Publications (6)
Quantum adiabatic machine learning with zooming
Alexander Zlokapa, Alex Mott, Joshua Job +3
Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific applicat…
Orthogonal Gradient Descent for Continual Learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott +1
Neural networks are achieving state of the art and sometimes super-human performance on learning tasks across a variety of domains. Whenever these problems require learning in a co…
Optimization and Generalization of Regularization-Based Continual Learning: a Loss Approximation Viewpoint
Dong Yin, Mehrdad Farajtabar, Ang Li +2
Neural networks have achieved remarkable success in many cognitive tasks. However, when they are trained sequentially on multiple tasks without access to old data, their performanc…
The Effectiveness of Memory Replay in Large Scale Continual Learning
Yogesh Balaji, Mehrdad Farajtabar, Dong Yin +2
We study continual learning in the large scale setting where tasks in the input sequence are not limited to classification, and the outputs can be of high dimension. Among multiple…
Towards Robust Image Classification Using Sequential Attention Models
Daniel Zoran, Mike Chrzanowski, Po-Sen Huang +3
In this paper we propose to augment a modern neural-network architecture with an attention model inspired by human perception. Specifically, we adversarially train and analyze a ne…
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
Alex Mott, Daniel Zoran, Mike Chrzanowski +2
Inspired by recent work in attention models for image captioning and question answering, we present a soft attention model for the reinforcement learning domain. This model uses a…