Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Sekulić, Ivan"'
Autor:
Müller, Gabriel, Martínez-Lahuerta, V. J., Sekulic, Ivan, Burger, Sven, Schneider, Philipp-Immanuel, Gaaloul, Naceur
State engineering of quantum objects is a central requirement in most implementations. In the cases where the quantum dynamics can be described by analytical solutions or simple approximation models, optimal state preparation protocols have been theo
Externí odkaz:
http://arxiv.org/abs/2404.18234
Conversational information-seeking (CIS) is an emerging paradigm for knowledge acquisition and exploratory search. Traditional web search interfaces enable easy exploration of entities, but this is limited in conversational settings due to the limite
Externí odkaz:
http://arxiv.org/abs/2403.01747
Autor:
Sekulić, Ivan, Terragni, Silvia, Guimarães, Victor, Khau, Nghia, Guedes, Bruna, Filipavicius, Modestas, Manso, André Ferreira, Mathis, Roland
In the realm of dialogue systems, user simulation techniques have emerged as a game-changer, redefining the evaluation and enhancement of task-oriented dialogue (TOD) systems. These methods are crucial for replicating real user interactions, enabling
Externí odkaz:
http://arxiv.org/abs/2402.13374
While the body of research directed towards constructing and generating clarifying questions in mixed-initiative conversational search systems is vast, research aimed at processing and comprehending users' answers to such questions is scarce. To this
Externí odkaz:
http://arxiv.org/abs/2401.11463
Clarifying user's information needs is an essential component of modern search systems. While most of the approaches for constructing clarifying prompts rely on query facets, the impact of the quality of the facets is relatively unexplored. In this w
Externí odkaz:
http://arxiv.org/abs/2401.04524
Autor:
Anton, Oliver, Henderson, Victoria A., Da Ros, Elisa, Sekulic, Ivan, Burger, Sven, Schneider, Philipp-Immanuel, Krutzik, Markus
Publikováno v:
Mach. Learn. Sci. Technol. 5, 025022 (2024)
The generation of cold atom clouds is a complex process which involves the optimization of noisy data in high dimensional parameter spaces. Optimization can be challenging both in and especially outside of the lab due to lack of time, expertise, or a
Externí odkaz:
http://arxiv.org/abs/2312.13397
Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion. However, LLMs can generate irrelevant information that harms retrieval effectiveness
Externí odkaz:
http://arxiv.org/abs/2306.09938
This research aims to explore various methods for assessing user feedback in mixed-initiative conversational search (CS) systems. While CS systems enjoy profuse advancements across multiple aspects, recent research fails to successfully incorporate f
Externí odkaz:
http://arxiv.org/abs/2304.13874
We derive the $\mathcal{T}$-matrix formalism tailored for the numerical analysis of second-harmonic (SH) generation from arbitrarily-shaped particles made of centrosymmetric optical materials. First, the transfer matrix of a single particle is comput
Externí odkaz:
http://arxiv.org/abs/2208.13977
Clarifying the underlying user information need by asking clarifying questions is an important feature of modern conversational search system. However, evaluation of such systems through answering prompted clarifying questions requires significant hu
Externí odkaz:
http://arxiv.org/abs/2204.08046