| 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 | 209* | 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 | 80 | 2017 |
| 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 | 30 | 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 | 30 | 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 | 28 | 2017 |
| Learning to SMILE(S) S Jastrzebski, D Lesniak, WM Czarnecki International Conference on Learning Representation 2016 (Workshop track), 2016 | 26* | 2016 |
| Osprey: Hyperparameter Optimization for Machine Learning. RT McGibbon, CX Hernández, MP Harrigan, SM Kearnes, MM Sultan, ... J. Open Source Software 1 (5), 34, 2016 | 20 | 2016 |
| Cramer-Wold AutoEncoder J Tabor, S Knop, P Spurek, I Podolak, M Mazur, S Jastrzebski arXiv preprint arXiv:1805.09235, 2018 | 11 | 2018 |
| Parameter-Efficient Transfer Learning for NLP N Houlsby, A Giurgiu*, S Jastrzebski*, B Morrone, Q Laroussilhe, ... International Conference on Machine Learning (ICML) 2019, 2019 | 9 | 2019 |
| On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length S Jastrzebski, Z Kenton, N Ballas, A Fischer, Y Bengio, A Storkey International Conference on Learning Algorithms (ICLR) 2019, 2018 | 9* | 2018 |
| 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, ... | 7 | 2019 |
| Stiffness: A new perspective on generalization in neural networks S Fort, PK Nowak, S Jastrzebski, Stanislaw, Narayanan arXiv preprint arXiv:1901.09491, 2019 | 4 | 2019 |
| 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 | 4 | 2018 |
| Density Invariant Detection of Osteoporosis Using Growing Neural Gas IT Podolak, SK Jastrzebski Proceedings of the 8th International Conference on Computer Recognition …, 2013 | 4 | 2013 |
| Evolutionary-Neural Hybrid Agents for Architecture Search K Maziarz, A Khorlin, Q de Laroussilhe, S Jastrzebski, T Mingxing, ... arXiv preprint arXiv:1811.09828, 2018 | 3 | 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 | 3 | 2018 |
| Quo vadis G Protein-Coupled Receptor ligands? A tool for analysis of the emergence of new groups of compounds over time AJB Damian Lesniak, Stanislaw Jastrzebski, Sabina Podlewska, Wojciech M ... Bioorganic & Medicinal Chemistry Letters, 2016 | 3* | 2016 |
| Three-dimensional descriptors for aminergic GPCRs: dependence on docking conformation and crystal structure S Jastrzebski, I Sieradzki, D Leśniak, J Tabor, AJ Bojarski, S Podlewska Molecular Diversity, 1-11, 2018 | 2 | 2018 |
| Analysis of compounds activity concept learned by SVM using robust Jaccard based low-dimensional embedding S Jastrzebski, WM Czarnecki Schedae Informaticae 24, 9-19, 2016 | 2 | 2016 |
| Development of new methods needs proper evaluation – benchmarking sets for machine learning experiments for class A GPCRs D Leśniak, S Podlewska, S Jastrzebski, I Sieradzki, A Bojarski, J Tabor J. Chem. Inf. Model, 2019 | | 2019 |
Yoshua BengioProfessor, University of Montreal (Computer Sc. & Op. Res.), Mila, CIFAR, CRM, IVADO, REPARTI, GRSNCVerified email at mila.quebec
