Stanisław Jastrzębski
Stanisław Jastrzębski
Postdoctoral Fellow, New York University
Zweryfikowany adres z - Strona główna
TytułCytowane przezRok
A Closer Look at Memorization in Deep Networks
D Arpit*, S Jastrzębski*, N Ballas*, D Krueger*, E Bengio, MS Kanwal, ...
International Conference on Machine Learning 2017, 2017
Three factors influencing minima in SGD
S Jastrzębski*, Z Kenton*, D Arpit, N Ballas, A Fischer, Y Bengio, ...
International Conference on Artificial Neural Networks 2018; International …, 2017
Residual connections encourage iterative inference
S Jastrzębski*, D Arpit*, N Ballas, V Verma, T Che, Y Bengio
International Conference on Learning Algorithms (ICLR) 2018, 2017
Learning to SMILE(S)
S Jastrzębski, D Leśniak, WM Czarnecki
International Conference on Learning Representation 2016 (Workshop track), 2016
Learning to Compute Word Embeddings on the Fly
D Bahdanau, T Bosc*, S Jastrzębski*, E Grefenstette, P Vincent, Y Bengio
Montreal AI Symposium 2017, 2017
Osprey: Hyperparameter optimization for machine learning
RT McGibbon, CX Hernández, MP Harrigan, S Kearnes, MM Sultan, ...
J. Open Source Software 1 (34.10), 21105, 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
Cramer-Wold AutoEncoder
J Tabor, S Knop, P Spurek, I Podolak, M Mazur, S Jastrzębski
arXiv preprint arXiv:1805.09235, 2018
Density Invariant Detection of Osteoporosis Using Growing Neural Gas
IT Podolak, SK Jastrzębski
Proceedings of the 8th International Conference on Computer Recognition …, 2013
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, 2018
Quo vadis G Protein-Coupled Receptor ligands? A tool for analysis of the emergence of new groups of compounds over time
AJB Damian Leśniak, Stanisław Jastrzębski, Sabina Podlewska, Wojciech M ...
Bioorganic & Medicinal Chemistry Letters, 2016
Analysis of compounds activity concept learned by SVM using robust Jaccard based low-dimensional embedding
S Jastrzębski, WM Czarnecki
Schedae Informaticae 24, 9-19, 2016
Evolutionary-Neural Hybrid Agents for Architecture Search
K Maziarz, A Khorlin, Q de Laroussilhe, S Jastrzebski, T Mingxing, ...
arXiv preprint arXiv:1811.09828, 2018
Commonsense mining as knowledge base completion? A study on the impact of novelty
S Jastrzębski, D Bahdanau, S Hosseini, M Noukhovitch, Y Bengio, ...
New Forms of Generalization in Deep Learning and Natural Language Processing …, 2018
Improving Utilization of Lexical Knowledge in Natural Language Inference
J Chłędowski, T Wesołowski, S Jastrzębski
Schedae Informaticae 27, 2019
Split Batch Normalization: Improving Semi-Supervised Learning under Domain Shift
M Zając, K Żołna, S Jastrzębski
arXiv preprint arXiv:1904.03515, 2019
Deep networks generalization and optimization trajectory
S Jastrzębski
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, ...
arXiv preprint arXiv:1903.08297, 2019
Non-linear ICA based on Cramer-Wold metric
P Spurek, A Nowak, J Tabor, Ł Maziarka, S Jastrzębski
arXiv preprint arXiv:1903.00201, 2019
Parameter-Efficient Transfer Learning for NLP
N Houlsby, A Giurgiu*, S Jastrzębski*, B Morrone, Q Laroussilhe, ...
International Conference on Machine Learning (ICML) 2019, 2019
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