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2012.01685
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Cross-Loss Influence Functions to Explain Deep Network Representations
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
3 December 2020
Andrew Silva
Rohit Chopra
Matthew C. Gombolay
TDI
Re-assign community
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Papers citing
"Cross-Loss Influence Functions to Explain Deep Network Representations"
7 / 7 papers shown
Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
Marko Tuononen
Heikki Penttinen
Ville Hautamäki
250
0
0
19 Sep 2025
Detecting Instruction Fine-tuning Attacks using Influence Function
Jiawei Li
TDI
AAML
480
1
0
12 Apr 2025
How Video Meetings Change Your Expression
Sumit Sarin
Utkarsh Mall
Purva Tendulkar
Carl Vondrick
CVBM
341
0
0
03 Jun 2024
Evolving Interpretable Visual Classifiers with Large Language Models
Mia Chiquier
Utkarsh Mall
Carl Vondrick
VLM
344
22
0
15 Apr 2024
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
Haoyi Xiong
Xuhong Li
Xiaofei Zhang
Jiamin Chen
Xinhao Sun
Yuchen Li
Zeyi Sun
Jundong Li
XAI
413
15
0
09 Jan 2024
Addressing Selection Bias in Computerized Adaptive Testing: A User-Wise Aggregate Influence Function Approach
International Conference on Information and Knowledge Management (CIKM), 2023
Soonwoo Kwon
Sojung Kim
S. Lee
Jin-Young Kim
Suyeong An
Kyuseok Kim
140
9
0
23 Aug 2023
Towards Reconciling Usability and Usefulness of Explainable AI Methodologies
Pradyumna Tambwekar
Matthew C. Gombolay
198
9
0
13 Jan 2023
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