Zobrazeno 1 - 10
of 19 681
pro vyhledávání: '"Heinonen, A."'
Autor:
Darnet, Mathieu, Kim, Bitnarae, Vedrine, Simon, Deparis, Jacques, Bretaudeau, Francois, Gance, Julien, Vermeersch, Fabrice, Truffert, Catherine, Autio, Uula, Kamm, Jochen, Patzer, Cedric, Kalscheuer, Thomas, Heinonen, Suvi
Publikováno v:
EAGE NSG 2024, Sep 2024, Helsinki (FI), Finland
The transition towards carbon neutral transportation and energy sources increases the global demand for mineral raw materials while easy-to-find near-surface (\< 200 m) ore deposits are unlikely discovered in well-explored areas such as Europe. In or
Externí odkaz:
http://arxiv.org/abs/2407.11445
The problem of locating an odor source in turbulent environments is central to key applications such as environmental monitoring and disaster response. We address this challenge by designing an algorithm based on Bayesian inference, which uses odor m
Externí odkaz:
http://arxiv.org/abs/2407.08343
Deep neural networks (DNNs) excel on clean images but struggle with corrupted ones. Incorporating specific corruptions into the data augmentation pipeline can improve robustness to those corruptions but may harm performance on clean images and other
Externí odkaz:
http://arxiv.org/abs/2406.16540
Introducing training-time augmentations is a key technique to enhance generalization and prepare deep neural networks against test-time corruptions. Inspired by the success of generative diffusion models, we propose a novel approach coupling data aug
Externí odkaz:
http://arxiv.org/abs/2406.01494
In the domains of image and audio, diffusion models have shown impressive performance. However, their application to discrete data types, such as language, has often been suboptimal compared to autoregressive generative models. This paper tackles the
Externí odkaz:
http://arxiv.org/abs/2405.17889
Retrosynthesis, the task of identifying precursors for a given molecule, can be naturally framed as a conditional graph generation task. Diffusion models are a particularly promising modelling approach, enabling post-hoc conditioning and trading off
Externí odkaz:
http://arxiv.org/abs/2405.17656
Climate and weather prediction traditionally relies on complex numerical simulations of atmospheric physics. Deep learning approaches, such as transformers, have recently challenged the simulation paradigm with complex network forecasts. However, the
Externí odkaz:
http://arxiv.org/abs/2404.10024
Autor:
Dumitrescu, Alexandru, Korpela, Dani, Heinonen, Markus, Verma, Yogesh, Iakovlev, Valerii, Garg, Vikas, Lähdesmäki, Harri
This work introduces FMG, a field-based model for drug-like molecule generation. We show how the flexibility of this method provides crucial advantages over the prevalent, point-cloud based methods, and achieves competitive molecular stability genera
Externí odkaz:
http://arxiv.org/abs/2402.15864
Autor:
Khalili, Mahnaz, Brodic, Bojan, Göransson, Peter, Heinonen, Suvi, Hesthaven, Jan S., Pasanen, Antti, Vauhkonen, Marko, Yadav, Rahul, Lähivaara, Timo
With global groundwater levels undergoing rapid declines, there is an urgent need for advanced methodologies to monitor and manage aquifers. This paper explores the potential of seismic data as a tool for these purposes. Building upon prior studies e
Externí odkaz:
http://arxiv.org/abs/2312.14605
Autor:
Luukkonen, Risto, Komulainen, Ville, Luoma, Jouni, Eskelinen, Anni, Kanerva, Jenna, Kupari, Hanna-Mari, Ginter, Filip, Laippala, Veronika, Muennighoff, Niklas, Piktus, Aleksandra, Wang, Thomas, Tazi, Nouamane, Scao, Teven Le, Wolf, Thomas, Suominen, Osma, Sairanen, Samuli, Merioksa, Mikko, Heinonen, Jyrki, Vahtola, Aija, Antao, Samuel, Pyysalo, Sampo
Large language models (LLMs) excel in many tasks in NLP and beyond, but most open models have very limited coverage of smaller languages and LLM work tends to focus on languages where nearly unlimited data is available for pretraining. In this work,
Externí odkaz:
http://arxiv.org/abs/2311.05640