AS.050.375, AS.050.675

Probabilistic Models of the Visual Cortex:
 
Tues/Thurs: 9:00-10:15am Fall 2019, Krieger 170.
 
 

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Course Description

The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning.

Reading and Background Material

• Course notes and course readings (posted online).
  
• A.L. Yuille and D.K. Kersten. EarlyVision. Invited Book Chapter in From Neuron to Cognition. 2016. This covers much of the first part of the course.
  

• From Neuron to Cognition: via Computational Neuroscience. MIT Press 2016. Eds. M.A. Arbib and J.J. Bonaiuto. There are many powerpoints available in the suppelmental material online:  https://mitpress.ublish.com/book/neuron-cognition#supplements
  
• Understanding Vision: Theories, Models, and Data. Zhaoping Li. Oxford University Press. 2014. This gives more detailed coverage of some topics in the first part of the course.
  
• Note: the second part of the course is based on recent work and so there is no book reference (just class notes and readings).
• Seeing: The Computational Approach to Biological Vision.  J.P. Frisby and J.V. Stone. (2nd Ed) MIT Press. 2010. Introduces the computational approach to vision but with limited mathematics.
• Stanford tutorials, the engineering website, http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial, is a good resource for students with limited statistics background, as it introduces very basic regression, dimension reduction (PCA, ICA)  and vectors.
• Stanford tutorials,  http://vision.stanford.edu/teaching/cs231n/2017/ is a good supplement for the later part of the course, it describes how convolutional network are done and has a lot of codes and examples.
• Center for Brains, Minds, and Machines Woods Hole Summer Course. Ed. E. Hildreth. Links to these lectures will be added when they become available.
  
• Instructor: Prof. Alan Yuille, alan.yuille@jhu.edu, office Clark Hall 324b.
• TA: Hongru Zhu, hongruz@jhu.edu, Krieger 147, office hours Wed 5-6pm.
  
• Prof. Yuille's Office Hours: by email appointment.
  
• Syllabus.
  

Grading Plan: 5 homework assignments will be posed on blackboard (roughly biweekly).

Homework 1, submission via Gradescope (Entry Code: 944X7V).
Homework 2.
Homework 3.
Homework 4.
Homework 5.

Preliminary Schedule (subject to revision)

Lecture	Date	Topics	Handouts	Required Reading	Optional Reading
1	Sept-3	Introduction (Part I)	Slides	YuilleKersten (Section 1.1, 1.2, 1.3)
2	Sept-5	Introduction (Part II) - How Biological Vision Can Help AI Vision	Slides		J. Tenenbaum et al. 2017 Bialek EyeSmarter Microprocessor VisualCrowding
3	Sept-10	Introduction to Retina and Primary Visual Cortex (V1)	Retina V1 V1_Mike_May		Lecture by Clay Reid
4	Sept-12	Linear Filtering	Slides	YuilleKersten (Section 2.1)
5	Sept-17	Sparsity and Hebbian Learning	Sparsity Hebbian RF	YuilleKersten (Section 2.2)
6	Sept-19	Filters for Binocular Stereo and Motion	Figures Slides	YuilleKersten (Section 2.4)
7	Sept-24	Regression, Nonlinearity and Neural Networks	Slides	YuilleKersten (Section 2.3)	Talibi&Baker Zhang2016Poster ZhangPaper
8	Sept-26	Bayesian Decision Theories I	Slides	YuilleKersten (Section 3.1, 3.2)
9	Oct-1	Bayesian Decision Theories II	Slides1 Slides2	YuilleKersten (Section 3.1, 3.2)
10	Oct-3	Cue Coupling I	Slides		Yuille&Buelthoff(1993)
11	Oct-8	Cue Coupling II	Slides		ProbModelsOnGraphs
12	Oct-10	Context and Spatial Interactions Between Neurons I	Slides	YuilleKersten (Section 4)
13	Oct-15	Context and Spatial Interactions Between Neurons II	Slides	YuilleKersten (Section 4)
14	Oct-17	Boltzmann Machines & More Context Examples	Slides		TS Lee (2014)
15	Oct-22	Motion and Kalman Filter	Slides
16	Oct-24	Bayes Historical Overview	Slides
17	Oct-29	Intro to Deep Nets	Slides
18	Oct-31	Adversarial Machine Learning	Slides
19	Nov-5	Interpretable Deep Networks	Slides	Bolei_Zhou JianyuWang Jason_Yosinski VisualConceptsDeepNets
20	Nov-7	Unsupervised Deep Networks	Slides	BootstrappingDeepNetCueCoupling ChenWei2019 ChenxuLuo.et.al Qiao_CVPR_2018 SmirnakisYuille1994 ZheRen17
21	Nov-12	Human/Animal Parsing	Slides	Notes
22	Nov-14	Learning by Immagination	Slides	Learning_from_synthetic_animals
23	Nov-19	Compositional Models	Slides	Notes GeorgeCAPCHAS HongruZhuCogSci2019 CompNets
24	Nov-21	Compositional Theory	Slides
	Nov-26	Thanksgiving
	Nov-28	Thanksgiving
25	Dec-3	Analysis by Synthesis	Slides	Notes
26	Dec-5	Review of Course