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pro vyhledávání: '"A. Altmeyer"'
Autor:
Gala, Gautam, Unte, Tilmann, Maia, Luiz, Kühbacher, Johannes, Kadusale, Isser, Alkoudsi, Mohammad Ibrahim, Fohler, Gerhard, Altmeyer, Sebastian
Publikováno v:
3rd International Workshop on Real-Time Cloud systems (RT-Cloud), held in conjunction with the 36th Euromicro Conference on Real-time Systems (ECRTS) 2024
Edge computing processes data near its source, reducing latency and enhancing security compared to traditional cloud computing while providing its benefits. This paper explores edge computing for migrating an existing safety-critical robotics use cas
Externí odkaz:
http://arxiv.org/abs/2406.14391
Autor:
Altmeyer, A.J.
Publikováno v:
Vital Speeches of the Day. Mar2001, Vol. 67 Issue 10, p317. 4p.
Akademický článek
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We present Conformal Intent Classification and Clarification (CICC), a framework for fast and accurate intent classification for task-oriented dialogue systems. The framework turns heuristic uncertainty scores of any intent classifier into a clarific
Externí odkaz:
http://arxiv.org/abs/2403.18973
Developments in the field of Artificial Intelligence (AI), and particularly large language models (LLMs), have created a 'perfect storm' for observing 'sparks' of Artificial General Intelligence (AGI) that are spurious. Like simpler models, LLMs dist
Externí odkaz:
http://arxiv.org/abs/2402.03962
Counterfactual explanations offer an intuitive and straightforward way to explain black-box models and offer algorithmic recourse to individuals. To address the need for plausible explanations, existing work has primarily relied on surrogate models t
Externí odkaz:
http://arxiv.org/abs/2312.10648
Autor:
Altmeyer, Thomas H.
Publikováno v:
Vital Speeches of the Day. 12/1/94, Vol. 61 Issue 4, p112. 5p.
Autor:
Altmeyer, Patrick, Angela, Giovan, Buszydlik, Aleksander, Dobiczek, Karol, van Deursen, Arie, Liem, Cynthia C. S.
Publikováno v:
in 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Raleigh, NC, USA, 2023 pp. 418-431
Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment: given some estimated model, the goal is to find valid counterfactuals for an individual instance that f
Externí odkaz:
http://arxiv.org/abs/2308.08187
Publikováno v:
JuliaCon Proceedings, 1(1), 130 (2023)
We present CounterfactualExplanations.jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia. CE explain how inputs into a model need to change to yield specific model predictions. Ex
Externí odkaz:
http://arxiv.org/abs/2308.07198