Visual Intelligence: Theories and Feedback

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Related lectures (31)

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Improving Models of the Ventral Visual Pathway

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Feedback & Adaptation: Visual Intelligence

Covers feedback mechanisms in visual intelligence, human pose estimation, motor adaptation in legged robots, and PID controllers.

Computer Vision History Recap

Offers a historical overview of computer vision, exploring key developments and influential figures in the field.

Data-Driven Modeling in Neuroscience: Meenakshi Khosla

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Neural Networks: Hierarchical Models and Odor Taxis

Covers neural function, hierarchical models, odor taxis behaviors, and disparate circuit parameters in 18 slides.

Visual Theories: Vision and Perception

Explores the evolution of vision theories and key concepts in visual intelligence.

Classic Vision Theories: Nativism vs. Empiricism

Explores classic vision theories, including nativism, empiricism, structuralism, and gestaltism.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.