2.4k citations · 3.2k across the 7 of their papers we have counts for
7 papers
A multi-modal neural network for learning cis and trans regulation of stress response in yeast
Boxiang Liu, Nadine Hussami, Avanti Shrikumar +5
Deciphering gene regulatory networks is a central problem in computational biology. Here, we explore the use of multi-modal neural networks to learn predictive models of gene expre…
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
Amr Alexandari, Anshul Kundaje, Avanti Shrikumar
Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixe…
Technical Note on Transcription Factor Motif Discovery from Importance Scores (TF-MoDISco) version 0.5.6.5
Avanti Shrikumar, Katherine Tian, Žiga Avsec +5
TF-MoDISco (Transcription Factor Motif Discovery from Importance Scores) is an algorithm for identifying motifs from basepair-level importance scores computed on genomic sequence d…
Computationally Efficient Measures of Internal Neuron Importance
Avanti Shrikumar, Jocelin Su, Anshul Kundaje
The challenge of assigning importance to individual neurons in a network is of interest when interpreting deep learning models. In recent work, Dhamdhere et al. proposed Total Cond…
A General Framework for Abstention Under Label Shift
Amr M. Alexandari, Anshul Kundaje, Avanti Shrikumar
In safety-critical applications of machine learning, it is often important to abstain from making predictions on low confidence examples. Standard abstention methods tend to be foc…
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar, Peyton Greenside, Anshul Kundaje
The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important…