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Learning Representations: A Challenge for Learning Theory
Published on Feb 4, 202520593 Views
Perceptual tasks such as vision and audition require the construction of good features, or good internal representations of the input. Deep Learning designates a set of supervised and unsupervised met
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Presentation
Learning Representations: A Challenge For Learning Theory00:00
Learning Representations: a challenge for AI, ML, Neuroscience, Cognitive Science01:35
Architecture of “Mainstream” Machine Learning Systems02:44
This Basic Model has not evolved much since the 50's03:47
The Mammalian Visual Cortex is Hierarchical04:05
Let's be inspired by nature, but not too much05:20
Trainable Feature Hierarchies: End-to-end learning06:57
Do we really need deep architectures?07:31
Why would deep architectures be more efficient?08:47
Deep Learning: A Theoretician's Nightmare? - 109:41
Deep Learning: A Theoretician's Nightmare? - 211:34
Deep Learning: A Theoretician's Paradise?12:54
Deep Learning and Feature Learning Today13:56
In Many Fields, Feature Learning Has Caused a Revolution15:04
Convolutional Networks16:25
Early Hierarchical Feature Models for Vision16:55
The Convolutional Net Model18:16
Feature Transform - 118:20
Feature Transform - 219:03
Convolutional Network (ConvNet)19:12
Convolutional Network Architecture19:14
Convolutional Network (vintage 1990)19:16
“Mainstream” object recognition pipeline 2006-201221:33
Tasks for Which Deep Convolutional Nets are the Best21:48
Simple ConvNet Applications with State-of-the-Art Performance22:28
Prediction of Epilepsy Seizures from Intra-Cranial EEG22:33
Epilepsy Prediction22:36
ConvNet in Connectomics22:38
Object Recognition [Krizhevski, Sutskever, Hinton 2012] - 123:15
Object Recognition [Krizhevski, Sutskever, Hinton 2012] - 224:26
Another ImageNet-trained ConvNet [Zeiler & Fergus 2013]24:32
Object Recognition on-line demo25:29
ConvNet trained ImageNet [Zeiler & Fergus 2013]25:39
Features are generic: Caltech 25625:51
Features are generic: PASCAL VOC 201226:43
Labeling every pixel with the object it belongs to27:11
Scene Parsing/Labeling: ConvNet Architecture27:44
Scene Parsing/Labeling: Performance - 129:01
Scene Parsing/Labeling: Performance - 229:58
Scene Parsing/Labeling: SIFT Flow dataset - 130:27
Scene Parsing/Labeling: SIFT Flow dataset - 230:28
Scene Parsing/Labeling - 130:32
Scene Parsing/Labeling - 230:37
Scene Parsing/Labeling - 330:39
Scene Parsing/Labeling - 430:41
NYU RGB-Depth Indoor Scenes Dataset32:09
Scene Parsing/Labeling on RGB+Depth Images32:13
Semantic Segmentation on RGB+D Images and Videos32:21
Unsupervised Learning32:34
Discovering the Hidden Structure in High-Dimensional Data - 133:49
Discovering the Hidden Structure in High-Dimensional Data - 234:26
Basic Idea of Feature Learning36:03
Energy-Based Unsupervised Learning39:05
Energy Function foro a Manifold40:07
Sparse Coding & Sparse Modeling41:04
Strategies to Learn an Energy Function42:31
How to Speed Up Inference in a Generative Model?44:21
Idea: Train a “simple” function to approximate the solution44:34
Regularized Encoder-Decoder Model44:50
PSD: Basis Functions on MNIST45:01
Predictive Sparse Decomposition (PSD): Training45:03
Learned Features on natural patches45:27
Better Idea: Give the “right” structure to the encoder45:29
LISTA: Train We and S matrices46:18
Discriminative Recurrent Sparse Auto-Encoder46:36
DrSAE Discovers manifold structure of handwritten digits46:52
Convolutional Sparse Coding47:12
Convolutional PSD47:33
Convolutional Sparse Auto-Encoder on Natural Images48:06
Using PSD to Train a Hierarchy of Features - 148:11
Using PSD to Train a Hierarchy of Features - 248:36
Using PSD to Train a Hierarchy of Features - 348:46
Using PSD to Train a Hierarchy of Features - 448:51
Using PSD to Train a Hierarchy of Features - 548:54
Detection49:28
Pedastrian Detection49:57
Unsupervised pre-training with convolutional PSD 50:30
Detection Picture - 150:35
Detection Picture - 251:01
Unsupervised Learning: Invariant Features51:13
Learning Invariant Features with L2 Group Sparsity51:15
Groups are local in a 2D Topographic Map51:20
Topographic Maps51:46
Image-level training, local filters but no weight sharing51:50