nlp

Reem Alzahrani

Ph.D. Student

Deep learning nlp Computer Vision Large Language Models vision language models Multi-Modal Large Language Models

Reem Alzahrani is an MS/PhD student at KAUST, working on Large Language Models and Vision-Language Models. Her research focuses on identifying and addressing the limitations of AI systems to improve their robustness and reliability.

Yimeng Chen

Postdoctoral Research Fellow, Computer Science

machine learning nlp deep learning statistics computational probability Python

Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

Dr.Syed Adnan Yusuf

Nov 28, 12:00 - 13:00

B9 L2 R2322 H1

Computer Vision robotics automation IoT nlp artificial intelligence

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

The Prospect of Generative Language Processing

Prof. Muhammad Abdul-Mageed

Nov 6, 11:30 - 12:30

B9 L2 H2 H2

nlp generative language processing artificial intelligence

In the evolving landscape of artificial intelligence, generative models are revolutionizing our interface with computational systems and reshaping societal paradigms. For example, foundation models have the potential to transform content creation across languages, offering discovery and productivity pathways for humans to engage with one another and their environment. This talk sketches the core methodologies propelling this groundbreaking progress, charting a grand vision for generative natural language processing.

Improving Interpretation Faithfulness for Transformers

Di Wang, Assistant Professor, Computer Science

Nov 20, 11:30 - 12:30

B9 L2 H2 H2

transformers nlp interpretation faithfulness

Currently, attention mechanism becomes a standard fixture in most state-of-the-art NLP, Vision and GNN models, not only due to outstanding performance it could gain, but also due to plausible innate explanation for the behaviors of neural architectures it provides, which is notoriously difficult to analyze. However, recent studies show that attention is unstable against randomness and perturbations during training or testing, such as random seeds and slight perturbation of input or embedding vectors, which impedes it from becoming a faithful explanation tool. Thus, a natural question is whether we can find some substitute of the current attention which is more stable and could keep the most important characteristics on explanation and prediction of attention.