Zobrazeno 1 - 10
of 2 075
pro vyhledávání: '"Stathis, P."'
Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically, support responsible decisions, and solve challenging problems. Traditionally, AI has addressed reasoning in the context of lo
Externí odkaz:
http://arxiv.org/abs/2410.05339
Game theory is a powerful framework for reasoning about strategic interactions, with applications in domains ranging from day-to-day life to international politics. However, applying formal reasoning tools in such contexts is challenging, as these sc
Externí odkaz:
http://arxiv.org/abs/2409.12300
Autor:
Megas, Stathis, Chen, Daniel G., Polanski, Krzysztof, Eliasof, Moshe, Schonlieb, Carola-Bibiane, Teichmann, Sarah A.
Celcomen leverages a mathematical causality framework to disentangle intra- and inter- cellular gene regulation programs in spatial transcriptomics and single-cell data through a generative graph neural network. It can learn gene-gene interactions, a
Externí odkaz:
http://arxiv.org/abs/2409.05804
We introduce the Logic-Enhanced Language Model Agents (LELMA) framework, a novel approach to enhance the trustworthiness of social simulations that utilize large language models (LLMs). While LLMs have gained attention as agents for simulating human
Externí odkaz:
http://arxiv.org/abs/2408.16081
We provide a one-to-one correspondence between line operators and states in four-dimensional CFTs with continuous 1-form symmetries. In analogy with 0-form symmetries in two dimensions, such CFTs have a free photon realisation and enjoy an infinite-d
Externí odkaz:
http://arxiv.org/abs/2406.02662
The remarkable progress in 3D face reconstruction has resulted in high-detail and photorealistic facial representations. Recently, Diffusion Models have revolutionized the capabilities of generative methods by surpassing the performance of GANs. In t
Externí odkaz:
http://arxiv.org/abs/2312.04465
Autor:
Bagga, Pallavi, Stathis, Kostas
Publikováno v:
Workshop Workshop on Explainable AI in Finance, November 27, 2023, ACM, New York, USA
This paper bridges the gap between mathematical heuristic strategies learned from Deep Reinforcement Learning (DRL) in automated agent negotiation, and comprehensible, natural language explanations. Our aim is to make these strategies more accessible
Externí odkaz:
http://arxiv.org/abs/2311.14061
We consider the entanglement entropy arising from edge-modes in Abelian $p$-form topological field theories in $d$ dimensions on arbitrary spatial topology and across arbitrary entangling surfaces. We find a series of descending area laws plus univer
Externí odkaz:
http://arxiv.org/abs/2310.18391
Autor:
Makris, N., Ntanos, A., Papageorgopoulos, A., Stathis, A., Konteli, P., Tsoni, I., Giannoulis, G., Setaki, F., Stathopoulos, T., Lyberopoulos, G., Avramopoulos, H., Kanellos, G. T., Syvridis, D.
We have successfully integrated an O-band commercial Quantum-Key-Distribution (QKD) system over a lit GPON testbed that replicates a carrier-grade Fiber-to-the-Home (FTTH) optical access network with multiple ONTs to emulate real-life FTTH operationa
Externí odkaz:
http://arxiv.org/abs/2310.17259
The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal textual entailment. We present an analysis of GPT-3.5 (ChatGPT) and GPT-4
Externí odkaz:
http://arxiv.org/abs/2309.05501