parameters: A Robust Multi-task Weighting Framework

Pavan Kumar Anasosalu Vasu, Shreyas Saxena, Oncel Tuzel

Recent works have shown that deep neural networks benefit from multi-task learning by learning a shared representation across several related tasks. However, performance of such sy…

cs.LG20211 cited

Training With Data Dependent Dynamic Learning Rates

Shreyas Saxena, Nidhi Vyas, Dennis DeCoste

Recently many first and second order variants of SGD have been proposed to facilitate training of Deep Neural Networks (DNNs). A common limitation of these works stem from the fact…

eess.AS20212 cited

Dynamic curriculum learning via data parameters for noise robust keyword spotting

Takuya Higuchi, Shreyas Saxena, Mehrez Souden +3

We propose dynamic curriculum learning via data parameters for noise robust keyword spotting. Data parameter learning has recently been introduced for image processing, where weigh…

q-bio.NC2020

Chronic, cortex-wide imaging of specific cell populations during behavior

Joao Couto, Simon Musall, Xiaonan R Sun +8

Measurements of neuronal activity across brain areas are important for understanding the neural correlates of cognitive and motor processes like attention, decision-making, and act…

cs.LG202018 cited

Learning Soft Labels via Meta Learning

Nidhi Vyas, Shreyas Saxena, Thomas Voice

One-hot labels do not represent soft decision boundaries among concepts, and hence, models trained on them are prone to overfitting. Using soft labels as targets provide regulariza…

stat.ML2018

Nonlinear Evolution via Spatially-Dependent Linear Dynamics for Electrophysiology and Calcium Data

Daniel Hernandez, Antonio Khalil Moretti, Ziqiang Wei +3

Latent variable models have been widely applied for the analysis of time series resulting from experimental neuroscience techniques. In these datasets, observations are relatively…