Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Rampasek, Ladislav"'
Autor:
Korablyov, Maksym, Liu, Cheng-Hao, Jain, Moksh, van der Sloot, Almer M., Jolicoeur, Eric, Ruediger, Edward, Nica, Andrei Cristian, Bengio, Emmanuel, Lapchevskyi, Kostiantyn, St-Cyr, Daniel, Schuetz, Doris Alexandra, Butoi, Victor Ion, Rector-Brooks, Jarrid, Blackburn, Simon, Feng, Leo, Nekoei, Hadi, Gottipati, SaiKrishna, Vijayan, Priyesh, Gupta, Prateek, Rampášek, Ladislav, Avancha, Sasikanth, Bacon, Pierre-Luc, Hamilton, William L., Paige, Brooks, Misra, Sanchit, Jastrzebski, Stanislaw Kamil, Kaul, Bharat, Precup, Doina, Hernández-Lobato, José Miguel, Segler, Marwin, Bronstein, Michael, Marinier, Anne, Tyers, Mike, Bengio, Yoshua
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active lear
Externí odkaz:
http://arxiv.org/abs/2405.01616
Autor:
Cantürk, Semih, Liu, Renming, Lapointe-Gagné, Olivier, Létourneau, Vincent, Wolf, Guy, Beaini, Dominique, Rampášek, Ladislav
Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, rendering them essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for all grap
Externí odkaz:
http://arxiv.org/abs/2307.07107
Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing) graph neural networks. So far, they have shown promising empirical results, e.g., on molecu
Externí odkaz:
http://arxiv.org/abs/2302.04181
Autor:
Masters, Dominic, Dean, Josef, Klaser, Kerstin, Li, Zhiyi, Maddrell-Mander, Sam, Sanders, Adam, Helal, Hatem, Beker, Deniz, Fitzgibbon, Andrew, Huang, Shenyang, Rampášek, Ladislav, Beaini, Dominique
We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction. Our model integrates a well-tuned local message passing component and biased global attention with other key ideas from prior liter
Externí odkaz:
http://arxiv.org/abs/2302.02947
Autor:
Masters, Dominic, Dean, Josef, Klaser, Kerstin, Li, Zhiyi, Maddrell-Mander, Sam, Sanders, Adam, Helal, Hatem, Beker, Deniz, Rampášek, Ladislav, Beaini, Dominique
This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task. Our approach implements several key principles from the prior literat
Externí odkaz:
http://arxiv.org/abs/2212.02229
Autor:
Dwivedi, Vijay Prakash, Rampášek, Ladislav, Galkin, Mikhail, Parviz, Ali, Wolf, Guy, Luu, Anh Tuan, Beaini, Dominique
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer. In principle, such networks are not able to capture long-range intera
Externí odkaz:
http://arxiv.org/abs/2206.08164
Autor:
Liu, Renming, Cantürk, Semih, Wenkel, Frederik, McGuire, Sarah, Wang, Xinyi, Little, Anna, O'Bray, Leslie, Perlmutter, Michael, Rieck, Bastian, Hirn, Matthew, Wolf, Guy, Rampášek, Ladislav
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on developing GNN models with superior performance according to a collectio
Externí odkaz:
http://arxiv.org/abs/2206.07729
Autor:
Rampášek, Ladislav, Galkin, Mikhail, Dwivedi, Vijay Prakash, Luu, Anh Tuan, Wolf, Guy, Beaini, Dominique
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph repres
Externí odkaz:
http://arxiv.org/abs/2205.12454
Autor:
Liu, Renming, Cantürk, Semih, Wenkel, Frederik, Sandfelder, Dylan, Kreuzer, Devin, Little, Anna, McGuire, Sarah, O'Bray, Leslie, Perlmutter, Michael, Rieck, Bastian, Hirn, Matthew, Wolf, Guy, Rampášek, Ladislav
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data. Although many different types of GNN models have been developed, with many benchmarking procedures to demonst
Externí odkaz:
http://arxiv.org/abs/2110.14809
Autor:
Rampášek, Ladislav, Wolf, Guy
Graph neural networks (GNNs) based on message passing between neighboring nodes are known to be insufficient for capturing long-range interactions in graphs. In this project we study hierarchical message passing models that leverage a multi-resolutio
Externí odkaz:
http://arxiv.org/abs/2107.07432