Search Results for author:
Found 3 papers, 1 papers with code
Parallel Algorithms for Exact Enumeration of Deep Neural Network Activation Regions
Our work has three main contributions: (1) we present a novel algorithm framework and parallel algorithms for region enumeration; (2) we implement one of our algorithms on a variety of network architectures and experimentally show how the number of regions dictates runtime; and (3) we show, using our algorithm's output, how the dimension of a region's affine transformation impacts further partitioning of the region by deeper layers.
Continual Learning with Self-Organizing Maps
Most previous approaches to this problem rely on memory replay buffers which store samples from previously learned tasks, and use them to regularize the learning on new ones.
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples.
