Search Results for author: Eilif Muller

Found 4 papers, 2 papers with code

GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models

1 code implementation5 Nov 2024 Nizar Islah, Justine Gehring, Diganta Misra, Eilif Muller, Irina Rish, Terry Yue Zhuo, Massimo Caccia

The rapid evolution of software libraries presents a significant challenge for code generation models, which must adapt to frequent version updates while maintaining compatibility with previous versions.

Code Completion Code Generation

Learning to combine top-down context and feed-forward representations under ambiguity with apical and basal dendrites

no code implementations9 Dec 2023 Nizar Islah, Guillaume Etter, Mashbayar Tugsbayar, Tugce Gurbuz, Blake Richards, Eilif Muller

Altogether, this suggests that the apical prior and biophysically inspired integration rule could be key components necessary for handling the ambiguities that animals encounter in the diverse contexts of the real world.

Anatomy Temporal Sequences

Generating Diverse Realistic Laughter for Interactive Art

no code implementations4 Nov 2021 M. Mehdi Afsar, Eric Park, Étienne Paquette, Gauthier Gidel, Kory W. Mathewson, Eilif Muller

We propose an interactive art project to make those rendered invisible by the COVID-19 crisis and its concomitant solitude reappear through the welcome melody of laughter, and connections created and explored through advanced laughter synthesis approaches.

Diversity

Predicting Infectiousness for Proactive Contact Tracing

1 code implementation ICLR 2021 Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT).

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