Density Invariant Detection of Osteoporosis Using Growing Neural Gas. - Igor T. Podolak; Stanislaw K. Jastrzebski (CORES, 2013)

Learning to SMILE(S). - Stanislaw Jastrzebski; Damian Lesniak; Wojciech Marian Czarnecki (CoRR, 2016)

Osprey: Hyperparameter Optimization for Machine Learning. - Robert T. McGibbon; Carlos X. Hernández; Matthew P. Harrigan; Steven Kearnes; Mohammad M. Sultan; Stanislaw Jastrzebski; Brooke E. Husic; Vijay S. Pande (J. Open Source Softw., 2016)

Deep Nets Don't Learn via Memorization. - David Krueger 001; Nicolas Ballas; Stanislaw Jastrzebski; Devansh Arpit; Maxinder S. Kanwal; Tegan Maharaj; Emmanuel Bengio; Asja Fischer; Aaron C. Courville (ICLR (Workshop), 2017)

A Closer Look at Memorization in Deep Networks. - Devansh Arpit; Stanislaw Jastrzebski; Nicolas Ballas; David Krueger 001; Emmanuel Bengio; Maxinder S. Kanwal; Tegan Maharaj; Asja Fischer; Aaron C. Courville; Yoshua Bengio; Simon Lacoste-Julien (ICML, 2017)

Learning to Compute Word Embeddings On the Fly. - Dzmitry Bahdanau; Tom Bosc; Stanislaw Jastrzebski; Edward Grefenstette; Pascal Vincent; Yoshua Bengio (CoRR, 2017)

How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks. - Stanislaw Jastrzebski; Damian Lesniak; Wojciech Marian Czarnecki (CoRR, 2017)

Three Factors Influencing Minima in SGD. - Stanislaw Jastrzebski; Zachary Kenton; Devansh Arpit; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (CoRR, 2017)

Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio. - Stanislaw Jastrzebski; Zachary Kenton; Devansh Arpit; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (ICANN (3), 2018)

Residual Connections Encourage Iterative Inference. - Stanislaw Jastrzebski; Devansh Arpit; Nicolas Ballas; Vikas Verma; Tong Che; Yoshua Bengio (ICLR (Poster), 2018)

Finding Flatter Minima with SGD. - Stanislaw Jastrzebski; Zachary Kenton; Devansh Arpit; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (ICLR (Workshop), 2018)

Commonsense mining as knowledge base completion? A study on the impact of novelty. - Stanislaw Jastrzebski; Dzmitry Bahdanau; Seyedarian Hosseini; Michael Noukhovitch; Yoshua Bengio; Jackie Chi Kit Cheung (CoRR, 2018)

Cramer-Wold AutoEncoder. - Jacek Tabor; Szymon Knop; Przemyslaw Spurek; Igor T. Podolak; Marcin Mazur; Stanislaw Jastrzebski (CoRR, 2018)

DNN's Sharpest Directions Along the SGD Trajectory. - Stanislaw Jastrzebski; Zachary Kenton; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (CoRR, 2018)

Neural Architecture Search Over a Graph Search Space. - Quentin de Laroussilhe; Stanislaw Jastrzebski; Neil Houlsby; Andrea Gesmundo (CoRR, 2018)

Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function. - Wojciech Tarnowski; Piotr Warchol; Stanislaw Jastrzebski; Jacek Tabor; Maciej A. Nowak (AISTATS, 2019)

On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length. - Stanislaw Jastrzebski; Zachary Kenton; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (ICLR (Poster), 2019)

Parameter-Efficient Transfer Learning for NLP. - Neil Houlsby; Andrei Giurgiu; Stanislaw Jastrzebski; Bruna Morrone; Quentin de Laroussilhe; Andrea Gesmundo; Mona Attariyan; Sylvain Gelly (ICML, 2019)

Large Scale Structure of Neural Network Loss Landscapes. - Stanislav Fort; Stanislaw Jastrzebski (NeurIPS, 2019)

