Chapter
Representing Prior Knowledge in Models
47:28 - 51:26 (03:57)
This episode discusses the representation of prior knowledge in models for efficient and distributed inference, comparing it to convolutional neural networks and their feed forward processing cascade.
Clips
The feed-forward cascade used in convolutional neural networks for feature detection and pooling helps represent prior knowledge in the model.
47:28 - 50:11 (02:43)
Neural Networks
Summary
The feed-forward cascade used in convolutional neural networks for feature detection and pooling helps represent prior knowledge in the model. This ensures efficient inference and distributed processing when new evidence is introduced.
ChapterRepresenting Prior Knowledge in Models
Episode#115 – Dileep George: Brain-Inspired AI
PodcastLex Fridman Podcast
Neuroscientist and AI expert Andrew Saxe explains the importance of constraints and inference in neural networks, specifically the need for coordinated transformations and stable connections between adjacent layers.
50:11 - 51:26 (01:14)
Neural Networks
Summary
Neuroscientist and AI expert Andrew Saxe explains the importance of constraints and inference in neural networks, specifically the need for coordinated transformations and stable connections between adjacent layers.