I am a final year PhD student @ Jagiellonian University supervised by Prof. Jacek Tabor and co-supervised
by Prof. Amos Storkey (University of Edinburgh). Currently at Google AI (Brain) training neural networks which train neural networks. Before that I spent two summers as a visiting student with
Prof. Yoshua Bengio. I also completed industrial internships with Palantir and Microsoft.
My email is staszek.jastrzebski (on gmail).
New: papers accepted to ICLR 2019, and AISTATS 2019! Also, our preprint on Neural Architecture Search is online.
My main research goal is to understand how deep network generalize. My research interests include:
- Optimization in Deep Learning
- Representation Learning
- Natural Language Processing
- Computer Aided Drug Design
Co-supervised MSc/BSc students:
- Michał Zmysłowski (UW) - Gradient structure and generalization
- Sławomir Mucha - Towards the ImageNet of virtual screening
- [Defended, MSc] Tomasz Wesołowski - Relevance of enriching word embeddings in modern deep natural language processing
- [Defended, MSc] Andrii Krutsylo - Physics aware representation for drug discovery
- [Defended, BSc] Michał Soboszek - Evaluating word embeddings
- [Defended, MSc] Jakub Chłędowski - Representation learning for textual entailment
- [Defended, MSc] Mikołaj Sacha - Meta learning and sharpness of the minima
For a full list please see my Google Scholar profile.
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 Representations 2019
Three factors influencing minima in SGD
S. Jastrzębski*, Z. Kenton*, D. Arpit, N. Ballas, A. Fischer, Y. Bengio, A. Storkey
International Conference on Artificial Neural Networks 2018 (oral), International Conference on Learning Representations 2018 (workshop)
Residual Connections Encourage Iterative Inference
S. Jastrzębski*, D. Arpit*, N. Ballas, V. Verma, T. Che, Y. Bengio
International Conference on Learning Representations 2018
A Closer Look at Memorization in Deep Networks
D. Arpit*, S. Jastrzębski*, N. Ballas*, D. Krueger*, T. Maharaj, E. Bengio, A. Fischer, A. Courville, S. Lacoste-Julien, Y. Bengio
International Conference on Machine Learning 2017
Quo vadis G Protein-Coupled Receptor ligands? A tool for analysis of the emergence of new groups of compounds over time
D. Lesniak, S. Jastrzębski, W. M. Czarnecki, S. Podlewska, A. Bojarski
Bioorganic & Medicinal Chemistry Letters, 2017
Learning to SMILE(S)
S. Jastrzębski, D. Lesniak, W. M. Czarnecki
International Conference on Learning Representations 2016 (workshop track)
Research intern, automatic machine learning
11.2018-, Zurich, Switzerland
Machine Learning intern, fraud detection models and Deep NLP applications
7-9.2016, London, UK
University of Edinburgh
Research intern, Deep Learning for Go, under the supervision of prof. Amos Storkey
9-11.2015, Edinburgh, UK
SDE intern, distributed data systems
2015, Palo Alto, USA
SDE intern, API research and design
2014, Redmond, USA
SDE intern, data processing framework development
2013, London, UK
Word Embeddings Benchmarks
Python package for evaluating word embeddings.
ML algorithms C++ implementations for R, including online clustering, swappable SVM library interface and new clustering algorithm.
Simulator for 2015 and 2016 editions online qualification round.
Understanding How Deep Networks Learn
Invited talk @ PL in ML
2017, Warsaw, Poland