Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Dang Xuan Hong"'
Autor:
Stallone, Matt, Saxena, Vaibhav, Karlinsky, Leonid, McGinn, Bridget, Bula, Tim, Mishra, Mayank, Soria, Adriana Meza, Zhang, Gaoyuan, Prasad, Aditya, Shen, Yikang, Surendran, Saptha, Guttula, Shanmukha, Patel, Hima, Selvam, Parameswaran, Dang, Xuan-Hong, Koyfman, Yan, Sood, Atin, Feris, Rogerio, Desai, Nirmit, Cox, David D., Puri, Ruchir, Panda, Rameswar
This paper introduces long-context Granite code models that support effective context windows of up to 128K tokens. Our solution for scaling context length of Granite 3B/8B code models from 2K/4K to 128K consists of a light-weight continual pretraini
Externí odkaz:
http://arxiv.org/abs/2407.13739
Autor:
Mishra, Mayank, Stallone, Matt, Zhang, Gaoyuan, Shen, Yikang, Prasad, Aditya, Soria, Adriana Meza, Merler, Michele, Selvam, Parameswaran, Surendran, Saptha, Singh, Shivdeep, Sethi, Manish, Dang, Xuan-Hong, Li, Pengyuan, Wu, Kun-Lung, Zawad, Syed, Coleman, Andrew, White, Matthew, Lewis, Mark, Pavuluri, Raju, Koyfman, Yan, Lublinsky, Boris, de Bayser, Maximilien, Abdelaziz, Ibrahim, Basu, Kinjal, Agarwal, Mayank, Zhou, Yi, Johnson, Chris, Goyal, Aanchal, Patel, Hima, Shah, Yousaf, Zerfos, Petros, Ludwig, Heiko, Munawar, Asim, Crouse, Maxwell, Kapanipathi, Pavan, Salaria, Shweta, Calio, Bob, Wen, Sophia, Seelam, Seetharami, Belgodere, Brian, Fonseca, Carlos, Singhee, Amith, Desai, Nirmit, Cox, David D., Puri, Ruchir, Panda, Rameswar
Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based age
Externí odkaz:
http://arxiv.org/abs/2405.04324
Time series forecasting presents a significant challenge, particularly when its accuracy relies on external data sources rather than solely on historical values. This issue is prevalent in the financial sector, where the future behavior of time serie
Externí odkaz:
http://arxiv.org/abs/2310.01232
The most effective domain adaptation (DA) involves the decomposition of data representation into a domain independent representation (DIRep), and a domain dependent representation (DDRep). A classifier is trained by using the DIRep of the labeled sou
Externí odkaz:
http://arxiv.org/abs/2306.00262
Autor:
Shah, Syed Yousaf, Patel, Dhaval, Vu, Long, Dang, Xuan-Hong, Chen, Bei, Kirchner, Peter, Samulowitz, Horst, Wood, David, Bramble, Gregory, Gifford, Wesley M., Ganapavarapu, Giridhar, Vaculin, Roman, Zerfos, Petros
A large number of time series forecasting models including traditional statistical models, machine learning models and more recently deep learning have been proposed in the literature. However, choosing the right model along with good parameter value
Externí odkaz:
http://arxiv.org/abs/2102.12347
Multimodal analysis that uses numerical time series and textual corpora as input data sources is becoming a promising approach, especially in the financial industry. However, the main focus of such analysis has been on achieving high prediction accur
Externí odkaz:
http://arxiv.org/abs/1912.10858
seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention
Discovering temporal lagged and inter-dependencies in multivariate time series data is an important task. However, in many real-world applications, such as commercial cloud management, manufacturing predictive maintenance, and portfolios performance
Externí odkaz:
http://arxiv.org/abs/1812.04448
Detecting a small number of outliers from a set of data observations is always challenging. This problem is more difficult in the setting of multiple network samples, where computing the anomalous degree of a network sample is generally not sufficien
Externí odkaz:
http://arxiv.org/abs/1610.00054
Modeling information that resides on vertices of large graphs is a key problem in several real-life applications, ranging from social networks to the Internet-of-things. Signal Processing on Graphs and, in particular, graph wavelets can exploit the i
Externí odkaz:
http://arxiv.org/abs/1602.03320
Data mining practitioners are facing challenges from data with network structure. In this paper, we address a specific class of global-state networks which comprises of a set of network instances sharing a similar structure yet having different value
Externí odkaz:
http://arxiv.org/abs/1512.06173