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2026 Participants: Martin Bartelmus * David M. Berry * Alan Blackwell * Gregory Bringman * David Cao * Claire Carroll * Sean Cho Ayres * Hunmin Choi * Jongchan Choi * Lyr Colin * Dan Cox * Christina Cuneo * Orla Delaney * Adrian Demleitner * Pierre Depaz * Mehulkumar Desai * Ranjodh Singh Dhaliwal * Koundinya Dhulipalla * Kevin Driscoll * Iain Emsley * Michael Falk * Leonardo Flores * Jordan Freitas * Aide Violeta Fuentes Barron * Erika Fülöp * Tiffany Fung * Sarah Groff Hennigh-Palermo * Gregor Große-Bölting * Dennis Jerz * Joey Jones * Titaÿna Kauffmann * Haley Kinsler * Todd Millstein * Charu Maithani * Judy Malloy * Eon Meridian * Luis Navarro * Collier Nogues * Stefano Penge * Marta Perez-Campos * Arpita Rathod * Abby Rinaldi * Ari Schlesinger * Carly Schnitzler * Arthur Schwarz * Haerin Shin * Jongbeen Song * Harlin/Hayley Steele * Daniel Temkin * Zach Whalen * Zijian Xia * Waliya Yohanna * Zachary Mann
CCSWG 2026 is coordinated by Lyr Colin-Pacheco (USC), Jeremy Douglass (UCSB), and Mark C. Marino (USC). Sponsored by the Humanities and Critical Code Studies Lab (USC), the Transcriptions Lab (UCSB), and the Digital Arts and Humanities Commons (UCSB).
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minhhua12345
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Comments
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I appreciate the discussion on close reading core deep learning code (i.e. code that directly contributes to the production of prediction, like model architecture and weights), but I also wanted to start a separate thread on code that our paper term…
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@eleazhong The analysis you provide is a right step towards what a CCS close reading of the "internals" (e.g. weights, embeddings, model architecture) of a machine learning model might look like. Without a complete understanding of how a machine lea…
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How do we scale our close reading of vectorization and word embeddings (e.g. Alison Parish's "Experimental Creative Writing with the Vectorized Word" for today's models? Can our understanding of vectorization (e.g. "man + ruler = king") adequately b…