no code implementations • 15 Apr 2024 • Michał Koziarski, Mohammed Abukalam, Vedant Shah, Louis Vaillancourt, Doris Alexandra Schuetz, Moksh Jain, Almer van der Sloot, Mathieu Bourgey, Anne Marinier, Yoshua Bengio
DNA-encoded libraries (DELs) are a powerful approach for rapidly screening large numbers of diverse compounds.
no code implementations • 20 Oct 2023 • Alexandra Volokhova, Michał Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Alán Aspuru-Guzik, Yoshua Bengio
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule.
1 code implementation • 7 Oct 2023 • Mila AI4Science, Alex Hernandez-Garcia, Alexandre Duval, Alexandra Volokhova, Yoshua Bengio, Divya Sharma, Pierre Luc Carrier, Yasmine Benabed, Michał Koziarski, Victor Schmidt
Accelerating material discovery holds the potential to greatly help mitigate the climate crisis.
1 code implementation • 6 Oct 2023 • Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michał Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew Fitzgibbon, Błażej Banaszewski, Chad Martin, Dominic Masters
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
1 code implementation • 5 Jul 2023 • Piotr Gaiński, Michał Koziarski, Jacek Tabor, Marek Śmieja
Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics.
no code implementations • 28 Nov 2021 • Michał Koziarski
The focus of this thesis is development of novel data resampling strategies natively utilizing the information about the distribution of both minority and majority class.
1 code implementation • 9 May 2021 • Michał Koziarski, Colin Bellinger, Michał Woźniak
Our $5\times2$ cross-validated results on 57 benchmark binary datasets with 9 classifiers show that RB-CCR achieves a better precision-recall trade-off than CCR and generally out-performs the state-of-the-art resampling methods in terms of AUC and G-mean.
1 code implementation • 9 May 2021 • Michał Koziarski, Bogusław Cyganek, Bogusław Olborski, Zbigniew Antosz, Marcin Żydak, Bogdan Kwolek, Paweł Wąsowicz, Andrzej Bukała, Jakub Swadźba, Piotr Sitkowski
Cancer diseases constitute one of the most significant societal challenges.
1 code implementation • 17 Apr 2021 • Michał Koziarski
The results of the experiments conducted on 60 imbalanced datasets show outperformance of Potential Anchoring over state-of-the-art resampling algorithms, including previously proposed methods that utilize radial basis functions to model class potential.
no code implementations • 7 Apr 2020 • Michał Koziarski
Furthermore, we combine both in the Combined Synthetic Oversampling and Undersampling Technique (CSMOUTE), which integrates SMOTE oversampling with SMUTE undersampling.
no code implementations • 7 Apr 2020 • Michał Koziarski
Data imbalance remains one of the open challenges in the contemporary machine learning.
no code implementations • 7 Apr 2020 • Michał Koziarski, Michał Woźniak, Bartosz Krawczyk
The proposed method utilizes an energy-based approach to modeling the regions suitable for oversampling, less affected by small disjuncts and outliers than SMOTE.
1 code implementation • 2 Jun 2019 • Michał Koziarski
Aforementioned difficulty factors can also limit the applicability of some of the methods of dealing with data imbalance, in particular the neighborhood-based oversampling algorithms based on SMOTE.