Oct 14, 2020 · In particular, we find that gradual pruning allows access to narrow, well-generalizing minima, which are typically ignored when using one-shot ...

Apr 30, 2020 · Title:Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima ; Subjects: Machine Learning (cs.LG); ...

Pruning Artificial Neural Networks: A Way to Find Well-Generalizing, High-Entropy Sharp Minima. Publisher: Published version: DOI:10.1007/978-3-030-61616-8_6.

In particular, we find that gradual pruning allows access to narrow, well-generalizing minima, which are typically ignored when using one-shot approaches. In ...

This work compares and analyzes pruned solutions with two different pruning approaches, one-shot and gradual, and finds that gradual pruning allows access ...

In particular, we find that gradual pruning allows access to narrow, well-generalizing minima, which are typically ignored when using one-shot approaches. In ...

However, there is a general lack in understanding why these pruning strategies are effective. In this work, we are going to compare and analyze pruned solutions ...

However, there is a general lack in understanding why these pruning strategies are effective. In this work, we are going to compare and analyze pruned solutions ...

Bibliographic details on Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima.

Pruning artificial neural networks: A way to find well-generalizing, high-entropy sharp minima ... A round-trip journey in pruned artificial neural networks.