Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Oglic, Dino"'
The primary objective of most lead optimization campaigns is to enhance the binding affinity of ligands. For large molecules such as antibodies, identifying mutations that enhance antibody affinity is particularly challenging due to the combinatorial
Externí odkaz:
http://arxiv.org/abs/2406.07263
Graph neural networks (GNNs) and variations of the message passing algorithm are the predominant means for learning on graphs, largely due to their flexibility, speed, and satisfactory performance. The design of powerful and general purpose GNNs, how
Externí odkaz:
http://arxiv.org/abs/2402.10793
Autor:
Ucar, Talip, Ramon, Aubin, Oglic, Dino, Croasdale-Wood, Rebecca, Diethe, Tom, Sormanni, Pietro
We investigate the potential of patent data for improving the antibody humanness prediction using a multi-stage, multi-loss training process. Humanness serves as a proxy for the immunogenic response to antibody therapeutics, one of the major causes o
Externí odkaz:
http://arxiv.org/abs/2401.14442
An effective aggregation of node features into a graph-level representation via readout functions is an essential step in numerous learning tasks involving graph neural networks. Typically, readouts are simple and non-adaptive functions designed such
Externí odkaz:
http://arxiv.org/abs/2211.04952
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2022
We study the problem of learning robust acoustic models in adverse environments, characterized by a significant mismatch between training and test conditions. This problem is of paramount importance for the deployment of speech recognition systems th
Externí odkaz:
http://arxiv.org/abs/2110.08634
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing 2021
We investigate the potential of stochastic neural networks for learning effective waveform-based acoustic models. The waveform-based setting, inherent to fully end-to-end speech recognition systems, is motivated by several comparative studies of auto
Externí odkaz:
http://arxiv.org/abs/1906.09526
Autor:
Oglic, Dino, Gärtner, Thomas
We provide the first mathematically complete derivation of the Nystr\"om method for low-rank approximation of indefinite kernels and propose an efficient method for finding an approximate eigendecomposition of such kernel matrices. Building on this r
Externí odkaz:
http://arxiv.org/abs/1809.02157
Random Fourier features is a widely used, simple, and effective technique for scaling up kernel methods. The existing theoretical analysis of the approach, however, remains focused on specific learning tasks and typically gives pessimistic bounds whi
Externí odkaz:
http://arxiv.org/abs/1806.09178
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