172 citations · 446 across the 27 of their papers we have counts for
4 papers · 1 filter
Efficiently Training Low-Curvature Neural Networks
Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju +1
The highly non-linear nature of deep neural networks causes them to be susceptible to adversarial examples and have unstable gradients which hinders interpretability. However, exis…
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations
Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
A critical problem in the field of post hoc explainability is the lack of a common foundational goal among methods. For example, some methods are motivated by function approximatio…
Data-Efficient Structured Pruning via Submodular Optimization
Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien
Structured pruning is an effective approach for compressing large pre-trained neural networks without significantly affecting their performance. However, most current structured pr…
Cyclical Pruning for Sparse Neural Networks
Suraj Srinivas, Andrey Kuzmin, Markus Nagel +3
Current methods for pruning neural network weights iteratively apply magnitude-based pruning on the model weights and re-train the resulting model to recover lost accuracy. In this…