22 citations · 42 across the 10 of their papers we have counts for
10 papers
Slumbering to Precision: Enhancing Artificial Neural Network Calibration Through Sleep-like Processes
Jean Erik Delanois, Aditya Ahuja, Giri P. Krishnan +1
Artificial neural networks are often overconfident, undermining trust because their predicted probabilities do not match actual accuracy. Inspired by biological sleep and the role…
-ML Ensembles for Selecting Quantum Chemistry Methods to Compute Intermolecular Interactions
Austin M. Wallace, C. David Sherrill, Giri P. Krishnan
Ab initio quantum chemical methods for accurately computing interactions between molecules have a wide range of applications but are often computationally expensive. Hence, selecti…
Quantifying the Role of OpenFold Components in Protein Structure Prediction
Tyler L. Hayes, Giri P. Krishnan
Models such as AlphaFold2 and OpenFold have transformed protein structure prediction, yet their inner workings remain poorly understood. We present a methodology to systematically…
Unsupervised Replay Strategies for Continual Learning with Limited Data
Anthony Bazhenov, Pahan Dewasurendra, Giri P. Krishnan +1
Artificial neural networks (ANNs) show limited performance with scarce or imbalanced training data and face challenges with continuous learning, such as forgetting previously learn…
Sleep-Like Unsupervised Replay Improves Performance when Data are Limited or Unbalanced
Anthony Bazhenov, Pahan Dewasurendra, Giri Krishnan +1
The performance of artificial neural networks (ANNs) degrades when training data are limited or imbalanced. In contrast, the human brain can learn quickly from just a few examples.…
Replay in Deep Learning: Current Approaches and Missing Biological Elements
Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov +3
Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences. Replay was first observed in bio…