Split Batch Normalization: Improving Semi-Supervised Learning under Domain Shift. - Michal Zajac 005; Konrad Zolna; Stanislaw Jastrzebski (CoRR, 2019)

Development of New Methods Needs Proper Evaluation - Benchmarking Sets for Machine Learning Experiments for Class A GPCRs. - Damian Lesniak; Sabina Podlewska; Stanislaw Jastrzebski; Igor Sieradzki; Andrzej J. Bojarski; Jacek Tabor (J. Chem. Inf. Model., 2019)

The Break-Even Point on Optimization Trajectories of Deep Neural Networks. - Stanislaw Jastrzebski; Maciej Szymczak; Stanislav Fort; Devansh Arpit; Jacek Tabor; Kyunghyun Cho; Krzysztof J. Geras (ICLR, 2020)

Non-linear ICA Based on Cramer-Wold Metric. - Przemyslaw Spurek; Aleksandra Nowak 001; Jacek Tabor; Lukasz Maziarka; Stanislaw Jastrzebski (ICONIP (3), 2020)

Improving the Ability of Deep Neural Networks to Use Information from Multiple Views in Breast Cancer Screening. - Nan Wu; Stanislaw Jastrzebski; Jungkyu Park 001; Linda Moy; Kyunghyun Cho; Krzysztof J. Geras (MIDL, 2020)

Molecule Attention Transformer. - Lukasz Maziarka; Tomasz Danel; Slawomir Mucha; Krzysztof Rataj; Jacek Tabor; Stanislaw Jastrzebski (CoRR, 2020)

Understanding the robustness of deep neural network classifiers for breast cancer screening. - Witold Oleszkiewicz; Taro Makino; Stanislaw Jastrzebski; Tomasz Trzcinski; Linda Moy; Kyunghyun Cho; Laura Heacock; Krzysztof J. Geras (CoRR, 2020)

Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits. - Mikolaj Sacha; Mikolaj Blaz; Piotr Byrski; Pawel Wlodarczyk-Pruszynski; Stanislaw Jastrzebski (CoRR, 2020)

We should at least be able to Design Molecules that Dock Well. - Tobiasz Cieplinski; Tomasz Danel; Sabina Podlewska; Stanislaw Jastrzebski (CoRR, 2020)

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. - Farah E. Shamout; Yiqiu Shen; Nan Wu; Aakash Kaku; Jungkyu Park 001; Taro Makino; Stanislaw Jastrzebski; Duo Wang; Ben Zhang; Siddhant Dogra; Meng Cao; Narges Razavian; et al. (CoRR, 2020)

Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks. - Luke Nicholas Darlow; Stanislaw Jastrzebski; Amos J. Storkey (CoRR, 2020)

RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design. - Cheng-Hao Liu; Maksym Korablyov; Stanislaw Jastrzebski; Pawel Wlodarczyk-Pruszynski; Yoshua Bengio; Marwin H. S. Segler (CoRR, 2020)

Differences between human and machine perception in medical diagnosis. - Taro Makino; Stanislaw Jastrzebski; Witold Oleszkiewicz; Celin Chacko; Robin Ehrenpreis; Naziya Samreen; Chloe Chhor; Eric Kim; Jiyon Lee; Kristine Pysarenko; Beatriu Reig; Hildegard Toth; et al. (CoRR, 2020)

Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening. - Stanislaw Jastrzebski; Maciej Szymczak; Agnieszka Pocha; Stefan Mordalski; Jacek Tabor; Andrzej J. Bojarski; Sabina Podlewska (J. Chem. Inf. Model., 2020)

Cramer-Wold Auto-Encoder. - Szymon Knop; Przemyslaw Spurek; Jacek Tabor; Igor T. Podolak; Marcin Mazur; Stanislaw Jastrzebski (J. Mach. Learn. Res., 2020)

