Ilia Sucholutsky

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Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton UniversityVerified email at princeton.edu
Matthias SchonlauUniversity of WaterlooVerified email at uwaterloo.ca
Raja MarjiehPhD Candidate, Princeton UniversityVerified email at princeton.edu
Nori JacobyMax Planck Institute for Empirical Aesthetics. Incoming assistant professor at Cornell (Fall 2024).Verified email at ae.mpg.de
Pol van RijnPhD Candidate, Max Planck Institute for Empirical AestheticsVerified email at ae.mpg.de
Katherine M. CollinsMachine Learning PhD Student at the University of CambridgeVerified email at cam.ac.uk
Adrian WellerDirector of Research, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
Theodore SumersPrinceton UniversityVerified email at princeton.edu
Umang BhattNew York UniversityVerified email at nyu.edu
Harin LeePhD candidate at MPI for Human Cognitive and Brain Sciences / MPI for Empirical AestheticsVerified email at cbs.mpg.de
Claire E RobertsonPhD Student, New York UniversityVerified email at nyu.edu
Dan-Mircea MireaPhD student in Psychology, Princeton UniversityVerified email at princeton.edu
Steve RathjePostdoctoral Researcher, New York UniversityVerified email at alumni.stanford.edu
Jay Van BavelProfessor of Psychology & Neural Science, New York UniversityVerified email at nyu.edu
Vihari PiratlaPostdoc, Cambridge UniversityVerified email at cam.ac.uk
Bradley C. LoveProfessor of Cognitive and Decision Sciences, University College LondonVerified email at ucl.ac.uk
Weiyang LiuUniversity of Cambridge & Max Planck Institute for Intelligent SystemsVerified email at cam.ac.uk
Apurva NarayanWestern University, University of British Columbia and University of WaterlooVerified email at uwo.ca
Mateo Espinosa ZarlengaUniversity of CambridgeVerified email at cam.ac.uk
Matthew BarkerStudent at University of CambridgeVerified email at cam.ac.uk

Ilia Sucholutsky

Princeton University

Verified email at princeton.edu - Homepage

deep learning machine learning representation learning AI alignment small data


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Soft-Label Dataset Distillation and Text Dataset Distillation I Sucholutsky, M Schonlau 2021 International Joint Conference on Neural Networks, 2019	98	2019
Text mining with n-gram variables M Schonlau, N Guenther, I Sucholutsky The Stata Journal 17 (4), 866-881, 2017	77*	2017
GPT is an effective tool for multilingual psychological text analysis S Rathje, DM Mirea, I Sucholutsky, R Marjieh, C Robertson, JJ Van Bavel PsyArXiv, 2023	60	2023
'Less Than One'-Shot Learning: Learning N Classes From M< N Samples I Sucholutsky, M Schonlau Proceedings of the AAAI Conference on Artificial Intelligence 35 (11), 9739-9746, 2021	54	2021
Words are all you need? Language as an approximation for human similarity judgments R Marjieh, P Van Rijn, I Sucholutsky, TR Sumers, H Lee, TL Griffiths, ... arXiv preprint arXiv:2206.04105, 2022	21*	2022
Human uncertainty in concept-based ai systems KM Collins, M Barker, M Espinosa Zarlenga, N Raman, U Bhatt, M Jamnik, ... Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 869-889, 2023	18	2023
Getting aligned on representational alignment I Sucholutsky, L Muttenthaler, A Weller, A Peng, A Bobu, B Kim, BC Love, ... arXiv preprint arXiv:2310.13018, 2023	17	2023
What language reveals about perception: Distilling psychophysical knowledge from large language models R Marjieh, I Sucholutsky, P van Rijn, N Jacoby, T Griffiths Proceedings of the Annual Meeting of the Cognitive Science Society 45 (45), 2023	17	2023
Alignment with human representations supports robust few-shot learning I Sucholutsky, T Griffiths Advances in Neural Information Processing Systems 36, 2024	16	2024
Secdd: Efficient and secure method for remotely training neural networks (student abstract) I Sucholutsky, M Schonlau Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 15897 …, 2021	14	2021
Predicting human similarity judgments using large language models R Marjieh, I Sucholutsky, TR Sumers, N Jacoby, TL Griffiths arXiv preprint arXiv:2202.04728, 2022	13	2022
Large language models meet cognitive science: Llms as tools, models, and participants M Hardy, I Sucholutsky, B Thompson, T Griffiths Proceedings of the annual meeting of the cognitive science society 45 (45), 2023	10	2023
Deep Learning for System Trace Restoration I Sucholutsky, A Narayan, M Schonlau, S Fischmeister 2019 International Joint Conference on Neural Networks (IJCNN), 2019	9	2019
On the informativeness of supervision signals I Sucholutsky, RM Battleday, KM Collins, R Marjieh, J Peterson, P Singh, ... Uncertainty in Artificial Intelligence, 2036-2046, 2023	8	2023
Human-in-the-Loop Mixup KM Collins, U Bhatt, W Liu, V Piratla, I Sucholutsky, B Love, A Weller Uncertainty in Artificial Intelligence, 2023, 2022	8*	2022
Large language models predict human sensory judgments across six modalities R Marjieh, I Sucholutsky, P van Rijn, N Jacoby, TL Griffiths arXiv preprint arXiv:2302.01308, 2023	5	2023
Can Humans Do Less-Than-One-Shot Learning? M Malaviya, I Sucholutsky, K Oktar, TL Griffiths arXiv preprint arXiv:2202.04670, 2022	5	2022
One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes I Sucholutsky, NH Kim, RP Browne, M Schonlau 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021	5	2021
Concept alignment as a prerequisite for value alignment S Rane, M Ho, I Sucholutsky, TL Griffiths arXiv preprint arXiv:2310.20059, 2023	4	2023
Pay attention and you won’t lose it: a deep learning approach to sequence imputation I Sucholutsky, A Narayan, M Schonlau, S Fischmeister PeerJ Computer Science 5 (e210), 2019	4	2019

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