Presentation is loading. Please wait.

Presentation is loading. Please wait.

Discussion: Modeling Visual Cortex V4 in Naturalistic Conditions with Invariant and Sparse Image Representations Ping Li Department of Statistics and Biostatistics.

Published byDamian Thompson Modified over 9 years ago

Similar presentations

Presentation on theme: "Discussion: Modeling Visual Cortex V4 in Naturalistic Conditions with Invariant and Sparse Image Representations Ping Li Department of Statistics and Biostatistics."— Presentation transcript:

1 Discussion: Modeling Visual Cortex V4 in Naturalistic Conditions with Invariant and Sparse Image Representations Ping Li Department of Statistics and Biostatistics Department of Computer Science Rutgers University May 2, 2014

2 Picture from Simon Thorpe

3 Question (Curiosity) #1 The input images are, by default, dense. Are there firm scientific evidences that human brains process visual signals through a sparse and low-rank mechanism? If so, then do we know, at which layer, an input image becomes a sparse signal: retina, V1, V2, V4…?

4 Question (Curiosity) #2 The talk mentioned Deep Learning as future work. Are there more concrete thoughts & research plans? For example, will the current multi-layer invariant feature extractor be replaced by an automatic feature learner? How many layers are reasonable for this task? 10? Are there firm biological evidence that the human visual system functions as a deep architecture? If so, is there intuition why “human/brain computers” are so fast while deep learning can take months of GPU time?

5 Thank you and other questions from audience?

Download ppt "Discussion: Modeling Visual Cortex V4 in Naturalistic Conditions with Invariant and Sparse Image Representations Ping Li Department of Statistics and Biostatistics."

Similar presentations

Copyright 2011, Data Mining Research Laboratory Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining Xintian Yang, Srinivasan.

Copyright 2011, Data Mining Research Laboratory Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining Xintian Yang, Srinivasan.
Junzhou Huang, Shaoting Zhang, Dimitris Metaxas CBIM, Dept. Computer Science, Rutgers University Efficient MR Image Reconstruction for Compressed MR Imaging.

Junzhou Huang, Shaoting Zhang, Dimitris Metaxas CBIM, Dept. Computer Science, Rutgers University Efficient MR Image Reconstruction for Compressed MR Imaging.
Object recognition and scene “understanding”

Object recognition and scene “understanding”
Rajat Raina Honglak Lee, Roger Grosse Alexis Battle, Chaitanya Ekanadham, Helen Kwong, Benjamin Packer, Narut Sereewattanawoot Andrew Y. Ng Stanford University.

Rajat Raina Honglak Lee, Roger Grosse Alexis Battle, Chaitanya Ekanadham, Helen Kwong, Benjamin Packer, Narut Sereewattanawoot Andrew Y. Ng Stanford University.
Lesson 6: Telemedicine. What is telemedicine? Telemedicine is the remote diagnosis and treatment of patients by means of telecommunications technology.

Lesson 6: Telemedicine. What is telemedicine? Telemedicine is the remote diagnosis and treatment of patients by means of telecommunications technology.
Sparse Coding in Sparse Winner networks Janusz A. Starzyk 1, Yinyin Liu 1, David Vogel 2 1 School of Electrical Engineering & Computer Science Ohio University,

Sparse Coding in Sparse Winner networks Janusz A. Starzyk 1, Yinyin Liu 1, David Vogel 2 1 School of Electrical Engineering & Computer Science Ohio University,
Fast Readout of Object Identity from Macaque Inferior Tempora Cortex Chou P. Hung, Gabriel Kreiman, Tomaso Poggio, James J.DiCarlo McGovern Institute for.

Fast Readout of Object Identity from Macaque Inferior Tempora Cortex Chou P. Hung, Gabriel Kreiman, Tomaso Poggio, James J.DiCarlo McGovern Institute for.
HMAX Models Architecture Jim Mutch March 31, 2010.

HMAX Models Architecture Jim Mutch March 31, 2010.
Introduction: Neurons and the Problem of Neural Coding Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne Swiss Federal Institute of Technology.

Introduction: Neurons and the Problem of Neural Coding Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne Swiss Federal Institute of Technology.
Selbo 2 SCORM Editor for eLearning Based on Ontologies Part of eLSE project Damyan Mitev University of Plovdiv “Paisii Hilendarski”

Selbo 2 SCORM Editor for eLearning Based on Ontologies Part of eLSE project Damyan Mitev University of Plovdiv “Paisii Hilendarski”
BEYOND SIMPLE FEATURES: A LARGE-SCALE FEATURE SEARCH APPROACH TO UNCONSTRAINED FACE RECOGNITION Nicolas Pinto Massachusetts Institute of Technology David.

BEYOND SIMPLE FEATURES: A LARGE-SCALE FEATURE SEARCH APPROACH TO UNCONSTRAINED FACE RECOGNITION Nicolas Pinto Massachusetts Institute of Technology David.
Computational Vision: Object Recognition Object Recognition Jeremy Wyatt.

Computational Vision: Object Recognition Object Recognition Jeremy Wyatt.
The Brain is Embodied and the Body is Embedded in the Environment Jeff Krichmar Department of Cognitive Sciences University of California, Irvine.

The Brain is Embodied and the Body is Embedded in the Environment Jeff Krichmar Department of Cognitive Sciences University of California, Irvine.
EE141 1 Broca’s area Pars opercularis Motor cortexSomatosensory cortex Sensory associative cortex Primary Auditory cortex Wernicke’s area Visual associative.

EE141 1 Broca’s area Pars opercularis Motor cortexSomatosensory cortex Sensory associative cortex Primary Auditory cortex Wernicke’s area Visual associative.
Human Cognition in Mechanical Engineering Design Roots are back in the 1970’s at the Roots are back in the 1970’s at the Engineering Design Laboratory.

Human Cognition in Mechanical Engineering Design Roots are back in the 1970’s at the Roots are back in the 1970’s at the Engineering Design Laboratory.
Image Annotation using XML and MPEG-7 Manjeet Rege Department of Computer Science Wayne State University

Image Annotation using XML and MPEG-7 Manjeet Rege Department of Computer Science Wayne State University
December 1, 2009Introduction to Cognitive Science Lecture 22: Neural Models of Mental Processes 1 Some YouTube movies: The Neocognitron Part I:

December 1, 2009Introduction to Cognitive Science Lecture 22: Neural Models of Mental Processes 1 Some YouTube movies: The Neocognitron Part I:
Associative Learning in Hierarchical Self Organizing Learning Arrays Janusz A. Starzyk, Zhen Zhu, and Yue Li School of Electrical Engineering and Computer.

Associative Learning in Hierarchical Self Organizing Learning Arrays Janusz A. Starzyk, Zhen Zhu, and Yue Li School of Electrical Engineering and Computer.
GPU vs. CPU computations Árni Einarsson Jacek Kolodziej.

GPU vs. CPU computations Árni Einarsson Jacek Kolodziej.
From R-CNN to Fast R-CNN

From R-CNN to Fast R-CNN

Similar presentations

Ads by Google