2 papers
cs.LG2024
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou +3
A major challenge in sample-based inference (SBI) for Bayesian neural networks is the size and structure of the networks' parameter space. Our work shows that successful SBI is pos…
cs.LG2020
Depth-2 Neural Networks Under a Data-Poisoning Attack
Sayar Karmakar, Anirbit Mukherjee, Theodore Papamarkou
In this work, we study the possibility of defending against data-poisoning attacks while training a shallow neural network in a regression setup. We focus on doing supervised learn…