Zobrazeno 1 - 10
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pro vyhledávání: '"Hosseini, Mohammad"'
Segmenting text into fine-grained units of meaning is important to a wide range of NLP applications. The default approach of segmenting text into sentences is often insufficient, especially since sentences are usually complex enough to include multip
Externí odkaz:
http://arxiv.org/abs/2406.19803
Natural Language Inference (NLI) remains an important benchmark task for LLMs. NLI datasets are a springboard for transfer learning to other semantic tasks, and NLI models are standard tools for identifying the faithfulness of model-generated text. T
Externí odkaz:
http://arxiv.org/abs/2402.12368
Autor:
Hosseini, Mohammad, Hasan, Mahmudul
To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy
Externí odkaz:
http://arxiv.org/abs/2309.05150
Autor:
Siska, Emily, Smith, G. Alexander, Villa-Cortes, Sergio, Conway, Lewis J., Husband, Rachel J., Van Cleave, Joshua, Petitgirard, Sylvain, Cerantola, Valerio, Appel, Karen, Baehtz, Carsten, Bouffetier, Victorien, Dwiwedi, Anand, Göde, Sebastian, Gorkhover, Taisia, Konopkova, Zuzana, Hosseini, Mohammad, Kuschel, Stephan, Laurus, Torsten, Nakatsutsumi, Motoaki, Strohm, Cornelius, Sztuk-Dambietz, Jolanta, Zastrau, Ulf, Smith, Dean, Lawler, Keith V., Pickard, Chris J., Schwartz, Craig P., Salamat, Ashkan
Controlling the formation and stoichiometric content of desired phases of materials has become a central interest for the study of a variety of fields, notably high temperature superconductivity under extreme pressures. The further possibility of acc
Externí odkaz:
http://arxiv.org/abs/2307.11293
Autor:
Milbauer, Jeremiah, Louis, Annie, Hosseini, Mohammad Javad, Fabrikant, Alex, Metzler, Donald, Schuster, Tal
Transformer encoders contextualize token representations by attending to all other tokens at each layer, leading to quadratic increase in compute effort with the input length. In practice, however, the input text of many NLP tasks can be seen as a se
Externí odkaz:
http://arxiv.org/abs/2305.19585
Autor:
McKenna, Nick, Li, Tianyi, Cheng, Liang, Hosseini, Mohammad Javad, Johnson, Mark, Steedman, Mark
Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization. We present a series of behavioral studies on several LLM families (LLaMA, GPT-3.5, and
Externí odkaz:
http://arxiv.org/abs/2305.14552
Publikováno v:
Journal of Real-Time Image Processing. 2022 Jun;19(3):623-38
In most image retrieval systems, images include various high-level semantics, called tags or annotations. Virtually all the state-of-the-art image annotation methods that handle imbalanced labeling are search-based techniques which are time-consuming
Externí odkaz:
http://arxiv.org/abs/2304.06907
Recent advances in language modeling have enabled new conversational systems. In particular, it is often desirable for people to make choices among specified options when using such systems. We address this problem of reference resolution, when peopl
Externí odkaz:
http://arxiv.org/abs/2212.10933
Autor:
Malekie, Shahryar1,2 smaleki@aeoi.org.ir, Hosseini, Mohammad Amin3,4, Abiz, Ahmadreza5, Bolourinovin, Fatemeh1, Tajudin, Suffian Mohamad2
Publikováno v:
Polyolefins Journal. Aug2024, Vol. 11 Issue 3, p199-211. 13p.