When less is more: Simplifying inputs aids neural network understanding

When less is more: Simplifying inputs aids neural network understanding

14 January 2022

R. Schirrmeister

Papers citing "When less is more: Simplifying inputs aids neural network understanding"

11 / 11 papers shown

Title
Data Distillation: A Survey Noveen Sachdeva Julian McAuley DD 25 73 0 11 Jan 2023
Accelerating Dataset Distillation via Model Augmentation Lei Zhang Jie M. Zhang Bowen Lei Subhabrata Mukherjee Xiang Pan Bo-Lu Zhao Caiwen Ding Y. Li Dongkuan Xu DD 21 62 0 12 Dec 2022
"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification Jasmijn Bastings Sebastian Ebert Polina Zablotskaia Anders Sandholm Katja Filippova 110 75 0 14 Nov 2021
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining Andreas Madsen Nicholas Meade Vaibhav Adlakha Siva Reddy 96 35 0 15 Oct 2021
Dataset Condensation with Differentiable Siamese Augmentation Bo-Lu Zhao Hakan Bilen DD 189 288 0 16 Feb 2021
Hierarchical VAEs Know What They Don't Know Jakob Drachmann Havtorn J. Frellsen Søren Hauberg Lars Maaløe DRL 30 71 0 16 Feb 2021
High-Performance Large-Scale Image Recognition Without Normalization Andrew Brock Soham De Samuel L. Smith Karen Simonyan VLM 223 512 0 11 Feb 2021
Estimating Example Difficulty Using Variance of Gradients Chirag Agarwal Daniel D'souza Sara Hooker 195 107 0 26 Aug 2020
Meta Pseudo Labels Hieu H. Pham Zihang Dai Qizhe Xie Minh-Thang Luong Quoc V. Le VLM 248 656 0 23 Mar 2020
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,233 0 24 Jun 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 243 11,659 0 09 Mar 2017