Zobrazeno 1 - 10
of 651
pro vyhledávání: '"Simoncelli P"'
Autor:
Goblot, Valentin, Wu, Kexin, Di Lucente, Enrico, Zhu, Yuchun, Losero, Elena, Jobert, Quentin, Concha, Claudio Jaramillo, Marzari, Nicola, Simoncelli, Michele, Galland, Christophe
Among all materials, mono-crystalline diamond has one of the highest measured thermal conductivities, with values above 2000 W/m/K at room temperature. This stems from momentum-conserving `normal' phonon-phonon scattering processes dominating over mo
Externí odkaz:
http://arxiv.org/abs/2411.04065
Prediction is a fundamental capability of all living organisms, and has been proposed as an objective for learning sensory representations. Recent work demonstrates that in primate visual systems, prediction is facilitated by neural representations t
Externí odkaz:
http://arxiv.org/abs/2411.01777
Temporal prediction is inherently uncertain, but representing the ambiguity in natural image sequences is a challenging high-dimensional probabilistic inference problem. For natural scenes, the curse of dimensionality renders explicit density estimat
Externí odkaz:
http://arxiv.org/abs/2411.00842
Image representations (artificial or biological) are often compared in terms of their global geometry; however, representations with similar global structure can have strikingly different local geometries. Here, we propose a framework for comparing a
Externí odkaz:
http://arxiv.org/abs/2410.15433
Score diffusion methods can learn probability densities from samples. The score of the noise-corrupted density is estimated using a deep neural network, which is then used to iteratively transport a Gaussian white noise density to a target density. V
Externí odkaz:
http://arxiv.org/abs/2410.11646
Autor:
Un, Hio-Ieng, Iwanowski, Kamil, Orri, Jordi Ferrer, Jacobs, Ian E., Fukui, Naoya, Cornil, David, Beljonne, David, Simoncelli, Michele, Nishihara, Hiroshi, Sirringhaus, Henning
Thermoelectric materials, enabling direct waste-heat to electricity conversion, need to be highly electrically conducting while simultaneously thermally insulating. This is fundamentally challenging since electrical and thermal conduction are usually
Externí odkaz:
http://arxiv.org/abs/2410.11555
Advances in machine learning have led to the development of foundation models for atomistic materials chemistry, enabling quantum-accurate descriptions of interatomic forces across diverse compounds at reduced computational cost. Hitherto, these mode
Externí odkaz:
http://arxiv.org/abs/2408.00755
Autor:
Lipshutz, David, Simoncelli, Eero P.
Publikováno v:
Adv. Neural Information Processing (NeurIPS), Dec 2024
Efficient coding theory posits that sensory circuits transform natural signals into neural representations that maximize information transmission subject to resource constraints. Local interneurons are thought to play an important role in these trans
Externí odkaz:
http://arxiv.org/abs/2405.17745
We re-examine the problem of reconstructing a high-dimensional signal from a small set of linear measurements, in combination with image prior from a diffusion probabilistic model. Well-established methods for optimizing such measurements include pri
Externí odkaz:
http://arxiv.org/abs/2405.17456
Autor:
Simoncelli, Michele, Fournier, Daniele, Marangolo, Massimiliano, Balan, Etienne, Béneut, Keevin, Baptiste, Benoit, Doisneau, Béatrice, Marzari, Nicola, Mauri, Francesco
The thermal conductivities of crystals and glasses vary strongly and with opposite trends upon heating, decreasing in crystals and increasing in glasses. Here, we show--both with first-principles predictions based on the Wigner transport equation and
Externí odkaz:
http://arxiv.org/abs/2405.13161