Zobrazeno 1 - 10
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pro vyhledávání: '"A P, Barry"'
Large Language Models (LLMs) have shown remarkable capabilities in natural language processing but exhibit significant performance gaps among different languages. Most existing approaches to address these disparities rely on pretraining or fine-tunin
Externí odkaz:
http://arxiv.org/abs/2410.12462
Autor:
Augeri, Christopher James, Mullins, Barry E., Baird III, Leemon C., Bulutoglu, Dursun A., Baldwin, Rusty O.
Publikováno v:
Proceedings of the 2007 workshop on Experimental Computer Science (ExpCS) at ACM FCRC 2007
XML simplifies data exchange among heterogeneous computers, but it is notoriously verbose and has spawned the development of many XML-specific compressors and binary formats. We present an XML test corpus and a combined efficiency metric integrating
Externí odkaz:
http://arxiv.org/abs/2410.07603
Autor:
Sanjaripour, Sogol, Hemmati, Shoubaneh, Mobasher, Bahram, Canalizo, Gabriela, Barish, Barry, Shivaei, Irene, Coil, Alison L., Chartab, Nima, Jafariyazani, Marziye, Reddy, Naveen A., Azadi, Mojegan
The growing volume of data produced by large astronomical surveys necessitates the development of efficient analysis techniques capable of effectively managing high-dimensional datasets. This study addresses this need by demonstrating some applicatio
Externí odkaz:
http://arxiv.org/abs/2410.07354
A long-standing challenge in quantum error correction is the infeasibility of universal transversal gates, as shown by the Eastin-Knill theorem. We obtain a necessary and sufficient condition for a quantum code to have universal transversal gates and
Externí odkaz:
http://arxiv.org/abs/2410.07045
Accommodating human preferences is essential for creating AI agents that deliver personalized and effective interactions. Recent work has shown the potential for LLMs to infer preferences from user interactions, but they often produce broad and gener
Externí odkaz:
http://arxiv.org/abs/2410.06273
Autor:
Hirota, Akihiko, Koda, Jin, Egusa, Fumi, Sawada, Tsuyoshi, Sakamoto, Kazushi, Heyer, Mark, Lee, Amanda M, Maeda, Fumiya, Boissier, Samuel, Calzetti, Daniela, Elmegreen, Bruce G., Harada, Nanase, Ho, Luis C., Kobayashi, Masato I. N., Kuno, Nario, Madore, Barry F., Martín, Sergio, Meyer, Jennifer Donovan, Muraoka, Kazuyuki, Watanabe, Yoshimasa
We present a catalog of clouds identified from the $^{12}$CO (1--0) data of M83, which was observed using Atacama Large Millimeter/submillimeter Array (ALMA) with a spatial resolution of $\sim$46 pc and a mass sensitivity of $\sim$10$^4$ $M_{\odot}$
Externí odkaz:
http://arxiv.org/abs/2410.05424
Autor:
Sultanow, Eldar, Selimllari, Fation, Dutta, Siddhant, Reese, Barry D., Tehrani, Madjid, Buchanan, William J
Data poisoning attacks on machine learning models aim to manipulate the data used for model training such that the trained model behaves in the attacker's favor. In classical models such as deep neural networks, large chains of dot products do indeed
Externí odkaz:
http://arxiv.org/abs/2410.05145
In multi-component dark matter models, a fraction $f_\text{pbh}$ of the dark matter could be in the form of primordial black holes (PBHs) with (sub)solar masses. Some would have formed binaries that presently trace the Milky Way halo of particle dark
Externí odkaz:
http://arxiv.org/abs/2410.04522
Large language models (LLMs) are stochastic, and not all models give deterministic answers, even when setting temperature to zero with a fixed random seed. However, few benchmark studies attempt to quantify uncertainty, partly due to the time and cos
Externí odkaz:
http://arxiv.org/abs/2410.03492
Large language models (LLMs) have started to play a vital role in modelling speech and text. To explore the best use of context and multiple systems' outputs for post-ASR speech emotion prediction, we study LLM prompting on a recent task named GenSEC
Externí odkaz:
http://arxiv.org/abs/2410.03312