Caffeine induces age-dependent increases in brain complexity and criticality during sleep
Philipp Thölke
Maxine Arcand-Lavigne
Tarek Lajnef
Sonia Frenette
Julie Carrier
ImmunoStruct: a multimodal neural network framework for immunogenicity prediction from peptide-MHC sequence, structure, and biochemical properties
Kevin Bijan Givechian
João Felipe Rocha
Edward Yang
Chen Liu
Kerrie Greene
Rex Ying
Etienne Caron
Akiko Iwasaki
ImmunoStruct: a multimodal neural network framework for immunogenicity prediction from peptide-MHC sequence, structure, and biochemical properties
Kevin Bijan Givechian
João Felipe Rocha
Edward Yang
Chen Liu
Kerrie Greene
Rex Ying
Etienne Caron
Akiko Iwasaki
Rootlets-based registration to the spinal cord PAM50 template
Sandrine B'edard
Jan Valovsek
Valeria Oliva
Kenneth A. Weber
Performance Smells in ML and Non-ML Python Projects: A Comparative Study
Franccois Belias
Leuson Da Silva
Cyrine Zid
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini
Omri Abend
Dina M. Acklin
Henny Admoni
Gregor Aichinger
Nitay Alon
Zahra Ashktorab
Ashish Atreja
Moises Auron
Alexander Aufreiter
Raghav Awasthi
Soumya Banerjee
Joseph Barnby
Rhea Basappa
Severin Bergsmann
Djallel Bouneffouf
Patrick Callaghan
Marc Cavazza
Thierry Chaminade
Sonia Chernova … (voir 88 de plus)
Mohamed Chetouan
Moumita Choudhury
Axel Cleeremans
J. Cywinski
Fabio Cuzzolin
Hokin Deng
N'yoma Diamond
C. D. Pasquasio
Max J. van Duijn
Mahapatra Dwarikanath
Qingying Gao
Ashok Goel
Rebecca R. Goldstein
Matthew C. Gombolay
Gabriel Enrique Gonzalez
Amar Halilovic
Tobias Halmdienst
Mahimul Islam
Julian Jara-Ettinger
Natalie Kastel
Renana Keydar
Ashish K. Khanna
Mahdi Khoramshahi
Jihyun Kim
Mihyeon Kim
Youngbin Kim
Senka Krivic
Nikita Krasnytskyi
Arun Kumar
JuneHyoung Kwon
EunJu Lee
Shane Lee
Peter R. Lewis 0001
Xue Li
Yijiang Li
Michal Lewandowski
Nathan Lloyd
Matthew B. Luebbers
Dezhi Luo
Haiyun Lyu
Dwarikanath Mahapatra
Kamal Maheshwari
Mallika Mainali
P. Mathur
Patrick Mederitsch
Shuwa Miura
Manuel Preston de Miranda
Reuth Mirsky
Shreya Mishra
Nina M. Moorman
Katelyn Morrison
John Muchovej
Bernhard Nessler
Felix Nessler
Hieu Minh Jord Nguyen
Abby Ortego
F. Papay
Antoine Pasquali
Hamed Rahimi
C. Raghu
Amanda L. Royka
Stefan Sarkadi
Jaelle Scheuerman
Simon Schmid
Paul Schrater
Anik Sen
Zahra Sheikhbahaee
Ke Shi
Reid G. Simmons
Nishant Singh
Mason O. Smith
Ramira van der Meulen
Anthia Solaki
Haoran Sun
Viktor Szolga
Matthew E. Taylor
Travis Taylor
Sanne van Waveren
Juan David Vargas
R. Verbrugge
Eitan Wagner
Justin D. Weisz
Ximing Wen
William Yeoh
Wenlong Zhang
Michelle Zhao
Shlomo Zilberstein
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini
Omri Abend
Dina M. Acklin
Henny Admoni
Gregor Aichinger
Nitay Alon
Zahra Ashktorab
Ashish Atreja
Moises Auron
Alexander Aufreiter
Raghav Awasthi
Soumya Banerjee
Joseph Barnby
Rhea Basappa
Severin Bergsmann
Djallel Bouneffouf
Patrick Callaghan
Marc Cavazza
Thierry Chaminade
Sonia Chernova … (voir 88 de plus)
Mohamed Chetouan
Moumita Choudhury
Axel Cleeremans
J. Cywinski
Fabio Cuzzolin
Hokin Deng
N'yoma Diamond
C. D. Pasquasio
Max J. van Duijn
Mahapatra Dwarikanath
Qingying Gao
Ashok Goel
Rebecca R. Goldstein
Matthew C. Gombolay
Gabriel Enrique Gonzalez
Amar Halilovic
Tobias Halmdienst
Mahimul Islam
Julian Jara-Ettinger
Natalie Kastel
Renana Keydar
Ashish K. Khanna
Mahdi Khoramshahi
Jihyun Kim
Mihyeon Kim
Youngbin Kim
Senka Krivic
Nikita Krasnytskyi
Arun Kumar
JuneHyoung Kwon
EunJu Lee
Shane Lee
Peter R. Lewis 0001
Xue Li
Yijiang Li
Michal Lewandowski
Nathan Lloyd
Matthew B. Luebbers
Dezhi Luo
Haiyun Lyu
Dwarikanath Mahapatra
Kamal Maheshwari
Mallika Mainali
P. Mathur
Patrick Mederitsch
Shuwa Miura
Manuel Preston de Miranda
Reuth Mirsky
