Zobrazeno 1 - 10
of 15 764
pro vyhledávání: '"Gittens AT"'
Publikováno v:
Harvard Law Review, 1999 May 01. 112(7), 1789-1794.
Externí odkaz:
https://www.jstor.org/stable/1342421
Knowledge graph (KG) completion aims to identify additional facts that can be inferred from the existing facts in the KG. Recent developments in this field have explored this task in the inductive setting, where at test time one sees entities that we
Externí odkaz:
http://arxiv.org/abs/2410.00876
Missing data is commonly encountered in practice, and when the missingness is non-ignorable, effective remediation depends on knowledge of the missingness mechanism. Learning the underlying missingness mechanism from the data is not possible in gener
Externí odkaz:
http://arxiv.org/abs/2409.04407
Autor:
Rathnashyam, Arvind, Gittens, Alex
We derive approximation bounds for learning single neuron models using thresholded gradient descent when both the labels and the covariates are possibly corrupted adversarially. We assume the data follows the model $y = \sigma(\mathbf{w}^{*} \cdot \m
Externí odkaz:
http://arxiv.org/abs/2409.03703
Publikováno v:
Nicotine & Tobacco Research, 2013 Oct 01. 15(10), 1794-1795.
Externí odkaz:
https://www.jstor.org/stable/26765905
Autor:
Ngweta, Lilian, Agarwal, Mayank, Maity, Subha, Gittens, Alex, Sun, Yuekai, Yurochkin, Mikhail
Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications. Alignment is challenging, costly, and needs to be repeated for every LLM and alignment criterion. We propose to decouple
Externí odkaz:
http://arxiv.org/abs/2403.04224
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-16 (2024)
Abstract Transcription generates superhelical stress in DNA that poses problems for genome stability, but determining when and where such stress arises within chromosomes is challenging. Here, using G1-arrested S. cerevisiae cells, and employing rapi
Externí odkaz:
https://doaj.org/article/ad0e51d0d35b4319b2b87cad8f49db70
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions. Nevertheless, they are computationally expensive to creat
Externí odkaz:
http://arxiv.org/abs/2308.15027
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation. Although this task is important for language understanding and knowledge discovery, recent works in this domain have largely
Externí odkaz:
http://arxiv.org/abs/2308.03891