Give us a call or drop by anytime, we endeavor to answer all inquiries within 24 hours.
PO Box 16122 Collins Street West Victoria, Australia
info@domain.com / example@domain.com
Phone: + (066) 0760 0260 / + (057) 0760 0560
Jeremy Bernstein MIT CSAIL Monday, March 31 11:00 AM - 12:00 PM (PST) CSE 1242 Title: Metrized Deep Learning Abstract: We build neural networks in a modular and programmatic way using software libraries like PyTorch and JAX. But optimization theory has not caught up to the flexibility of this paradigm, and practical advances in neural net […]
When Wednesday Feb 5th 2:00pm Where: Computer Science & Engineering (CSE) 1st floor, Seminar Room 1242 Title: Fréchet Regression of Random Objects on Vector Covariates and Its applications for Single Cell RNA-seq Data Analysis Abstract: Population-level single-cell RNA-seq data captures gene expression profiles across thousands of cells from each individual in a sizable cohort. This data facilitates the […]
The UC San Diego EnCORE is excited to host Industry Day on Wednesday, September 4th, 2024, at the UCSD campus. This all-day event will showcase exciting research in foundations of data science, ML systems and AI, with contributions from leading experts in industry and academia. Attendees will have the opportunity to network with entrepreneurs, industry researchers, faculty […]
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.
Abstract: As the intelligence of everyday smart devices continues to evolve, they can already monitor basic health behaviors such as physical activities and heart rates. The vision of an intelligent behavior change intervention pipeline for health -- combining behavior modeling & interaction design -- seems to be within reach. How do we get there? In […]
Abstract: Reasoning, the ability to logically draw conclusions from existing knowledge, has been long pursued as a goal of artificial intelligence. Although numerous learning algorithms have been developed for reasoning, most of them are limited to the domain they are trained on. By contrast, humans often derive high-level rules or principles from experience and apply them to new domains — an ability referred as inductive generalization. In this talk, we present a series of works that learn inductive representations for reasoning over knowledge graphs. First, we introduce Neural Bellman-Ford Networks (NBFNet) that captures paths between entities and can generalize to graphs of new entities. Then we discuss Graph Neural Network Query Executor (GNN-QE), an extension of NBNet that answers multi-hop logical queries and generalizes well on our inductive benchmark. Finally, by learning inductive representations for both entities and relations, we demonstrate that a model can generalize to any graph with arbitrary entity and relation vocabularies, paving the way for foundation models for knowledge graph reasoning.
