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Found 6 papers, 0 papers with code
Less is More! A slim architecture for optimal language translation
Our method outperforms existing techniques in terms of test loss while simultaneously halving the number of parameters.
Prediction and compression of lattice QCD data using machine learning algorithms on quantum annealer
In the compression algorithm, we define a mapping from lattice QCD data of floating-point numbers to the binary coefficients that closely reconstruct the input data from a set of basis vectors.
Lossy compression of statistical data using quantum annealer
We present a new lossy compression algorithm for statistical floating-point data through a representation learning with binary variables.
Inference of neutrino flavor evolution through data assimilation and neural differential equations
In this paper, we build upon recent work to explore inference-based techniques for estimation of model parameters and neutrino flavor evolution histories.
High Energy Astrophysical Phenomena High Energy Physics - Theory Computational Physics
Exact representations of many body interactions with RBM neural networks
Restricted Boltzmann Machines (RBM) are simple statistical models defined on a bipartite graph which have been successfully used in studying more complicated many-body systems, both classical and quantum.
Nuclear Theory Computational Physics Quantum Physics
Constraints on Axion-like Particles and Nucleon Pairing in Dense Matter from the Hot Neutron Star in HESS J1731-347
If the thermal evolution of the hot young neutron star in the supernova remnant HESS J1731-347 is driven by neutrino emission, it provides a stringent constraint on the coupling of light (mass $\ll 10$ keV) axion-like particles to neutrons.
High Energy Astrophysical Phenomena
