Zobrazeno 1 - 10
of 8 091
pro vyhledávání: '"Catanzaro A"'
State-of-the-art retrieval models typically address a straightforward search scenario, where retrieval tasks are fixed (e.g., finding a passage to answer a specific question) and only a single modality is supported for both queries and retrieved resu
Externí odkaz:
http://arxiv.org/abs/2411.02571
Autor:
Alonso-Santiago, J., Frasca, A., Bragaglia, A., Catanzaro, G., Fu, X., Andreuzzi, G., Magrini, L., Lucatello, S., Vallenari, A., Jian, M.
The Radcliffe Wave has only recently been recognised as a about 3 kpc long coherent gas structure encompassing most of the star forming regions in the solar vicinity. Since its discovery, it has been mainly studied from the perspective of dynamics, b
Externí odkaz:
http://arxiv.org/abs/2410.14373
Large Language Models (LLMs) have made significant strides in text generation and comprehension, with recent advancements extending into multimodal LLMs that integrate visual and audio inputs. However, these models continue to struggle with fine-grai
Externí odkaz:
http://arxiv.org/abs/2410.12109
Autor:
Akter, Syeda Nahida, Prabhumoye, Shrimai, Kamalu, John, Satheesh, Sanjeev, Nyberg, Eric, Patwary, Mostofa, Shoeybi, Mohammad, Catanzaro, Bryan
The utility of synthetic data to enhance pretraining data quality and hence to improve downstream task accuracy has been widely explored in recent large language models (LLMs). Yet, these approaches fall inadequate in complex, multi-hop and mathemati
Externí odkaz:
http://arxiv.org/abs/2410.12881
Autor:
He, Ethan, Khattar, Abhinav, Prenger, Ryan, Korthikanti, Vijay, Yan, Zijie, Liu, Tong, Fan, Shiqing, Aithal, Ashwath, Shoeybi, Mohammad, Catanzaro, Bryan
Upcycling pre-trained dense language models into sparse mixture-of-experts (MoE) models is an efficient approach to increase the model capacity of already trained models. However, optimal techniques for upcycling at scale remain unclear. In this work
Externí odkaz:
http://arxiv.org/abs/2410.07524
Autor:
Ranzinger, Mike, Barker, Jon, Heinrich, Greg, Molchanov, Pavlo, Catanzaro, Bryan, Tao, Andrew
Various visual foundation models have distinct strengths and weaknesses, both of which can be improved through heterogeneous multi-teacher knowledge distillation without labels, termed "agglomerative models." We build upon this body of work by studyi
Externí odkaz:
http://arxiv.org/abs/2410.01680
We present Synthio, a novel approach for augmenting small-scale audio classification datasets with synthetic data. Our goal is to improve audio classification accuracy with limited labeled data. Traditional data augmentation techniques, which apply a
Externí odkaz:
http://arxiv.org/abs/2410.02056
Autor:
Dai, Wenliang, Lee, Nayeon, Wang, Boxin, Yang, Zhuolin, Liu, Zihan, Barker, Jon, Rintamaki, Tuomas, Shoeybi, Mohammad, Catanzaro, Bryan, Ping, Wei
We introduce NVLM 1.0, a family of frontier-class multimodal large language models (LLMs) that achieve state-of-the-art results on vision-language tasks, rivaling the leading proprietary models (e.g., GPT-4o) and open-access models (e.g., Llama 3-V 4
Externí odkaz:
http://arxiv.org/abs/2409.11402
To gain better understanding of the Ap stars with the longest rotation periods, we obtained high resolution spectra of a sample of super-slowly rotating Ap (ssrAp) star candidates identified by a TESS photometric survey, to confirm that they are inde
Externí odkaz:
http://arxiv.org/abs/2409.08060
Autor:
Catanzaro, Michael
Publikováno v:
The New Atlantis, 2024 Oct 01(78), 33-39.
Externí odkaz:
https://www.jstor.org/stable/27332595
