23 citations · 35 across the 6 of their papers we have counts for
8 papers
Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects
Boyang Yu, Aakash Kaku, Kangning Liu +7
Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a novel framework to address this challenge, which leverages AI model…
Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution
Aakash Kaku, Kangning Liu, Avinash Parnandi +7
Automatic action identification from video and kinematic data is an important machine learning problem with applications ranging from robotics to smart health. Most existing works…
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
Aakash Kaku, Sahana Upadhya, Narges Razavian
We show that bringing intermediate layers' representations of two augmented versions of an image closer together in self-supervised learning helps to improve the momentum contrasti…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…
Towards data-driven stroke rehabilitation via wearable sensors and deep learning
Aakash Kaku, Avinash Parnandi, Anita Venkatesan +3
Recovery after stroke is often incomplete, but rehabilitation training may potentiate recovery by engaging endogenous neuroplasticity. In preclinical models of stroke, high doses o…
Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization
Aakash Kaku, Sreyas Mohan, Avinash Parnandi +2
Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data. In this work, we show that the presence of suc…