Zobrazeno 1 - 10
of 57 521
pro vyhledávání: '"Aslan ON"'
Autor:
Dizaji, Aslan S.
It has been shown that social institutions impact human motivations to produce different behaviours, such as amount of working or specialisation in labor. With advancement in artificial intelligence (AI), specifically large language models (LLMs), no
Externí odkaz:
http://arxiv.org/abs/2411.17724
In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a
Externí odkaz:
http://arxiv.org/abs/2411.13447
Autor:
Tsesmelis, Theodore, Palmieri, Luca, Khoroshiltseva, Marina, Islam, Adeela, Elkin, Gur, Shahar, Ofir Itzhak, Scarpellini, Gianluca, Fiorini, Stefano, Ohayon, Yaniv, Alali, Nadav, Aslan, Sinem, Morerio, Pietro, Vascon, Sebastiano, Gravina, Elena, Napolitano, Maria Cristina, Scarpati, Giuseppe, Zuchtriegel, Gabriel, Spühler, Alexandra, Fuchs, Michel E., James, Stuart, Ben-Shahar, Ohad, Pelillo, Marcello, Del Bue, Alessio
This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks. Our dataset has unique properties that are uncommon to current benchmarks for
Externí odkaz:
http://arxiv.org/abs/2410.24010
Autor:
Son, Guijin, Yoon, Dongkeun, Suk, Juyoung, Aula-Blasco, Javier, Aslan, Mano, Kim, Vu Trong, Islam, Shayekh Bin, Prats-Cristià, Jaume, Tormo-Bañuelos, Lucía, Kim, Seungone
Large language models (LLMs) are commonly used as evaluators in tasks (e.g., reward modeling, LLM-as-a-judge), where they act as proxies for human preferences or judgments. This leads to the need for meta-evaluation: evaluating the credibility of LLM
Externí odkaz:
http://arxiv.org/abs/2410.17578
Publikováno v:
ACCV2024
Jigsaw puzzle solving is a challenging task for computer vision since it requires high-level spatial and semantic reasoning. To solve the problem, existing approaches invariably use color and/or shape information but in many real-world scenarios, suc
Externí odkaz:
http://arxiv.org/abs/2410.16857
Human actions are based on the mental perception of the environment. Even when all the aspects of an environment are not visible, humans have an internal mental model that can generalize the partially visible scenes to fully constructed and connected
Externí odkaz:
http://arxiv.org/abs/2410.12372
Conventional tomographic reconstruction typically depends on centralized servers for both data storage and computation, leading to concerns about memory limitations and data privacy. Distributed reconstruction algorithms mitigate these issues by part
Externí odkaz:
http://arxiv.org/abs/2410.06106
Autor:
Aslan, Sipan, Ombao, Hernando
One fundamental challenge of data-driven analysis in neuroscience is modeling causal interactions and exploring the connectivity between nodes in a brain network. Various statistical methods, using different perspectives and data modalities, have bee
Externí odkaz:
http://arxiv.org/abs/2409.10374
Autor:
Rogers, Ted, Aslan, Fatma, Boglione, Mariaelena, Gonzalez-Hernandez, J. Osvaldo, Rainaldi, Tommaso, Simonelli, Andrea
This talk summarized work done recently to organize the steps for implementing TMD phenomenology in a way optimized for contexts where the extraction and interpretation of hadronic structures and nonperturbative effects is the primary driving motivat
Externí odkaz:
http://arxiv.org/abs/2408.07170
Neural network models for audio tasks, such as automatic speech recognition (ASR) and acoustic scene classification (ASC), are susceptible to noise contamination for real-life applications. To improve audio quality, an enhancement module, which can b
Externí odkaz:
http://arxiv.org/abs/2408.06264