Machine Learning of Accurate Energy-conserving Molecular Force Fields

1 code implementation • Science Advances 2017 • Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., Müller, K.-R.

Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories.

Atomic Forces BIG-bench Machine Learning

Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields

1 code implementation • Nature Communications 2018 • Chmiela, S., Sauceda, H. E., Müller, K.-R., Tkatchenko, A.

We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules.

sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning

1 code implementation • Computer Physics Communications 2019 • Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K.-R., Tkatchenko, A.

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model.

Atomic Forces BIG-bench Machine Learning

Cosmic Background Removal with Deep Neural Networks in SBND

1 code implementation • 2 Dec 2020 • SBND Collaboration, R. Acciarri, C. Adams, C. Andreopoulos, J. Asaadi, M. Babicz, C. Backhouse, W. Badgett, L. Bagby, D. Barker, V. Basque, Q. Bazetto, M. Betancourt, A. Bhanderi, A. Bhat, C. Bonifazi, D. Brailsford, G. Brandt, T. Brooks, F. Carneiro, Y. Chen, H. Chen, G. Chisnall, I. Crespo-Anadón, E. Cristaldo, C. Cuesta, I., L. de Icaza Astiz, A. De Roeck, G. de Sá Pereira, M. Del Tutto, V. Di Benedetto, A. Ereditato, J. Evans, C. Ezeribe, S. Fitzpatrick, T. Fleming, W. Foreman, D. Franco, I. Furic, P. Furmanski, S. Gao, D. Garcia-Gamez, H. Frandini, G. Ge, I. Gil-Botella, S. Gollapinni, O. Goodwin, P. Green, C. Griffith, R. Guenette, P. Guzowski, T. Ham, J. Henzerling, A. Holin, B. Howard, R., S. Jones, D. Kalra, G. Karagiorgi, L. Kashur, W. Ketchum, M., J. Kim, A. Kudryavtsev, J. Larkin, H. Lay, I. Lepetic, B., R. Littlejohn, W., C. Louis, A., A. Machado, M. Malek, D. Mardsen, C. Mariani, F. Marinho, A. Mastbaum, K. Mavrokoridis, N. McConkey, V. Meddage, P. Méndez, T. Mettler, K. Mistry, A. Mogan, J. Molina, M. Mooney, L. Mora, C., A. Moura, J. Mousseau, A. Navrer-Agasson, F., J. Nicolas-Arnaldos, A. Nowak, O. Palamara, V. Pandey, J. Pater, L. Paulucci, V., L. Pimentel, F. Psihas, G. Putnam, X. Qian, E. Raguzin, H. Ray, M. Reggiani-Guzzo, D. Rivera, M. Roda, M. Ross-Lonergan, G. Scanavini, A. Scarff, D., W. Schmitz, A. Schukraft, E. Segreto, M. Soares Nunes, M. Soderberg, S. Söldner-Rembold, J. Spitz, N., J., C. Spooner, M. Stancari, V. Stenico, A. Szelc, W. Tang, J. Tena Vidal, D. Torretta, M. Toups, C. Touramanis, M. Tripathi, S. Tufanli, E. Tyley, G., A. Valdiviesso, E. Worcester, M. Worcester, G. Yarbrough, J. Yu, B. Zamorano, J. Zennamo, A. Zglam

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded.

Semantic Segmentation Data Analysis, Statistics and Probability

Improving existing segmentators performance with zero-shot segmentators

1 code implementation • journal 2023 • Nanni, L., Fusaro, D., Fantozzi, C., Pretto, A.

We release with this paper the open-source implementation of our method.

Camouflaged Object Segmentation Segmentation +1

Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition

1 code implementation • BMVC 2021 • Alsehaim, A., Breckon, T.P.

Our consideration of Re-ID and action recognition as a multi-task problem results in a multi-branch 2D CNN architecture that outperforms prior work in the field (rank-1 (mAP) – MARS: 93. 21%(87. 23%), LPW: 79. 60%) without any reliance 3D convolutions or multi-stream networks architectures as found in other contemporary work.

Person Re-Identification Video Understanding +1

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

no code implementations • 12 Nov 2020 • Botelho, Joshi, A., Khara, Sarkar, S., Hegde, C., Rao, V., Adavani, S.S., & Ganapathysubramanian, B.

Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models training in reasonable time as well as distributing the storage requirements.

Distributed Computing

Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy

1 code implementation • Scientific Reports 2023 • Annys, A., Jannis, D. & Verbeeck, J.

Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner.