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Poster
Fri 16:30 On the Parameter Identifiability of Partially Observed Linear Causal Models
Xinshuai Dong · Ignavier Ng · Biwei Huang · Yuewen Sun · Songyao Jin · Roberto Legaspi · Peter Spirtes · Kun Zhang
Workshop
Identifying and Addressing Delusions for Target-Directed Decision Making
Mingde Zhao · Tristan Sylvain · Doina Precup · Yoshua Bengio
Workshop
DIETing: Self-Supervised Learning with Instance Discrimination Learns Identifiable Features
Attila Juhos · Alice Bizeul · Patrik Reizinger · Randall Balestriero · David Klindt · Mark Ibrahim · Julia Vogt · Wieland Brendel
Workshop
Explainable AI for computational pathology identifies model limitations and tissue biomarkers
Jakub Roman Kaczmarzyk · Joel Saltz · Peter Koo
Workshop
KAN4Drift: Are KAN Effective for Identifying and Tracking Concept Drift in Time Series?
Kunpeng Xu · Lifei Chen · Shengrui Wang
Workshop
A method for identifying causality in the response of nonlinear dynamical systems
Joseph Massingham · Ole Nielsen · Tore Butlin
Workshop
DIETing: Self-Supervised Learning with Instance Discrimination Learns Identifiable Features
Attila Juhos · Alice Bizeul · Patrik Reizinger · David Klindt · Randall Balestriero · Mark Ibrahim · Julia Vogt · Wieland Brendel
Workshop
Challenges in Explaining Representational Similarity through Identifiability
Beatrix M. G. Nielsen · Luigi Gresele · Andrea Dittadi
Workshop
Position: Maximizing Neural Regression Scores May Not Identify Good Models of the Brain
Rylan Schaeffer · Mikail Khona · Sarthak Chandra · Mitchell Ostrow · Brando Miranda · Sanmi Koyejo
Workshop
Using Scenario-Writing for Identifying and Mitigating Impacts of Generative AI
Kimon Kieslich · Nicholas Diakopoulos · Natali Helberger
Workshop
Quantifying Likeness: A Simple Machine Learning Approach to Identifying Copyright Infringement in (AI-Generated) Artwork
Michaela Drouillard · Ryan Spencer · Nikée Nantambu-Allen · Tegan Maharaj
Workshop
Efficiently Identifying Watermarked Segments in Mixed-Source Texts
Xuandong Zhao · Chenwen Liao · Yu-Xiang Wang · Lei Li