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
Jacek TaborProfesor informatyki, Uniwersytet JagiellońskiVerified email at ii.uj.edu.pl
Andrea GesmundoGoogleVerified email at google.com
Amos StorkeyProfessor of Machine Learning and AI, School of Informatics, University of Edinburgh, UKVerified email at ed.ac.uk
Krzysztof J. GerasNew York UniversityVerified email at nyu.edu
Emmanuel BengioMcGill UniversityVerified email at mail.mcgill.ca
David Scott KruegerUniversity Assistant Professor, University of CambridgeVerified email at cam.ac.uk
Tegan MaharajAssistant Professor at University of TorontoVerified email at polymtl.ca
Maxinder S. KanwalPhD Student, Stanford UniversityVerified email at stanford.edu
Zachary KentonDeepMindVerified email at google.com
Neil HoulsbyGoogleVerified email at google.com
Quentin de LaroussilheGoogleVerified email at google.com
Aaron CourvilleAssociate Professor, DIRO, Université de Montréal, Mila, Cifar CAI chairVerified email at umontreal.ca
Simon Lacoste-JulienAssociate Professor - Canada CIFAR AI Chair, University of Montreal / MilaVerified email at iro.umontreal.ca
Kyunghyun ChoNew York University, GenentechVerified email at nyu.edu
Wojciech Marian CzarneckiIsomorphic LabsVerified email at google.com
Tomasz DanelJagiellonian UniversityVerified email at ii.uj.edu.pl

Stanisław Jastrzębski

CTO 👨💻 & CSO 🧪 @ Molecule.One

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	1002	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	553	2019
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	326	2017
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, ...	281	2019
Learning to SMILE(S) S Jastrzebski, D Lesniak, WM Czarnecki International Conference on Learning Representation 2016 (Workshop track), 2016	76*	2016
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	74	2017
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	74	2017
Molecule Attention Transformer Ł Maziarka, T Danel, S Mucha, K Rataj, J Tabor, S Jastrzębski NeurIPS 2019 workshop; arXiv preprint arXiv:2002.08264, 2020	64	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	63*	2018
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	62	2020
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	62*	2019
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	60	2017
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department FE Shamout, Y Shen, N Wu, A Kaku, J Park, T Makino, S Jastrzębski, ... NPJ digital medicine 4 (1), 1-11, 2021	53	2021
Stiffness: A new perspective on generalization in neural networks S Fort, PK Nowak, S Jastrzebski, S Narayanan arXiv preprint arXiv:1901.09491, 2019	51	2019
Large Scale Structure of Neural Network Loss Landscapes S Fort, S Jastrzebski NeurIPS 2019, 2019	44	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	40	2016
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	34*	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	33	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	30	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	20	2020

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