Cross-Loss Influence Functions to Explain Deep Network Representations

Cross-Loss Influence Functions to Explain Deep Network Representations

3 December 2020

Matthew C. Gombolay

Papers citing "Cross-Loss Influence Functions to Explain Deep Network Representations"

10 / 10 papers shown

Title
Detecting Instruction Fine-tuning Attack on Language Models with Influence Function Jiawei Li TDI AAML 33 0 0 12 Apr 2025
How Video Meetings Change Your Expression Sumit Sarin Utkarsh Mall Purva Tendulkar Carl Vondrick CVBM 32 0 0 03 Jun 2024
Evolving Interpretable Visual Classifiers with Large Language Models Mia Chiquier Utkarsh Mall Carl Vondrick VLM 28 10 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 Mengnan Du XAI 37 8 0 09 Jan 2024
Sample based Explanations via Generalized Representers Che-Ping Tsai Chih-Kuan Yeh Pradeep Ravikumar FAtt 39 8 0 27 Oct 2023
Addressing Selection Bias in Computerized Adaptive Testing: A User-Wise Aggregate Influence Function Approach Soonwoo Kwon Sojung Kim S. Lee Jin-Young Kim Suyeong An Kyuseok Kim 14 3 0 23 Aug 2023
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling Stella Biderman Hailey Schoelkopf Quentin G. Anthony Herbie Bradley Kyle O'Brien ... USVSN Sai Prashanth Edward Raff Aviya Skowron Lintang Sutawika Oskar van der Wal 30 1,165 0 03 Apr 2023
Towards Reconciling Usability and Usefulness of Explainable AI Methodologies Pradyumna Tambwekar Matthew C. Gombolay 28 8 0 13 Jan 2023
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017
Efficient Estimation of Word Representations in Vector Space Tomáš Mikolov Kai Chen G. Corrado J. Dean 3DV 233 31,253 0 16 Jan 2013