10 citations · 22 across the 8 of their papers we have counts for
8 papers
Does Synthetic Data Make Large Language Models More Efficient?
Sia Gholami, Marwan Omar
Natural Language Processing (NLP) has undergone transformative changes with the advent of deep learning methodologies. One challenge persistently confronting researchers is the sca…
Can pruning make Large Language Models more efficient?
Sia Gholami, Marwan Omar
Transformer models have revolutionized natural language processing with their unparalleled ability to grasp complex contextual relationships. However, the vast number of parameters…
Can a student Large Language Model perform as well as it's teacher?
Sia Gholami, Marwan Omar
The burgeoning complexity of contemporary deep learning models, while achieving unparalleled accuracy, has inadvertently introduced deployment challenges in resource-constrained en…
Do Generative Large Language Models need billions of parameters?
Sia Gholami, Marwan Omar
This paper presents novel systems and methodologies for the development of efficient large language models (LLMs). It explores the trade-offs between model size, performance, and c…
Framework for 2D Ad placements in LinearTV
Divya Bhargavi, Karan Sindwani, Sia Gholami
Virtual Product placement(VPP) is the advertising technique of digitally placing a branded object into the scene of a movie or TV show. This type of advertising provides the abilit…
Alexa, Predict My Flight Delay
Sia Gholami, Saba Khashe
Airlines are critical today for carrying people and commodities on time. Any delay in the schedule of these planes can potentially disrupt the business and trade of thousands of em…
Knock, knock. Who's there? -- Identifying football player jersey numbers with synthetic data
Divya Bhargavi, Erika Pelaez Coyotl, Sia Gholami
Automatic player identification is an essential and complex task in sports video analysis. Different strategies have been devised over the years, but identification based on jersey…
Zero-Shot Open-Book Question Answering
Sia Gholami, Mehdi Noori
Open book question answering is a subset of question answering tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topi…