37 citations · 209 across the 24 of their papers we have counts for
5 papers · 1 filter
Efficient Feature Transformations for Discriminative and Generative Continual Learning
Vinay Kumar Verma, Kevin J Liang, Nikhil Mehta +2
As neural networks are increasingly being applied to real-world applications, mechanisms to address distributional shift and sequential task learning without forgetting are critica…
Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?
Nathan Inkawhich, Kevin J Liang, Jingyang Zhang +3
We design blackbox transfer-based targeted adversarial attacks for an environment where the attacker's source model and the target blackbox model may have disjoint label spaces and…
WAFFLe: Weight Anonymized Factorization for Federated Learning
Weituo Hao, Nikhil Mehta, Kevin J Liang +3
In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices. In light of…
Transferable Perturbations of Deep Feature Distributions
Nathan Inkawhich, Kevin J Liang, Lawrence Carin +1
Almost all current adversarial attacks of CNN classifiers rely on information derived from the output layer of the network. This work presents a new adversarial attack based on the…
Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors
Nikhil Mehta, Kevin J Liang, Vinay K Verma +1
Naively trained neural networks tend to experience catastrophic forgetting in sequential task settings, where data from previous tasks are unavailable. A number of methods, using v…