Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Klambauer, Guenter"'
Autor:
Schmidinger, Niklas, Schneckenreiter, Lisa, Seidl, Philipp, Schimunek, Johannes, Hoedt, Pieter-Jan, Brandstetter, Johannes, Mayr, Andreas, Luukkonen, Sohvi, Hochreiter, Sepp, Klambauer, Günter
Language models for biological and chemical sequences enable crucial applications such as drug discovery, protein engineering, and precision medicine. Currently, these language models are predominantly based on Transformer architectures. While Transf
Externí odkaz:
http://arxiv.org/abs/2411.04165
Autor:
Schmied, Thomas, Adler, Thomas, Patil, Vihang, Beck, Maximilian, Pöppel, Korbinian, Brandstetter, Johannes, Klambauer, Günter, Pascanu, Razvan, Hochreiter, Sepp
In recent years, there has been a trend in the field of Reinforcement Learning (RL) towards large action models trained offline on large-scale datasets via sequence modeling. Existing models are primarily based on the Transformer architecture, which
Externí odkaz:
http://arxiv.org/abs/2410.22391
Autor:
Beck, Maximilian, Pöppel, Korbinian, Spanring, Markus, Auer, Andreas, Prudnikova, Oleksandra, Kopp, Michael, Klambauer, Günter, Brandstetter, Johannes, Hochreiter, Sepp
In the 1990s, the constant error carousel and gating were introduced as the central ideas of the Long Short-Term Memory (LSTM). Since then, LSTMs have stood the test of time and contributed to numerous deep learning success stories, in particular the
Externí odkaz:
http://arxiv.org/abs/2405.04517
Autor:
Sestak, Florian, Schneckenreiter, Lisa, Brandstetter, Johannes, Hochreiter, Sepp, Mayr, Andreas, Klambauer, Günter
Being able to identify regions within or around proteins, to which ligands can potentially bind, is an essential step to develop new drugs. Binding site identification methods can now profit from the availability of large amounts of 3D structures in
Externí odkaz:
http://arxiv.org/abs/2404.07194
Autor:
Schneckenreiter, Lisa, Freinschlag, Richard, Sestak, Florian, Brandstetter, Johannes, Klambauer, Günter, Mayr, Andreas
Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling. The expressivity of GNNs with respect to their ability to discriminate non-isomorphic grap
Externí odkaz:
http://arxiv.org/abs/2403.04747
Autor:
Hoedt, Pieter-Jan, Klambauer, Günter
Input-Convex Neural Networks (ICNNs) are networks that guarantee convexity in their input-output mapping. These networks have been successfully applied for energy-based modelling, optimal transport problems and learning invariances. The convexity of
Externí odkaz:
http://arxiv.org/abs/2312.12474
Autor:
Schweighofer, Kajetan, Aichberger, Lukas, Ielanskyi, Mykyta, Klambauer, Günter, Hochreiter, Sepp
Quantifying uncertainty is important for actionable predictions in real-world applications. A crucial part of predictive uncertainty quantification is the estimation of epistemic uncertainty, which is defined as an integral of the product between a d
Externí odkaz:
http://arxiv.org/abs/2307.03217
Autor:
Schimunek, Johannes, Seidl, Philipp, Friedrich, Lukas, Kuhn, Daniel, Rippmann, Friedrich, Hochreiter, Sepp, Klambauer, Günter
A central task in computational drug discovery is to construct models from known active molecules to find further promising molecules for subsequent screening. However, typically only very few active molecules are known. Therefore, few-shot learning
Externí odkaz:
http://arxiv.org/abs/2305.09481
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
Activity and property prediction models are the central workhorses in drug discovery and materials sciences, but currently they have to be trained or fine-tuned for new tasks. Without training or fine-tuning, scientific language models could be used
Externí odkaz:
http://arxiv.org/abs/2303.03363
Autor:
Fürst, Andreas, Rumetshofer, Elisabeth, Lehner, Johannes, Tran, Viet, Tang, Fei, Ramsauer, Hubert, Kreil, David, Kopp, Michael, Klambauer, Günter, Bitto-Nemling, Angela, Hochreiter, Sepp
CLIP yielded impressive results on zero-shot transfer learning tasks and is considered as a foundation model like BERT or GPT3. CLIP vision models that have a rich representation are pre-trained using the InfoNCE objective and natural language superv
Externí odkaz:
http://arxiv.org/abs/2110.11316