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of 340
pro vyhledávání: '"LIPPI, MARCO"'
Artificial intelligence is continuously seeking novel challenges and benchmarks to effectively measure performance and to advance the state-of-the-art. In this paper we introduce KANDY, a benchmarking framework that can be used to generate a variety
Externí odkaz:
http://arxiv.org/abs/2402.17431
The paper reports the results of an experiment aimed at testing to what extent ChatGPT 3.5 and 4 is able to answer questions regarding privacy policies designed in the new format that we propose. In a world of human-only interpreters, there was a tra
Externí odkaz:
http://arxiv.org/abs/2402.00013
The paper deals with the construction of a synthetic indicator of economic growth, obtained by projecting a quarterly measure of aggregate economic activity, namely gross domestic product (GDP), into the space spanned by a finite number of smooth pri
Externí odkaz:
http://arxiv.org/abs/2305.06618
High-Dimensional Dynamic Factor Models are presented in detail: The main assumptions and their motivation, main results, illustrations by means of elementary examples. In particular, the role of singular ARMA models in the theory and applications of
Externí odkaz:
http://arxiv.org/abs/2202.07745
The increasing complexity and unpredictability of many ICT scenarios let us envision that future systems will have to dynamically learn how to act and adapt to face evolving situations with little or no a priori knowledge, both at the level of indivi
Externí odkaz:
http://arxiv.org/abs/2109.11223
We propose a novel architecture for Graph Neural Networks that is inspired by the idea behind Tree Kernels of measuring similarity between trees by taking into account their common substructures, named fragments. By imposing a series of regularizatio
Externí odkaz:
http://arxiv.org/abs/2110.00124
Many NLP applications require models to be interpretable. However, many successful neural architectures, including transformers, still lack effective interpretation methods. A possible solution could rely on building explanations from domain knowledg
Externí odkaz:
http://arxiv.org/abs/2110.00125
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol 31, pp 1877-1892, 2023
We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argumen
Externí odkaz:
http://arxiv.org/abs/2102.12227
Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain us
Externí odkaz:
http://arxiv.org/abs/2008.07346
Publikováno v:
Future Generation Computer Systems 105 (2020) 275-286
As ML applications are becoming ever more pervasive, fully-trained systems are made increasingly available to a wide public, allowing end-users to submit queries with their own data, and to efficiently retrieve results. With increasingly sophisticate
Externí odkaz:
http://arxiv.org/abs/2005.14080