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HDSI! Come and join MathWorks Engineers for a technical seminar on AI (and lunch!) on Wednesday, April 3! Come learn why data scientists should learn MATLAB – we will highlight the tools that will be serve your role as data scientists and data science students. You can also learn about our engineer’s journey, roles available […]
Abstract: "In biomedical data analysis, an emerging trend focuses on contextualizing observations within biological and real-world processes. This approach facilitates high-resolution, context-specific insights by integrating information across datasets, but it is difficult to design systems which both share information and dynamically adapt to context. Toward this aim, this presentation will examine “contextualized learning”, a meta-learning […]
Abstract: "Effective communication is crucial in healthcare for ensuring successful clinical interactions, as it affects how patients respond, the decisions being made by both patients and clinicians, and the outcomes of treatments. Recent developments in Natural Language Processing (NLP) aim to improve and support these interactions within clinical settings. In this talk, I will discuss […]
Abstract: Images are everywhere, especially images of the real world, and visual computing is important both for reconstructing useful models from these images and for providing us humans with interactive tools for visualization and analysis. These methods aid real world sensing and measurement, scientific and medical imaging, and media and the arts. The past three […]
Abstract: In this talk, I will present my work on developing and responsibly deploying generative AI systems that unlock and augment human creative potential in music. While we all possess a remarkably sophisticated intuition for and appreciation of music, conventional tools for creative musical expression (e.g., instruments, music notation) are inaccessible to those of us […]
Registration Required through 12Twenty: Join a panel of HDSI undergraduate and graduate students discussing their internship expectations and realities. Learn about their experiences finding roles, navigating the hiring process, lessons learned, and skills developed. There will also be plenty of time for Q&A. Don't miss this opportunity to gain valuable insights from your peers!
Transformers learn in-context by (functional) gradient descent Xiang Cheng, TILOS Postdoctoral Scholar at MIT HDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002 Abstract: Motivated by the in-context learning phenomenon, we investigate how the Transformer neural network can implement learning algorithms in its forward pass. We show that a linear Transformer naturally learns to implement gradient descent, which enables it to […]
The applications of large language models (LLMs) are increasingly complex and diverse, necessitating efficient and reliable frameworks for building and deploying them. In this talk, I will begin with algorithms and systems for serving LLMs for everyone (FlexGen, S-LoRA, VTC), highlighting the growing trend of personalized LLM services. My work addresses the need to run LLMs locally for isolated individual needs. It also tackles the problem of efficiency and service fairness when resource sharing among many users is required. Once we have efficient deployment, a primary concern is the reliability of generation. The second part of this talk aims to address this issue by exploring verifiable code generation. To achieve this, I adopt tools in formal verification to facilitate LLMs in generating correctness certificates alongside other artifacts (Clover). Finally, I will touch on future research avenues, such as integrating formal methods with LLMs and developing programming systems for generative AI.