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Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge
Brains, Minds + Machines Seminar Series: Feedforward and feedback processes in visual recognition
Title: Feedforward and feedback processes in visual recognition
Abstract: Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity and spatial relation judgments. Our group has developed a recurrent network model of classical and extra-classical receptive fields that is constrained by the anatomy and physiology of the visual cortex. The model was shown to account for diverse visual illusions providing computational evidence for a novel canonical circuit that is shared across visual modalities. I will show that this computational neuroscience model can be turned into a modern end-to-end trainable deep recurrent network architecture which addresses some of the shortcomings exhibited by state-of-the-art feedforward networks for solving complex visual reasoning tasks. This suggests that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.
Organizer: Hector Penagos Organizer Email: cbmm-contact@mit.eduUniversal and Non-universal Features of Musical Pitch Perception Revealed by Singing
CBMM Special Seminar: Beyond Empirical Risk Minimization: the lessons of deep learning
Title: Beyond Empirical Risk Minimization: the lessons of deep learning
Abstract: "A model with zero training error is overfit to the training data and will typically generalize poorly" goes statistical textbook wisdom. Yet, in modern practice, over-parametrized deep networks with near perfect fit on training data still show excellent test performance. This apparent contradiction points to troubling cracks in the conceptual foundations of machine learning. While classical analyses of Empirical Risk Minimization rely on balancing the complexity of predictors with training error, modern models are best described by interpolation. In that paradigm a predictor is chosen by minimizing (explicitly or implicitly) a norm corresponding to a certain inductive bias over a space of functions that fit the training data exactly. I will discuss the nature of the challenge to our understanding of machine learning and point the way forward to first analyses that account for the empirically observed phenomena. Furthermore, I will show how classical and modern models can be unified within a single "double descent" risk curve, which subsumes the classical U-shaped bias-variance trade-off.
Finally, as an example of a particularly interesting inductive bias, I will show evidence that deep over-parametrized autoencoders networks, trained with SGD, implement a form of associative memory with training examples as attractor states.
Organizer: Jean Lawrence Organizer Email: cbmm-contact@mit.eduCBMM Special Seminar: Quantum Computing: Current Approaches and Future Prospects-Jack Hidary
Abstract: Jack Hidary will take us through the nascent, but promising field of quantum computing and his new book, Quantum Computing: An Applied Approach
Bio: Jack D. Hidary is a research scientist in quantum computing and in AI at Alphabet X, formerly Google X. He and his group develop and research algorithms for NISQ-regime quantum processors as well as create new software libraries for quantum computing. In the AI field, Jack and his group focus on fundamental research such as the generalization of deep networks as well as applied AI technologies.
Organizer: Kathleen Sullivan Organizer Email: cbmm-contact@mit.eduHow Adults’ Actions, Outcomes, and Testimony Affect Preschoolers’ Persistence
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