37 citations · 129 across the 31 of their papers we have counts for
5 papers · 1 filter
Deep Probabilistic Models to Detect Data Poisoning Attacks
Mahesh Subedar, Nilesh Ahuja, Ranganath Krishnan +2
Data poisoning attacks compromise the integrity of machine-learning models by introducing malicious training samples to influence the results during test time. In this work, we inv…
Tree pyramidal adaptive importance sampling
Javier Felip, Nilesh Ahuja, Omesh Tickoo
This paper introduces Tree-Pyramidal Adaptive Importance Sampling (TP-AIS), a novel iterated sampling method that outperforms state-of-the-art approaches like deterministic mixture…
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection
Nilesh A. Ahuja, Ibrahima Ndiour, Trushant Kalyanpur +1
We present a principled approach for detecting out-of-distribution (OOD) and adversarial samples in deep neural networks. Our approach consists in modeling the outputs of the vario…
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
Ranganath Krishnan, Mahesh Subedar, Omesh Tickoo
Stochastic variational inference for Bayesian deep neural network (DNN) requires specifying priors and approximate posterior distributions over neural network weights. Specifying m…
Real-time Approximate Bayesian Computation for Scene Understanding
Javier Felip, Nilesh Ahuja, David Gómez-Gutiérrez +2
Consider scene understanding problems such as predicting where a person is probably reaching, or inferring the pose of 3D objects from depth images, or inferring the probable stree…