Date |
Topic |
Readings |
Presenters |
|
4/1
|
Introduction: Vision: are models of object recognition catching up with the brain?
|
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4/3 |
Functional organization of high-level visual cortex and its relation to perception
|
||
|
4/8
|
The importance of cortical connections
|
Jesse Gomez | |
4/10 |
No class. CBIS symposium: cbis.stanford.edu
|
||
|
4/15
|
Information processing in the primate visual system
|
Lane McIntosh | |
4/17 |
Neocognitron: A hierarchical neural network
|
Peter Tseng | |
|
4/22
|
A feedforward architecture accounts for rapid categorization
|
John Doherty | |
4/24 |
Image interpretation by a single bottom-up top-down cycle categorization
|
Andrew Giel & Qingping He | |
|
4/29
|
How Does the Brain Solve Visual Object Recognition?
|
Colleen Rhoades | |
5/1 |
Invariance and selectivity in the ventral visual pathway
|
Niru Maheswaranathan | |
|
5/6
|
Hierarchical Bayesian inference in the visual cortex
|
Ian Ballard | |
5/8 |
Learning AND-OR Templates for Object Recognition and Detection
|
Ben Poole | |
|
5/13
|
Basic objects in natural categories
|
Kim Kyunghee & Marius Catalin Iordan | |
5/15 |
Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey
|
Kaitlyn Benitez-Strine & Lauren Edelson | |
|
5/20
|
Hedging Your Bets: Optimizing Accuracy- Specificity Trade-offs in Large Scale Visual Recognition
|
Kevin Nam Truong | |
5/22 |
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
|
Andre Esteva | |
|
5/27
|
ImageNet Classification with Deep Convolutional Netral Networks
|
Rohan Chopra & Isaac Kauvar | |
5/29 |
View from the Top: Hierarchies and Reverse Hierarchies in the Visual System
|
Robin Woodby | |
|
6/3
|
No class: dead week
|
||
6/5
|
Class Project Due (On Midnight)
|