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The long-term goal of this work is to learn data-symmetries driven group convolutions and network topologies in CNNs. Imposing structure in subsets of weights, ...
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In this paper, we propose the use of data symmetries, in the sense of equivalences under signal trans- formations, as priors for learning symmetry-adapted ...
In this paper, we propose the use of data symmetries, in the sense of equivalences under signal transformations, as priors for learning symmetry-adapted ...
Dec 18, 2018 · In this paper, we propose the use of data symmetries, in the sense of equivalences under signal transformations, as priors for learning ...
Jan 29, 2023 · SALCs (Symmetry Adapted Linear Combinations) are the linear combinations of basis sets composed of the stretching vectors of the molecule.
Symmetry in efficient big data mining to protect against security attacks; Innovative deep learning framework for efficient detection of security attacks ...
Nov 25, 2019 · Tests on the H2 + Ag(111) system show that this approach yields an accurate, efficient, and continuous representation of EFT, making it possible ...
Mar 20, 2023 · We have developed a symmetry-adapted modeling procedure for molecules and crystals. By using the completeness of multipoles to express spatial and time- ...
Oct 21, 2019 · Symmetry-Based Disentangled Representation Learning cannot only be based on static observations: agents should interact with the environment to ...
Missing: adapted | Show results with:adapted
Disentangled Representation Learning aims at finding a low-dimensional vector representation of the world for which the underlying structure of the world is ...
Missing: adapted | Show results with:adapted