10 citations · 26 across the 6 of their papers we have counts for
6 papers
GneissWeb: Preparing High Quality Data for LLMs at Scale
Hajar Emami Gohari, Swanand Ravindra Kadhe, Syed Yousaf Shah +29
Data quantity and quality play a vital role in determining the performance of Large Language Models (LLMs). High-quality data, in particular, can significantly boost the LLM's abil…
Data-Prep-Kit: getting your data ready for LLM application development
David Wood, Boris Lublinsky, Alexy Roytman +21
Data preparation is the first and a very important step towards any Large Language Model (LLM) development. This paper introduces an easy-to-use, extensible, and scale-flexible ope…
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Mayank Mishra, Matt Stallone, Gaoyuan Zhang +43
Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environmen…
Modality-aware Transformer for Financial Time series Forecasting
Hajar Emami, Xuan-Hong Dang, Yousaf Shah +1
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 prev…
AutoAI-TS: AutoAI for Time Series Forecasting
Syed Yousaf Shah, Dhaval Patel, Long Vu +10
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 literat…
seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention
Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos
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 managem…