日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Software for dataset-wide XAI: From local explanations to global insights with Zennit, CoRelAy, and ViRelAy

用于数据集级 XAI 的软件:利用 Zennit、CoRelAy 和 ViRelAy 从局部解释到全局洞察

Anders, Christopher J; Neumann, David; Samek, Wojciech; Müller, Klaus-Robert; Lapuschkin, Sebastian

Aligning machine and human visual representations across abstraction levels

在不同抽象层次上协调机器和人类的视觉表征

Muttenthaler, Lukas; Greff, Klaus; Born, Frieda; Spitzer, Bernhard; Kornblith, Simon; Mozer, Michael C; Müller, Klaus-Robert; Unterthiner, Thomas; Lampinen, Andrew K

Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence

利用多模态真实世界数据和可解释人工智能解码泛癌治疗结果

Keyl, Julius; Keyl, Philipp; Montavon, Grégoire; Hosch, René; Brehmer, Alexander; Mochmann, Liliana; Jurmeister, Philipp; Dernbach, Gabriel; Kim, Moon; Koitka, Sven; Bauer, Sebastian; Bechrakis, Nikolaos; Forsting, Michael; Führer-Sakel, Dagmar; Glas, Martin; Grünwald, Viktor; Hadaschik, Boris; Haubold, Johannes; Herrmann, Ken; Kasper, Stefan; Kimmig, Rainer; Lang, Stephan; Rassaf, Tienush; Roesch, Alexander; Schadendorf, Dirk; Siveke, Jens T; Stuschke, Martin; Sure, Ulrich; Totzeck, Matthias; Welt, Anja; Wiesweg, Marcel; Baba, Hideo A; Nensa, Felix; Egger, Jan; Müller, Klaus-Robert; Schuler, Martin; Klauschen, Frederick; Kleesiek, Jens

Peering inside the black box by learning the relevance of many-body functions in neural network potentials

通过学习神经网络势中多体函数的相关性来窥探黑箱内部

Bonneau, Klara; Lederer, Jonas; Templeton, Clark; Rosenberger, David; Giambagli, Lorenzo; Müller, Klaus-Robert; Clementi, Cecilia

Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields

利用预训练神经网络和通用成对力场进行分子模拟

Kabylda, Adil; Frank, J Thorben; Suárez-Dou, Sergio; Khabibrakhmanov, Almaz; Medrano Sandonas, Leonardo; Unke, Oliver T; Chmiela, Stefan; Müller, Klaus-Robert; Tkatchenko, Alexandre

Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023

碰撞测试机器学习力场在分子、材料和界面中的应用:TEA 2023挑战赛中的分子动力学

Poltavsky, Igor; Puleva, Mirela; Charkin-Gorbulin, Anton; Fonseca, Grégory; Batatia, Ilyes; Browning, Nicholas J; Chmiela, Stefan; Cui, Mengnan; Frank, J Thorben; Heinen, Stefan; Huang, Bing; Käser, Silvan; Kabylda, Adil; Khan, Danish; Müller, Carolin; Price, Alastair J A; Riedmiller, Kai; Töpfer, Kai; Ko, Tsz Wai; Meuwly, Markus; Rupp, Matthias; Csányi, Gábor; Anatole von Lilienfeld, O; Margraf, Johannes T; Müller, Klaus-Robert; Tkatchenko, Alexandre

Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023

分子、材料和界面的碰撞测试机器学习力场:TEA Challenge 2023 中的模型分析

Poltavsky, Igor; Charkin-Gorbulin, Anton; Puleva, Mirela; Fonseca, Grégory; Batatia, Ilyes; Browning, Nicholas J; Chmiela, Stefan; Cui, Mengnan; Frank, J Thorben; Heinen, Stefan; Huang, Bing; Käser, Silvan; Kabylda, Adil; Khan, Danish; Müller, Carolin; Price, Alastair J A; Riedmiller, Kai; Töpfer, Kai; Ko, Tsz Wai; Meuwly, Markus; Rupp, Matthias; Csányi, Gábor; von Lilienfeld, O Anatole; Margraf, Johannes T; Müller, Klaus-Robert; Tkatchenko, Alexandre

The QCML dataset, Quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations

QCML数据集,包含来自3350万个DFT计算和147亿个半经验计算的量子化学参考数据。

Ganscha, Stefan; Unke, Oliver T; Ahlin, Daniel; Maennel, Hartmut; Kashubin, Sergii; Müller, Klaus-Robert

Analyzing Atomic Interactions in Molecules as Learned by Neural Networks

利用神经网络学习分析分子中的原子相互作用

Esders, Malte; Schnake, Thomas; Lederer, Jonas; Kabylda, Adil; Montavon, Grégoire; Tkatchenko, Alexandre; Müller, Klaus-Robert

Neural interaction explainable AI predicts drug response across cancers

神经交互可解释人工智能预测多种癌症的药物反应。

Keyl, Philipp; Keyl, Julius; Mock, Andreas; Dernbach, Gabriel; Mochmann, Liliana H; Kiermeyer, Niklas; Jurmeister, Philipp; Bockmayr, Michael; Schwarz, Roland F; Montavon, Grégoire; Müller, Klaus-Robert; Klauschen, Frederick