Zobrazeno 1 - 10
of 20 416
pro vyhledávání: '"A A Markov"'
Autor:
Consoli, Sergio, Markov, Peter, Stilianakis, Nikolaos I., Bertolini, Lorenzo, Gallardo, Antonio Puertas, Ceresa, Mario
Publikováno v:
Lecture Notes in Networks and Systems, 2024, vol 1011, pages 241-252. Springer, Singapore
This paper presents a novel approach to epidemic surveillance, leveraging the power of Artificial Intelligence and Large Language Models (LLMs) for effective interpretation of unstructured big data sources, like the popular ProMED and WHO Disease Out
Externí odkaz:
http://arxiv.org/abs/2408.14277
Autor:
Harshan, Anishya, Tripodi, Roberta, Martis, Nicholas S., Rihtaršič, Gregor, Bradač, Maruša, Asada, Yoshihisa, Brammer, Gabe, Desprez, Guillaume, Estrada-Carpenter, Vince, Matharu, Jasleen, Markov, Vladan, Muzzin, Adam, Mowla, Lamiya, Noirot, Gaël, Sarrouh, Ghassan T. E., Sawicki, Marcin, Strait, Victoria, Willot, Chris
We present galaxy MACS0416-Y1 at z$_{\rm{spec}} = 8.312$ as observed by the CAnadian NIRISS Unbiased Cluster Survey (CANUCS). MACS0416-Y1 has been shown to have extreme dust properties, thus, we study the physical properties and star formation histor
Externí odkaz:
http://arxiv.org/abs/2408.12310
Autor:
Rohringer, Georg, Markov, Anton A.
We study the Mott metal-insulator transition in the Hubbard-Hofstadter model as a function of the orbital magnetic field, exploiting dynamical mean field theory (DMFT). Considering interaction strengths between the electrons for which this model is i
Externí odkaz:
http://arxiv.org/abs/2406.18729
Autor:
Nicolicioiu, Armand, Iofinova, Eugenia, Kurtic, Eldar, Nikdan, Mahdi, Panferov, Andrei, Markov, Ilia, Shavit, Nir, Alistarh, Dan
The availability of powerful open-source large language models (LLMs) opens exciting use-cases, such as automated personal assistants that adapt to the user's unique data and demands. Two key desiderata for such assistants are personalization-in the
Externí odkaz:
http://arxiv.org/abs/2407.10994
CANUCS: Constraining the MACS J0416.1-2403 Strong Lensing Model with JWST NIRISS, NIRSpec and NIRCam
Autor:
Rihtaršič, Gregor, Bradač, Maruša, Desprez, Guillaume, Harshan, Anishya, Noirot, Gaël, Estrada-Carpenter, Vicente, Martis, Nicholas S., Abraham, Roberto G., Asada, Yoshihisa, Brammer, Gabriel, Iyer, Kartheik G., Matharu, Jasleen, Mowla, Lamiya, Muzzin, Adam, Sarrouh, Ghassan T. E., Sawicki, Marcin, Strait, Victoria, Willott, Chris J., Gledhill, Rachel, Markov, Vladan, Tripodi, Roberta
Strong gravitational lensing in galaxy clusters has become an essential tool in astrophysics, allowing us to directly probe the dark matter distribution and study magnified background sources. The precision and reliability of strong lensing models re
Externí odkaz:
http://arxiv.org/abs/2406.10332
We show how to improve the inference efficiency of an LLM by expanding it into a mixture of sparse experts, where each expert is a copy of the original weights, one-shot pruned for a specific cluster of input values. We call this approach $\textit{Sp
Externí odkaz:
http://arxiv.org/abs/2405.15756
Social media conversations frequently suffer from toxicity, creating significant issues for users, moderators, and entire communities. Events in the real world, like elections or conflicts, can initiate and escalate toxic behavior online. Our study i
Externí odkaz:
http://arxiv.org/abs/2405.13754
Recent work has demonstrated that the latent spaces of large language models (LLMs) contain directions predictive of the truth of sentences. Multiple methods recover such directions and build probes that are described as getting at a model's "knowled
Externí odkaz:
http://arxiv.org/abs/2404.18865
Cross-lingual transfer has become an effective way of transferring knowledge between languages. In this paper, we explore an often overlooked aspect in this domain: the influence of the source language of a language model on language transfer perform
Externí odkaz:
http://arxiv.org/abs/2404.18810
Toxic language remains an ongoing challenge on social media platforms, presenting significant issues for users and communities. This paper provides a cross-topic and cross-lingual analysis of toxicity in Reddit conversations. We collect 1.5 million c
Externí odkaz:
http://arxiv.org/abs/2404.18726