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Multitask Prompted Training Enables Zero-Shot Task Generalization


Oct 15, 2021
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, Alexander M. Rush

* https://github.com/bigscience-workshop/promptsource/ 

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Understood in Translation, Transformers for Domain Understanding


Dec 18, 2020
Dimitrios Christofidellis, Matteo Manica, Leonidas Georgopoulos, Hans Vandierendonck

* 4 figures, 7 tables, main text pages 8, appendix pages 6 

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Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks


Dec 05, 2020
Modestas Filipavicius, Matteo Manica, Joris Cadow, Maria Rodriguez Martinez

* 20 pages, 12 figures, accepted to Machine Learning for Structural Biology (MLSB) workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS) 

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Hierarchical Pre-training for Sequence Labelling in Spoken Dialog


Oct 03, 2020
Emile Chapuis, Pierre Colombo, Matteo Manica, Matthieu Labeau, Chloe Clavel

* EMNLP 2020 

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PaccMann$^{RL}$ on SARS-CoV-2: Designing antiviral candidates with conditional generative models


May 31, 2020
Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, María Rodríguez Martínez

* 5 pages, 6 figures 

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Targeted design of antiviral compounds against SARS-CoV-2 with conditional generative models


May 27, 2020
Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, María Rodríguez Martínez

* 5 pages, 6 figures 

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Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue


Feb 26, 2020
Pierre Colombo, Emile Chapuis, Matteo Manica, Emmanuel Vignon, Giovanna Varni, Chloe Clavel

* WACAI 2020 
* in French 

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Guiding attention in Sequence-to-sequence models for Dialogue Act prediction


Feb 26, 2020
Pierre Colombo, Emile Chapuis, Matteo Manica, Emmanuel Vignon, Giovanna Varni, Chloe Clavel

* AAAI 2020 

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Reinforcement learning-driven de-novo design of anticancer compounds conditioned on biomolecular profiles


Aug 29, 2019
Jannis Born, Matteo Manica, Ali Oskooei, María Rodríguez Martínez

* 11 pages, 5 figures 

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An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries


Jul 19, 2019
Matteo Manica, Christoph Auer, Valery Weber, Federico Zipoli, Michele Dolfi, Peter Staar, Teodoro Laino, Costas Bekas, Akihiro Fujita, Hiroki Toda, Shuichi Hirose, Yasumitsu Orii

* 4 pages, 1 figure, Workshop on Applied Data Science for Healthcare at KDD, Anchorage, AK, 2019 

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Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders


May 22, 2019
Matteo Manica, Ali Oskooei, Jannis Born, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

* 11 pages, 5 figures, 1 table, Workshop on Computational Biology at the International Conference on Machine Learning (ICML), Long Beach, CA, 2019 

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NeuNetS: An Automated Synthesis Engine for Neural Network Design


Jan 17, 2019
Atin Sood, Benjamin Elder, Benjamin Herta, Chao Xue, Costas Bekas, A. Cristiano I. Malossi, Debashish Saha, Florian Scheidegger, Ganesh Venkataraman, Gegi Thomas, Giovanni Mariani, Hendrik Strobelt, Horst Samulowitz, Martin Wistuba, Matteo Manica, Mihir Choudhury, Rong Yan, Roxana Istrate, Ruchir Puri, Tejaswini Pedapati

* 14 pages, 12 figures. arXiv admin note: text overlap with arXiv:1806.00250 

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PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks


Nov 16, 2018
Ali Oskooei, Jannis Born, Matteo Manica, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

* 10 pages, 5 figures, 2 tables. NIPS MLMM 2018 

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Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer


Aug 18, 2018
Ali Oskooei, Matteo Manica, Roland Mathis, Maria Rodriguez Martinez

* 36 pages, 5 figures, 3 supplementary figures 

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PIMKL: Pathway Induced Multiple Kernel Learning


Jul 05, 2018
Matteo Manica, Joris Cadow, Roland Mathis, María Rodríguez Martínez


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