DjVuLibre DjView Free - UnixLinuxWinMac DjVu Browser Plug-in Free - WinMac. Pdf to djvu mac Spotlight Plugin for DjVu Free - Add-on for Mac OS X 10. SearchPDF Commercial - Supports DjVu and PDF on Windows web servers.A brief googling reveals that there are various softwares that can do this, but as far as I know none that one can use on a. In Mac, there is no built in utility available to open the djvu file. For that, we have to download djvu viewer from its official website, djvu.org When the page opens up, scroll down and click on. DjVu Image Viewer Plug-in is a supplement that is installed on your browser to display images that have been compressed with DjVu format. Thanks to DjVu Image Viewer Plug-in developers of content for web pages can scan images at high resolution, books, magazines.
All the talks and posters on this page are provided in DjVu, PDF.Some are also provided in ODP (Open Office's Open Document Format), and PPT (MS PowerPoint).
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Caution: a few PDF files do not display correctly in ghostview.We very strongly encourage interested readers to use the DjVuversions: they display instantly, load much faster, and have nocompatibility problems.
- djview4: Free/Open Source DjVu viewer for Windows, Mac OS-X, and Linux.
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A similar seminar was given at the Xerox Research Center Europein Grenoble the following day.
Title: Learning Feature Hierarchies for Vision
Abstract: Intelligent perceptual tasks such as vision andaudition require the construction of good internalrepresentations. Theoretical and empirical evidence suggest that theperceptual world is best represented by a multi-stage hierarchy inwhich features in successive stages are increasingly global,invariant, and abstract. An important challenge for Machine Learningis to devise 'deep learning' methods for multi-stage architecture thancan automatically learn good feature hierarchies from labeled andunlabeled data.
A class of such methods that combine unsupervised sparse coding, andsupervised refinement will be described. We demonstrate the use ofthese deep learning methods to train convolutional networks(ConvNets). ConvNets are biologically-inspired architecturesconsisting of multiple stages of filter banks, interspersed withnon-linear operations, and spatial pooling operations, analogous tothe simple cells and complex cells in the mammalian visual cortex.
A number of applications will be shown through videos and live demos,including a category-level object recognition system that can betrained on the fly, a pedestrian detector, and system that recognizeshuman activities in videos, and a trainable vision system for off-roadmobile robot navigation.
A new kind of 'dataflow' computer architecture, dubbed NeuFlow, wasdesigned to run these algorithms (and other vision and recognitionalgorithms) in real time on small, embeddable platforms. an FPGAimplementation of NeuFlow running various vision applications will beshown. An ASIC is being designed in collaboration with e-lab at Yale,which will be capable of 700 Giga-operations per second for less than3 Watts.
Title: Learning Feature Hierarchies for Vision
Abstract: Animals and humans autonomously learn to perceive andnavigate the world. What 'learning algorithm' does the cortex use toorganize itself? Could computers and robots learn to perceive the wayanimals do, by just observing the world and moving around it? Thisconstitutes a major challenge for machine learning and computervision.
The visual cortex uses a multi-stage hierarchy of representations,from pixels, to edges, motifs, parts, objects, and scenes. A newbranch of machine learning research, known as 'deep learning' isproducing new algorithms that can learn such multi-stage hierarchiesof representations from raw inputs. I will describe abiologically-inspired, trainable vision architecture calledconvolutional network. It consists of multiple stages of filter banks,non-linear operations, and spatial pooling operations, analogous tothe simple cells and complex cells in the mammalian visualcortex. Convolutional nets are first trained with unlabeled samplesusing a learning method based on sparse coding, and subsequentlyfine-tuned using labelled samples with a gradient-based supervisedlearning algorithm.
A number of applications will be shown through videos and live demos,including a category-level object recognition system that can betrained on the fly, a pedestrian detector, and system that recognizeshuman activities in videos, and a trainable vision system for off-roadmobile robot navigation. A very fast implementation of these systemson specialized hardware will be shown. It is based on a newprogrammable and reconfigurable 'dataflow' architecture dubbedNeuFlow.
- Introduction [PDF]
- Energy-Based Learning [PDF]
- Multi-Stage Learning [PDF]
- Convolutional Networks [PDF]
- Unsupervised Deep Learning [PDF]
Keynote talk given at the International Conference on Image and Signal Processing in Trois Rivieres, Quebec.
Slides:
- [Slides in PDF (24.4MB)][Slides in DjVu (12.3MB)][Slides in ODP (Open Office / Open Document Format)(25.1MB)]
NASAIS&T Colloquium delivered atthe NASAGoddard Space Flight Center in Maryland (with video).
NASA has a link to thepresentation and a video of the talk.
Slides:
- [Slides in PDF (29.3MB)][Slides in DjVu (15.2MB)][Slides in ODP (27.7MB)]
Talk given at Columbia University forthe NSF Workshop onHybrid Neuro-Computer Vision Systems at Columbia University. Theaudience was a mixture of neuroscientists, computer vision researchersand hardware experts.
Slides:
- [Slides in PDF (19.7MB)][Slides in DjVu (10.4MB)]
Series of lectures atthe Microsoft/CIfARWinter School on Machine Learning and Computer Vision.
