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A model's capacity to generalize its knowledge to interpret unseen inputs with different characteristics is crucial to build robust and reliable machine learning systems. Language model evaluation tasks lack information metrics about model generaliza
Externí odkaz:
http://arxiv.org/abs/2404.15928
The SOTA in transcription of disfluent and conversational speech has in recent years favored two-stage models, with separate transcription and cleaning stages. We believe that previous attempts at end-to-end disfluency removal have fallen short becau
Externí odkaz:
http://arxiv.org/abs/2309.04516
Autor:
Agarwal, Aayush, Bassi, Saksham
The scarcity of high-dimensional causal inference datasets restricts the exploration of complex deep models. In this work, we propose a method to generate a synthetic causal dataset that is high-dimensional. The synthetic data simulates a causal effe
Externí odkaz:
http://arxiv.org/abs/2303.00821
Akademický článek
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Publikováno v:
International Journal of Advances in Engineering Sciences & Applied Mathematics; Sep2019, Vol. 11 Issue 3, p230-235, 6p
