Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory von Mathiasen, Alexander, Helal, Hatem, Balanca, Paul, Krzywaniak, Adam, Parviz, Ali, Hvilshøj, Frederik, Banaszewski, Blazej, Luschi, Carlo, Fitzgibbon, Andrew William

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BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion von Cattaneo, Alberto, Justus, Daniel, Mellor, Harry, Orr, Douglas, Maloberti, Jerome, Liu, Zhenying, Farnsworth, Thorin, Fitzgibbon, Andrew, Banaszewski, Blazej, Luschi, Carlo

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Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory von Mathiasen, Alexander, Helal, Hatem, Balanca, Paul, Krzywaniak, Adam, Ali, Parviz, Hvilshøj, Frederik, Banaszewski, Blazej, Luschi, Carlo, Fitzgibbon, Andrew William

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BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion von Cattaneo, Alberto, Justus, Daniel, Mellor, Harry, Orr, Douglas, Maloberti, Jerome, Liu, Zhenying, Farnsworth, Thorin, Fitzgibbon, Andrew, Banaszewski, Blazej, Luschi, Carlo

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texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning von Kläser, Kerstin, Banaszewski, Błażej, Maddrell-Mander, Samuel, McLean, Callum, Müller, Luis, Parviz, Ali, Huang, Shenyang, Fitzgibbon, Andrew

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Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets von Beaini, Dominique, Huang, Shenyang, Cunha, Joao Alex, Li, Zhiyi, Moisescu-Pareja, Gabriela, Dymov, Oleksandr, Maddrell-Mander, Samuel, McLean, Callum, Wenkel, Frederik, Müller, Luis, Mohamud, Jama Hussein, Parviz, Ali, Craig, Michael, Koziarski, Michał, Lu, Jiarui, Zhu, Zhaocheng, Gabellini, Cristian, Klaser, Kerstin, Dean, Josef, Wognum, Cas, Sypetkowski, Maciej, Rabusseau, Guillaume, Rabbany, Reihaneh, Tang, Jian, Morris, Christopher, Koutis, Ioannis, Ravanelli, Mirco, Wolf, Guy, Tossou, Prudencio, Mary, Hadrien, Bois, Therence, Fitzgibbon, Andrew, Banaszewski, Błażej, Martin, Chad, Masters, Dominic

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(\texttt{MiniMol}\): A Parameter-Efficient Foundation Model for Molecular Learning von Kläser, Kerstin, Banaszewski, Błażej, Maddrell-Mander, Samuel, McLean, Callum, Müller, Luis, Ali, Parviz, Huang, Shenyang, Fitzgibbon, Andrew

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Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets von Beaini, Dominique, Huang, Shenyang, Cunha, Joao Alex, Li, Zhiyi, Moisescu-Pareja, Gabriela, Dymov, Oleksandr, Maddrell-Mander, Samuel, McLean, Callum, Wenkel, Frederik, Müller, Luis, Jama Hussein Mohamud, Ali, Parviz, Craig, Michael, Koziarski, Michał, Lu, Jiarui, Zhu, Zhaocheng, Gabellini, Cristian, Klaser, Kerstin, Dean, Josef, Wognum, Cas, Sypetkowski, Maciej, Rabusseau, Guillaume, Rabbany, Reihaneh, Tang, Jian, Morris, Christopher, Koutis, Ioannis, Ravanelli, Mirco, Wolf, Guy, Prudencio Tossou, Hadrien, Mary, Bois, Therence, Fitzgibbon, Andrew, Banaszewski, Błażej, Martin, Chad, Masters, Dominic

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