Stanisław Jastrzębski

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Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Devansh ArpitSenior Research Scientist, SalesforceVerified email at salesforce.com
Nicolas BallasFacebook AI ResearchVerified email at fb.com
Asja FischerProfessor for Machine Learning, Ruhr University BochumVerified email at ini.rub.de
Amos StorkeyProfessor of Machine Learning and AI, School of Informatics, University of Edinburgh, UKVerified email at ed.ac.uk
Maxinder S. KanwalPhD Student, Stanford UniversityVerified email at stanford.edu
Jacek TaborProfesor informatyki, Uniwersytet JagiellońskiVerified email at ii.uj.edu.pl
Emmanuel BengioMcGill UniversityVerified email at mail.mcgill.ca
David Scott KruegerPhD Student, University of MontrealVerified email at umontreal.ca
Tegan MaharajPhD student, MILA (Polytechnique Montreal)Verified email at polymtl.ca
Zachary KentonDeepMindVerified email at google.com
Aaron CourvilleUniversité de MontréalVerified email at umontreal.ca
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / MilaVerified email at iro.umontreal.ca
Andrea GesmundoGoogleVerified email at google.com
Wojciech Marian CzarneckiResearch Scientist at DeepMindVerified email at google.com
Krzysztof J. GerasNew York UniversityVerified email at nyu.edu
Neil HoulsbyGoogleVerified email at google.com
Quentin de LaroussilheGoogleVerified email at google.com
Kyunghyun ChoNew York UniversityVerified email at nyu.edu
Stanislav FortGraduate student, Stanford University / Google Brain / DeepMindVerified email at stanford.edu

Stanisław Jastrzębski

CSO @ Molecule.one & Assistant Prof. @ Jagiellonian University (GMUM.net)

Verified email at molecule.one - Homepage

Deep Learning Drug Discovery Optimization


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A Closer Look at Memorization in Deep Networks D Arpit, S Jastrzebski, N Ballas, D Krueger, E Bengio, MS Kanwal, ... International Conference on Machine Learning 2017, 2017	676	2017
Three factors influencing minima in SGD S Jastrzebski, Z Kenton, D Arpit, N Ballas, A Fischer, Y Bengio, ... International Conference on Artificial Neural Networks 2018; International …, 2017	232	2017
Parameter-Efficient Transfer Learning for NLP N Houlsby, A Giurgiu, S Jastrzebski, B Morrone, Q Laroussilhe, ... International Conference on Machine Learning (ICML) 2019, 2019	188	2019
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening N Wu, J Phang, J Park, Y Shen, Z Huang, M Zorin, S Jastrzebski, T Févry, ...	159	2019
Residual connections encourage iterative inference S Jastrzebski, D Arpit, N Ballas, V Verma, T Che, Y Bengio International Conference on Learning Algorithms (ICLR) 2018, 2017	62	2017
Learning to SMILE(S) S Jastrzebski, D Lesniak, WM Czarnecki International Conference on Learning Representation 2016 (Workshop track), 2016	58*	2016
Learning to Compute Word Embeddings on the Fly D Bahdanau, T Bosc, S Jastrzebski, E Grefenstette, P Vincent, Y Bengio Montreal AI Symposium 2017, 2017	57	2017
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks S Jastrzebski, D Leśniak, WM Czarnecki arXiv preprint arXiv:1702.02170, 2017	53	2017
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length S Jastrzębski, Z Kenton, N Ballas, A Fischer, Y Bengio, A Storkey International Conference on Learning Algorithms (ICLR) 2019, 2019	48*	2019
The Break-Even Point on Optimization Trajectories of Deep Neural Networks S Jastrzebski, M Szymczak, S Fort, D Arpit, J Tabor, K Cho, K Geras International Conference on Learning Algorithms (ICLR) 2020, 2020	40	2020
Evolutionary-Neural Hybrid Agents for Architecture Search K Maziarz, A Khorlin, Q de Laroussilhe, S Jastrzebski, T Mingxing, ... arXiv preprint arXiv:1811.09828, 2018	39*	2018
Stiffness: A new perspective on generalization in neural networks S Fort, PK Nowak, S Jastrzebski, S Narayanan arXiv preprint arXiv:1901.09491, 2019	34	2019
Osprey: Hyperparameter optimization for machine learning R McGibbon, C Hernández, M Harrigan, S Kearnes, M Sultan, ... Journal of Open Source Software 1 (5), 34, 2016	34	2016
Molecule Attention Transformer Ł Maziarka, T Danel, S Mucha, K Rataj, J Tabor, S Jastrzębski NeurIPS 2019 workshop; arXiv preprint arXiv:2002.08264, 2020	31	2020
Large Scale Structure of Neural Network Loss Landscapes S Fort, S Jastrzebski NeurIPS 2019, 2019	28	2019
Cramer-Wold Auto-Encoder S Knop, P Spurek, J Tabor, I Podolak, M Mazur, S Jastrzębski Journal of Machine Learning Research 21 (164), 1-28, 2020	22*	2020
Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function W Tarnowski, P Warchoł, S Jastrzebski, J Tabor, M Nowak AISTATS 2019, 2018	20	2018
Commonsense mining as knowledge base completion? A study on the impact of novelty S Jastrzebski, D Bahdanau, S Hosseini, M Noukhovitch, Y Bengio, ... New Forms of Generalization in Deep Learning and Natural Language Processing …, 2018	14	2018
We Should at Least Be Able to Design Molecules That Dock Well T Cieplinski, T Danel, S Podlewska, S Jastrzebski arXiv preprint arXiv:2006.16955, 2020	10	2020
Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits M Sacha, P Błaż, Mikołaj, Byrski, P Włodarczyk-Pruszyński, S Jastrzębski Journal of Cheminformatics and Modeling (JCIM), 2020	8	2020

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