Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Maziarz, Krzysztof"'
Autor:
Gaiński, Piotr, Koziarski, Michał, Maziarz, Krzysztof, Segler, Marwin, Tabor, Jacek, Śmieja, Marek
Single-step retrosynthesis aims to predict a set of reactions that lead to the creation of a target molecule, which is a crucial task in molecular discovery. Although a target molecule can often be synthesized with multiple different reactions, it is
Externí odkaz:
http://arxiv.org/abs/2406.18739
Autor:
Maziarz, Krzysztof, Tripp, Austin, Liu, Guoqing, Stanley, Megan, Xie, Shufang, Gaiński, Piotr, Seidl, Philipp, Segler, Marwin
The planning of how to synthesize molecules, also known as retrosynthesis, has been a growing focus of the machine learning and chemistry communities in recent years. Despite the appearance of steady progress, we argue that imperfect benchmarks and i
Externí odkaz:
http://arxiv.org/abs/2310.19796
Autor:
Tripp, Austin, Maziarz, Krzysztof, Lewis, Sarah, Segler, Marwin, Hernández-Lobato, José Miguel
Retrosynthesis is the task of planning a series of chemical reactions to create a desired molecule from simpler, buyable molecules. While previous works have proposed algorithms to find optimal solutions for a range of metrics (e.g. shortest, lowest-
Externí odkaz:
http://arxiv.org/abs/2310.09270
Autor:
Muenkler, Hagen, Misztela, Hubert, Pikusa, Michal, Segler, Marwin, Schneider, Nadine, Maziarz, Krzysztof
Many contemporary generative models of molecules are variational auto-encoders of molecular graphs. One term in their training loss pertains to reconstructing the input, yet reconstruction capabilities of state-of-the-art models have not yet been tho
Externí odkaz:
http://arxiv.org/abs/2305.03041
Autor:
Liu, Guoqing, Xue, Di, Xie, Shufang, Xia, Yingce, Tripp, Austin, Maziarz, Krzysztof, Segler, Marwin, Qin, Tao, Zhang, Zongzhang, Liu, Tie-Yan
Retrosynthesis, which aims to find a route to synthesize a target molecule from commercially available starting materials, is a critical task in drug discovery and materials design. Recently, the combination of ML-based single-step reaction predictor
Externí odkaz:
http://arxiv.org/abs/2301.13755
In many applications one wants to identify identical subtrees of a program syntax tree. This identification should ideally be robust to alpha-renaming of the program, but no existing technique has been shown to achieve this with good efficiency (bett
Externí odkaz:
http://arxiv.org/abs/2105.02856
Recent advancements in computer vision promise to automate medical image analysis. Rheumatoid arthritis is an autoimmune disease that would profit from computer-based diagnosis, as there are no direct markers known, and doctors have to rely on manual
Externí odkaz:
http://arxiv.org/abs/2104.13915
Autor:
Maziarz, Krzysztof, Jackson-Flux, Henry, Cameron, Pashmina, Sirockin, Finton, Schneider, Nadine, Stiefl, Nikolaus, Segler, Marwin, Brockschmidt, Marc
Recent advancements in deep learning-based modeling of molecules promise to accelerate in silico drug discovery. A plethora of generative models is available, building molecules either atom-by-atom and bond-by-bond or fragment-by-fragment. However, m
Externí odkaz:
http://arxiv.org/abs/2103.03864
Autor:
Wojna, Zbigniew, Maziarz, Krzysztof, Jocz, Łukasz, Pałuba, Robert, Kozikowski, Robert, Kokkinos, Iasonas
We address six different classification tasks related to fine-grained building attributes: construction type, number of floors, pitch and geometry of the roof, facade material, and occupancy class. Tackling such a remote building analysis problem bec
Externí odkaz:
http://arxiv.org/abs/2008.10041
Autor:
Maziarz, Krzysztof, Kokiopoulou, Efi, Gesmundo, Andrea, Sbaiz, Luciano, Bartok, Gabor, Berent, Jesse
This paper proposes a novel learning method for multi-task applications. Multi-task neural networks can learn to transfer knowledge across different tasks by using parameter sharing. However, sharing parameters between unrelated tasks can hurt perfor
Externí odkaz:
http://arxiv.org/abs/1910.04915