Stanisław Jastrzębski
Stanisław Jastrzębski
CSO @ Molecule.one & Assistant Prof. @ Jagiellonian University (GMUM.net)
Zweryfikowany adres z nyu.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
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
594*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
2102017
Parameter-Efficient Transfer Learning for NLP
N Houlsby, A Giurgiu*, S Jastrzebski*, B Morrone, Q Laroussilhe, ...
International Conference on Machine Learning (ICML) 2019, 2019
1602019
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, ...
1332019
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
632017
Learning to SMILE(S)
S Jastrzebski, D Lesniak, WM Czarnecki
International Conference on Learning Representation 2016 (Workshop track), 2016
56*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
542017
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
512017
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
40*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
322016
Stiffness: A new perspective on generalization in neural networks
S Fort, PK Nowak, S Jastrzebski, S Narayanan
arXiv preprint arXiv:1901.09491, 2019
272019
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
262020
Molecule Attention Transformer
Ł Maziarka, T Danel, S Mucha, K Rataj, J Tabor, S Jastrzębski
NeurIPS 2019 workshop; arXiv preprint arXiv:2002.08264, 2020
252020
Large Scale Structure of Neural Network Loss Landscapes
S Fort, S Jastrzebski
NeurIPS 2019, 2019
212019
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
202020
Evolutionary-Neural Hybrid Agents for Architecture Search
K Maziarz, A Khorlin, Q de Laroussilhe, S Jastrzebski, T Mingxing, ...
arXiv preprint arXiv:1811.09828, 2018
19*2018
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
182018
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
122018
Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits
M Sacha, P Błaż, Mikołaj, Byrski, P Włodarczyk-Pruszyński, S Jastrzębski
arXiv, 2020
62020
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
62019
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