CIS Seminar: “Intrinsic images, lighting and relighting without any labelling”
Intrinsic images are maps of surface properties. A classical problem is to recover an intrinsic image, typically a map of surface lightness, from an image. The topic has mostly […]
CIS Seminar: “Modeling Atoms to Address Our Climate Crisis”
Climate change is a societal and political problem whose impact could be mitigated by technology. Underlying many of its technical challenges is a surprisingly simple yet challenging problem; modeling the […]
ASSET Seminar: “Scaling Your Large Language Models on a Budget” (Atlas Wang, University of Texas at Austin)
ABSTRACT: As the sizes of Large Language Models (LLMs) continue to grow exponentially, it becomes imperative to explore novel computing paradigms that can address the dual challenge of scaling these […]
ASSET Seminar: “Large Language Models in Medicine: Opportunities and Challenges” (Mark Dredze, Johns Hopkins University)
ABSTRACT: The rapid advance of AI driven by Large Language Models (LLMs), like ChatGPT, has led to impressive results across a range of different use cases. This has included several […]
ASSET Seminar: “Enforcing Right to Explanation: Algorithmic Challenges and Opportunities” (Himabindu Lakkaraju, Harvard University)
ABSTRACT: As predictive and generative models are increasingly being deployed in various high-stakes applications in critical domains including healthcare, law, policy and finance, it becomes important to ensure that relevant […]
ASSET Seminar: “Learning to Read X-Ray: Applications to Heart Failure Monitoring” (Polina Golland, Massachusetts Institute of Technology)
ABSTRACT: We propose and demonstrate a novel approach to training image classification models based on large collections of images with limited labels. We take advantage of availability of radiology reports […]
ASSET Seminar: “Robust Machine Learning with Foundation Models” (Aditi Raghunathan, Carnegie Mellon University)
ABSTRACT: In recent years, foundation models—large pretrained models that can be adapted for a wide range of tasks—have achieved state-of-the-art performance on a variety of tasks. While the pretrained models […]
ASSET Seminar: “Inherent Interpretability via Language Model Guided Bottleneck Design” (Mark Yatskar, Penn)
ABSTRACT: As deep learning systems improve, their applicability to critical domains is hampered because of a lack of transparency. Post-hoc explanations attempt to address this concern but they provide no […]
ASSET Seminar: “What Constitutes a Good Explanation?” (Lyle Ungar, Penn)
ABSTRACT: Shapley values and similar methods are widely used to explain the importance of features in model predictions. Clarity in the semantics of these feature importances is subtle, but crucial: […]
ASSET Seminar: “The Future of Algorithm Auditing is Sociotechnical” (Danaë Metaxa, Penn)
ABSTRACT: Algorithm audits are powerful tools for studying black-box systems without direct knowledge of those systems’ inner workings. While they have been effectively deployed to identify harms and biases in […]