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening. - Nan Wu; Jason Phang; Jungkyu Park 001; Yiqiu Shen; Zhe Huang 005; Masha Zorin; Stanislaw Jastrzebski; Thibault Févry; Joe Katsnelson; Eric Kim; Stacey Wolfson; Ujas Parikh; et al. (IEEE Trans. Medical Imaging, 2020)

Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization. - Stanislaw Jastrzebski; Devansh Arpit; Oliver Åstrand; Giancarlo Kerg; Huan Wang 014; Caiming Xiong; Richard Socher; Kyunghyun Cho; Krzysztof J. Geras (ICML, 2021)

Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits. - Mikolaj Sacha; Mikolaj Blaz; Piotr Byrski; Pawel Dabrowski-Tumanski; Mikolaj Chrominski; Rafal Loska; Pawel Wlodarczyk-Pruszynski; Stanislaw Jastrzebski (J. Chem. Inf. Model., 2021)

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. - Farah E. Shamout; Yiqiu Shen; Nan Wu; Aakash Kaku; Jungkyu Park 001; Taro Makino; Stanislaw Jastrzebski; Jan Witowski; Duo Wang; Ben Zhang; Siddhant Dogra; Meng Cao; et al. (npj Digit. Medicine, 2021)

HuggingMolecules: An Open-Source Library for Transformer-Based Molecular Property Prediction (Student Abstract). - Piotr Gainski; Lukasz Maziarka; Tomasz Danel; Stanislaw Jastrzebski (AAAI, 2022)

Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks. - Nan Wu; Stanislaw Jastrzebski; Kyunghyun Cho; Krzysztof J. Geras (ICML, 2022)

3D-GMIC: an efficient deep neural network to find small objects in large 3D images. - Jungkyu Park 001; Jakub Chledowski; Stanislaw Jastrzebski; Jan Witowski; Yanqi Xu; Linda Du; Sushma Gaddam; Eric Kim; Alana Lewin; Ujas Parikh; Anastasia Plaunova; Sardius Chen; et al. (CoRR, 2022)

RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software. - Cheng-Hao Liu; Maksym Korablyov; Stanislaw Jastrzebski; Pawel Wlodarczyk-Pruszynski; Yoshua Bengio; Marwin H. S. Segler (J. Chem. Inf. Model., 2022)

Molecule-Edit Templates for Efficient and Accurate Retrosynthesis Prediction. - Mikolaj Sacha; Michal Sadowski; Piotr Kozakowski; Ruard van Workum; Stanislaw Jastrzebski (CoRR, 2023)

Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark. - Tobiasz Cieplinski; Tomasz Danel; Sabina Podlewska; Stanislaw Jastrzebski (J. Chem. Inf. Model., 2023)

Generative Active Learning for the Search of Small-molecule Protein Binders. - Maksym Korablyov; Cheng-Hao Liu; Moksh Jain; Almer M. van der Sloot; Eric Jolicoeur; Edward Ruediger; Andrei Cristian Nica; Emmanuel Bengio; Kostiantyn Lapchevskyi; Daniel St-Cyr; Doris Alexandra Schuetz; Victor Ion Butoi; et al. (CoRR, 2024)

An Efficient Deep Neural Network to Classify Large 3D Images With Small Objects. - Jungkyu Park 001; Jakub Chledowski; Stanislaw Jastrzebski; Jan Witowski; Yanqi Xu; Linda Du; Sushma Gaddam; Eric Kim; Alana Lewin; Ujas Parikh; Anastasia Plaunova; Sardius Chen; et al. (IEEE Trans. Medical Imaging, 2024)

Relative molecule self-attention transformer. - Lukasz Maziarka; Dawid Majchrowski; Tomasz Danel; Piotr Gainski; Jacek Tabor; Igor T. Podolak; Pawel M. Morkisz; Stanislaw Jastrzebski (J. Cheminformatics, 2024)