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Music is intertwined with human emotion, memory, and identity, making it a powerful medium for affective experience and regulation.
An AI Measurement Science (AIMS) Seminar Series
An AI Measurement Science (AIMS) Seminar Series
Clinical Mind AI is a Stanford-developed, international collaborative platform designed to strengthen clinical reasoning education through AI-simulated patient encounters across health professions.
Clinical Mind AI is a Stanford-developed, international collaborative platform designed to strengthen clinical reasoning education through AI-simulated patient encounters across health professions.
Our November session is Scaling Training Across GPUs and Nodes with speaker Zoe Ryan. This session focuses on efficient strategies for using multiple GPUs and nodes. We will discuss how to deploy strategies of data parallelism and model parallelism to scale to multiple GPUs through PyTorch, enabling faster training times and better model performance.
Location: CoDa W401 & Zoom
Refreshments and snacks provided
Please follow the link to register
Our November session is Scaling Training Across GPUs and Nodes with speaker Zoe Ryan. This session focuses on efficient strategies for using multiple GPUs and nodes. We will discuss how to deploy strategies of data parallelism and model parallelism to scale to multiple GPUs through PyTorch, enabling faster training times and better model performance.
Location: CoDa W401 & Zoom
Refreshments and snacks provided
Please follow the link to register
This has motivated decades of research in music emotion recognition (MER), aiming to model emotional responses to music using computational methods. However, emotional responses to music are not fixed properties of the signal but emerge from interactions between musical structure, listener background, cultural context, and situational factors. As a result, traditional MER approaches that rely on averaged labels or universal ground truth struggle to capture the diversity and subjectivity of emotional experiences.
This talk argues for a human-centered perspective on MER that treats subjectivity not as noise but as a core signal. I discuss methodological challenges in constructing meaningful ground truth, including inter-annotator disagreement, contextual dependence, and personalization, as well as ethical concerns related to the use of emotion in a political context, and potential misuse.