Shreya Mishra
Nina M. Moorman
Katelyn Morrison
John Muchovej
Bernhard Nessler
Felix Nessler
Hieu Minh Jord Nguyen
Abby Ortego
F. Papay
Antoine Pasquali
Hamed Rahimi
C. Raghu
Amanda L. Royka
Stefan Sarkadi
Jaelle Scheuerman
Simon Schmid
Paul Schrater
Anik Sen
Zahra Sheikhbahaee
Ke Shi
Reid G. Simmons
Nishant Singh
Mason O. Smith
Ramira van der Meulen
Anthia Solaki
Haoran Sun
Viktor Szolga
Matthew E. Taylor
Travis Taylor
Sanne van Waveren
Juan David Vargas
R. Verbrugge
Eitan Wagner
Justin D. Weisz
Ximing Wen
William Yeoh
Wenlong Zhang
Michelle Zhao
Shlomo Zilberstein
Solving Combinatorial Pricing Problems using Embedded Dynamic Programming Models
Quang Minh Bui
José Neto
The combinatorial pricing problem (CPP) is a bilevel problem in which the leader maximizes their revenue by imposing tolls on certain items … (voir plus)that they can control. Based on the tolls set by the leader, the follower selects a subset of items corresponding to an optimal solution of a combinatorial optimization problem. To accomplish the leader's goal, the tolls need to be sufficiently low to discourage the follower from choosing the items offered by the competitors. In this paper, we derive a single-level reformulation for the CPP by rewriting the follower's problem as a longest path problem using a dynamic programming model, and then taking its dual and applying strong duality. We proceed to solve the reformulation in a dynamic fashion with a cutting plane method. We apply this methodology to 2 distinct dynamic programming models, namely, a novel formulation designated as selection diagram and the well-known decision diagram. We also produce numerical results to evaluate their performances across 3 different specializations of the CPP and a closely related problem that is the knapsack interdiction problem. Our results showcase the potential of the 2 proposed reformulations over the natural value function approach, expanding the set of tools to solve combinatorial bilevel programs.
A Learning-Based Framework for Fair and Scalable Solution Generation in Kidney Exchange Problems
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
Prateek Yadav
Colin Raffel
Mohammed Muqeeth
Lucas Caccia
Haokun Liu
Tianlong Chen
Mohit Bansal
Leshem Choshen
The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particula… (voir plus)r domain or task. Model MoErging methods aim to recycle expert models to create an aggregate system with improved performance or generalization. A key component of MoErging methods is the creation of a router that decides which expert model(s) to use for a particular input or application. The promise, effectiveness, and large design space of MoErging has spurred the development of many new methods over the past few years. This rapid pace of development has made it challenging to compare different MoErging methods, which are rarely compared to one another and are often validated in different experimental setups. To remedy such gaps, we present a comprehensive survey of MoErging methods that includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method. Apart from surveying MoErging research, we inventory software tools and applications that make use of MoErging. We additionally discuss related fields of study such as model merging, multitask learning, and mixture-of-experts models. Taken as a whole, our survey provides a unified overview of existing MoErging methods and creates a solid foundation for future work in this burgeoning field.
RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning
Mingqi Yuan
Roger Creus Castanyer
Bo Li
Xin Jin
Wenjun Zeng
What makes a good public EV charging station? A revealed preference study
Steven Lamontagne
Ribal Atallah