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pro vyhledávání: '"Vaska, Nathan"'
For the task of image classification, neural networks primarily rely on visual patterns. In robust networks, we would expect for visually similar classes to be represented similarly. We consider the problem of when semantically similar classes are vi
Externí odkaz:
http://arxiv.org/abs/2306.01148
Autor:
Vaska, Nathan, Helus, Victoria
The impressive advances and applications of large language and joint language-and-visual understanding models has led to an increased need for methods of probing their potential reasoning capabilities. However, the difficulty of gather naturally-occu
Externí odkaz:
http://arxiv.org/abs/2306.01144
Robustness in deep neural networks and machine learning algorithms in general is an open research challenge. In particular, it is difficult to ensure algorithmic performance is maintained on out-of-distribution inputs or anomalous instances that cann
Externí odkaz:
http://arxiv.org/abs/2211.11880
Many real-world tasks involve a mixed-initiative setup, wherein humans and AI systems collaboratively perform a task. While significant work has been conducted towards enabling humans to specify, through language, exactly how an agent should complete
Externí odkaz:
http://arxiv.org/abs/2208.08374
Increasing the semantic understanding and contextual awareness of machine learning models is important for improving robustness and reducing susceptibility to data shifts. In this work, we leverage contextual awareness for the anomaly detection probl
Externí odkaz:
http://arxiv.org/abs/2203.09354
Effective Human-AI teaming requires the ability to communicate the goals of the team and constraints under which you need the agent to operate. Providing the ability to specify the shared intent or operation criteria of the team can enable an AI agen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ac169974f6808274a239636545f7f77