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pro vyhledávání: '"Korat, Daniel"'
Autor:
Timor, Nadav, Mamou, Jonathan, Korat, Daniel, Berchansky, Moshe, Pereg, Oren, Wasserblat, Moshe, Galanti, Tomer, Gordon, Michal, Harel, David
Accelerating the inference of large language models (LLMs) is an important challenge in artificial intelligence. This paper introduces Distributed Speculative Inference (DSI), a novel distributed inference algorithm that is provably faster than specu
Externí odkaz:
http://arxiv.org/abs/2405.14105
Autor:
Mamou, Jonathan, Pereg, Oren, Korat, Daniel, Berchansky, Moshe, Timor, Nadav, Wasserblat, Moshe, Schwartz, Roy
Speculative decoding is commonly used for reducing the inference latency of large language models. Its effectiveness depends highly on the speculation lookahead (SL)-the number of tokens generated by the draft model at each iteration. In this work we
Externí odkaz:
http://arxiv.org/abs/2405.04304
Autor:
Howard, Phillip, Ma, Arden, Lal, Vasudev, Simoes, Ana Paula, Korat, Daniel, Pereg, Oren, Wasserblat, Moshe, Singer, Gadi
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM 2022). Association for Computing Machinery, New York, NY, USA, 780-790
The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text. Existing approaches for this task have yielded impressive results when the training and testing data are from the same domain. However, these methods show a
Externí odkaz:
http://arxiv.org/abs/2210.10144
Autor:
Tunstall, Lewis, Reimers, Nils, Jo, Unso Eun Seo, Bates, Luke, Korat, Daniel, Wasserblat, Moshe, Pereg, Oren
Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploiting training (PET), have achieved impressive results in label-scarce settings. However, they are difficult to employ since they are subject to high variability
Externí odkaz:
http://arxiv.org/abs/2209.11055
Autor:
Korat, Daniel
With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States. Lung cancer CT screening has been shown to reduce mortality by up to 40% and is now included in US screening guidelines. Reducing the
Externí odkaz:
http://arxiv.org/abs/2007.12898
We present ABSApp, a portable system for weakly-supervised aspect-based sentiment extraction. The system is interpretable and user friendly and does not require labeled training data, hence can be rapidly and cost-effectively used across different do
Externí odkaz:
http://arxiv.org/abs/1909.05608
Autor:
Mamou, Jonathan, Pereg, Oren, Wasserblat, Moshe, Eirew, Alon, Green, Yael, Guskin, Shira, Izsak, Peter, Korat, Daniel
We present SetExpander, a corpus-based system for expanding a seed set of terms into amore complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to-end workflow. It enables users to easily select a seed
Externí odkaz:
http://arxiv.org/abs/1808.08953
Autor:
Mamou, Jonathan, Pereg, Oren, Wasserblat, Moshe, Dagan, Ido, Goldberg, Yoav, Eirew, Alon, Green, Yael, Guskin, Shira, Izsak, Peter, Korat, Daniel
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to end workflow for term set expansion. It enables users
Externí odkaz:
http://arxiv.org/abs/1807.10104