Stanisław Jastrzębski
Stanisław Jastrzębski
Serving as the Chief Scientist at Molecule.one & Assistant Prof. at 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
504*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
1922017
Parameter-Efficient Transfer Learning for NLP
N Houlsby, A Giurgiu*, S Jastrzebski*, B Morrone, Q Laroussilhe, ...
International Conference on Machine Learning (ICML) 2019, 2019
1132019
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, ...
872019
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
562017
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
512017
Learning to SMILE(S)
S Jastrzebski, D Lesniak, WM Czarnecki
International Conference on Learning Representation 2016 (Workshop track), 2016
51*2016
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
502017
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
35*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
282016
Stiffness: A new perspective on generalization in neural networks
S Fort, PK Nowak, S Jastrzebski, S Narayanan
arXiv preprint arXiv:1901.09491, 2019
252019
Evolutionary-Neural Hybrid Agents for Architecture Search
K Maziarz, A Khorlin, Q de Laroussilhe, S Jastrzebski, T Mingxing, ...
arXiv preprint arXiv:1811.09828, 2018
24*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
182020
Molecule Attention Transformer
Ł Maziarka, T Danel, S Mucha, K Rataj, J Tabor, S Jastrzębski
arXiv preprint arXiv:2002.08264, 2020
182020
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
182020
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
162018
Large Scale Structure of Neural Network Loss Landscapes
S Fort, S Jastrzebski
NeurIPS 2019, 2019
132019
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
102018
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
52019
Density Invariant Detection of Osteoporosis Using Growing Neural Gas
IT Podolak, SK Jastrzebski
Proceedings of the 8th International Conference on Computer Recognition …, 2013
52013
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Prace 1–20