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Explore how psychology insights enhance computer vision, from texture analysis to scene recognition and 3D reasoning. Learn about occlusion, visual memory, and self-supervised learning.
Exploring biases in ImageNet and their impact on machine learning models, discussing dataset creation processes and their consequences for real-world applications of AI.
Explore the functional specificity of the human brain, examining key questions, methodologies, and findings in cognitive neuroscience with MIT researcher Nancy Kanwisher.
Learn to approximate nonlinear operators using DeepONet, a neural network architecture based on the universal approximation theorem. Explore applications in differential equations, fluid mechanics, and multiphysics problems.
Explore auditory processing through computational models, comparing AI and human performance in speech recognition, sound localization, and pitch perception.
Explore deep generative models for brain reading, covering GAN implementation, image reconstruction from brain signals, and evolving latent codes using genetic algorithms.
Explore human intelligence compared to animals and machines in this panel discussion, examining unique cognitive abilities, language, abstract thinking, and self-awareness.
Explore the intersection of language and vision in AI, covering deep networks, sampling-based planners, and multitasking models for advanced communication and perception.
Explore adversarial examples in machine learning, their impact on human-ML alignment, and strategies for improving model robustness and interpretability.
Explore brain structure-function relationships through spike tracking in the visual hierarchy, using advanced techniques like neural pixels and high-resolution 3D atlases.
Explore neural population decoding to uncover abstract representations in the brain, analyzing experiments on invariance and information processing in visual cognition.
Explore brain imaging techniques revealing mind organization, focusing on temporal lobe functions, face perception, and cognitive neuroscience methods for understanding human cognition and behavior.
Exploring how domain-specific organization in the brain emerges from computational optimization for face and object recognition tasks, using artificial neural networks to model cortical segregation.
Explore deep learning techniques for animal pose tracking and behavior analysis, from fruit flies to giraffes. Learn to apply computer vision methods to quantify and interpret animal body language across species.
Explore neural mechanisms of pattern completion and visual inference, and how biologically-inspired models can enhance AI systems for improved visual recognition and search.
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