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HDSI

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LIVED EXPERIENCE RESEARCH SUMMIT

Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

Location: HDSI MPR To watch via zoom please contact: hdsiassistant@ucsd.edu Formerly Incarcerated professor speaking on his lived experience in research. Researchers from Smarr Lab and MOSAIC lab. Noel Vest, PhD, is an Assistant Professor at the Boston University School of Public Health. His research interests include mental health, substance use disorders, and addiction recovery. As […]

Special TILOS Seminar: Claire Boyer | Single location regression and attention-based models

Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

Talk Information Speaker: Claire Boyer (Université Paris-Saclay) Date & Time: Thursday, March 27 @ 2pm PDT Venue: HDSI 123 Title: Single location regression and attention-based models Abstract: Attention-based models, such as Transformer, excel across various tasks but lack a comprehensive theoretical understanding, especially regarding token-wise sparsity and internal linear representations. To address this gap, we introduce the single-location regression task, where […]

MathWorks Technical Seminar: AI & Machine Learning in Real-World Systems

Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

Beyond their use in traditional data analytics problems, AI and Machine Learning techniques are changing the way real-world complex systems (like vehicles, airplanes, and even industrial production lines) are designed, tested and fabricated. When building these systems, engineers rely heavily on modeling and computer-assisted simulations. This seminar showcases how MATLAB and Simulink can help integrate […]

Causality Workshop

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States +1 more
Event Series Special Seminar Series

“Instance-Optimization: Rethinking Database Design for the Next 1000X” | Jialin Ding

Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

Abstract: "Modern database systems aim to support a large class of different use cases while simultaneously achieving high performance. However, as a result of their generality, databases often achieve adequate performance for the average use case but do not achieve the best performance for any individual use case. In this talk, I will describe my […]

The Emergence of Reproducibility and Generalizability in Diffusion Models | Qing Qu

Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

Abstract: We reveal an intriguing and prevalent phenomenon of diffusion models which we term as ``consistent model reproducibility'': given the same starting noise input and a deterministic sampler, different diffusion models often yield remarkably similar outputs while they generate new samples. We demonstrate this phenomenon through comprehensive experiments and theoretical studies, implying that different diffusion […]

Event Series Special Seminar Series

Towards a Machine Capable of Learning Everything | Hao Liu

Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

Abstract: Large generative models such as ChatGPT have led to amazing results and revolutionized artificial intelligence. In this talk, I will discuss my research on advancing the foundation of these models, centered around addressing the architectural bottlenecks of learning from everything. First, I will describe our efforts to remove context size limitations of the transformer […]

Event Series Special Seminar Series

Making machine learning predictably reliable | Andrew Ilyas

Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

Abstract: "Despite ML models' impressive performance, training and deploying them is currently a somewhat messy endeavor. But does it have to be? In this talk, I overview my work on making ML “predictably reliable”---enabling developers to know when their models will work, when they will fail, and why.

To begin, we use a case study of adversarial inputs to show that human intuition can be a poor predictor of how ML models operate. Motivated by this, we present a line of work that aims to develop a precise understanding of the ML pipeline, combining statistical tools with large-scale experiments to characterize the role of each individual design choice: from how to collect data, to what dataset to train on, to what learning algorithm to use."

Event Series Special Seminar Series

Efficient Deep Learning with Sparsity: Algorithms, Systems, and Applications | Zhijian Liu

Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

Abstract: Deep learning is used across a broad spectrum of applications. However, behind its remarkable performance lies an increasing gap between the demand for and supply of computation. On the demand side, the computational costs of deep learning models have surged dramatically, driven by ever-larger input and model sizes. On the supply side, as Moore's […]