Slides:
- Part 1: [ PDF][ ODP]
- Part 2: [ PDF][ ODP]
- Part 3: [ PDF][ ODP]
Two lectures given atthe 2009Sino-USA Vision-Learning Pattern Recognition Summer School thattook place at Peking University, Beijing.
- [PDF (21.5MB)][DjVu (7.6MB)][ODP (15.2MB)] Deep Learning
- [PDF (8.5MB)][DjVu (4.2MB)][ODP (15.0MB)] Other Methods and Applications of Deep Learning
- [PDF (16.5MB)][DjVu (8.5MB)][ODP (12.0MB)] Learning Invariant Feature Hierarchies
- [PDF (3.2MB)][DjVu (0.9MB)][ODP (41KB)] Future Challenges
- [Slides in PDF (27.3MB)][Slides in DjVu (11.3MB)][Slides in ODP (Open Office / Open Document Format)(29.5MB)]
- [Slides in PDF (26.2MB)][Slides in DjVu (10.8MB)][Slides in ODP (Open Office / Open Document Format)(29.4MB)]
Intelligent tasks, such as visual perception, auditory perception, andlanguage understanding require the construction of good internalrepresentations of the world. Internal representations (or 'features')must be invariant (or robust) to irrelevant variations of the input,but must preserve the information relevant to the task. An importantgoal of our research is to devise methods that can automatically learngood internal representations from labeled and unlabeled data.Results from theoretical analysis, and experimental evidence fromvisual neuroscience, suggest that the visual world is best representedby a multi-stage hierarchy, in which features in successive stages areincreasingly global, invariant, and abstract. The main question is howcan one train such deep architectures from unlabeled data and limitedamounts of labeled data.
Several methods have recently been proposed to train deeparchitectures in an unsupervised fashion. Each layer of the deeparchitecture is composed of a feed-forward encoder which computes afeature vector from the input, and a feed-back decoder whichreconstructs the input from the features. The training shapes anenergy landscape with low valleys around the training samples and highplateaus everywhere else. A number of such layers can be stacked andtrained sequentially. A particular class of methods for deepenergy-based unsupervised learning will be described that imposessparsity constraints on the features. When applied to natural imagepatches, the method produces hierarchies of filters similar to thosefound in the mammalian visual cortex. A simple modification of thesparsity criterion produces locally-invariant features with similarcharacteristics as hand-designed features, such as SIFT.
An application to category-level object recognition with invariance topose and illumination will be described. By stacking multiple stagesof sparse features, and refining the whole system with supervisedtraining, state-the-art accuracy can be achieved on standard datasetswith very few labeled samples. Another application to vision-basednavigation for off-road mobile robots will be shown. After a phase ofoff-line unsupervised learning, the system autonomously learns todiscriminate obstacles from traversable areas at long range usinglabels produced with stereo vision for nearby areas.
This is joint work with Y-Lan Boureau, Karol Gregor, Raia Hadsell,Koray Kavakcuoglu, and Marc'Aurelio Ranzato.
- [PDF (16.2MB)][DjVu (7.3MB)][ODP (2.8MB)] Energy-Based Models
- [PDF (14.9MB)][DjVu (5.9MB)][ODP (4.4MB)] Supervised Learning
- [PDF (14.2MB)][DjVu (6.2MB)][ODP (4.6MB)] Manifold Learning
- [PDF (20.9MB)][DjVu (9.1MB)][ODP (19.7MB)]: Deep Learning
- [Slides in PDF (22.7MB)][Slides in DjVu (7.6MB)] [Slides in ODP (Open Office / Open Document Format)(19.4MB)]
67 minute Video on YouTube
Abstract: A long-term goal of Machine Learning research is tosolve highy complex 'intelligent' tasks, such as visual perceptionauditory perception, and language understanding. To reach that goal,the ML community must solve two problems: the Deep Learning Problem,and the Partition Function Problem.
There is considerable theoretical and empirical evidence that complextasks, such as invariant object recognition in vision, require 'deep'architectures, composed of multiple layers of trainable non-linearmodules. The Deep Learning Problem is related to the difficulty oftraining such deep architectures.
Several methods have recently been proposed to train (or pre-train)deep architectures in an unsupervised fashion. Each layer of the deeparchitecture is composed of an encoder which computes a feature vectorfrom the input, and a decoder which reconstructs the input from thefeatures. A large number of such layers can be stacked and trainedsequentially, thereby learning a deep hierarchy of features withincreasing levels of abstraction. The training of each layer can beseen as shaping an energy landscape with low valleys around thetraining samples and high plateaus everywhere else. Forming thesehigh plateaus constitute the so-called Partition Function problem.
A particular class of methods for deep energy-based unsupervisedlearning will be described that solves the Partition Function problemby imposing sparsity constraints on the features. The method can learnmultiple levels of sparse and overcomplete representations ofdata. When applied to natural image patches, the method produceshierarchies of filters similar to those found in the mammalian visualcortex.
An application to category-level object recognition with invariance topose and illumination will be described (with a live demo). Anotherapplication to vision-based navigation for off-road mobile robots willbe described (with videos). The system autonomously learns todiscriminate obstacles from traversable areas at long range.
This is joint work with Y-Lan Boureau, Sumit Chopra, Raia Hadsell,Fu-Jie Huang, Koray Kavakcuoglu, and Marc'Aurelio Ranzato.
A 15 minute Interview with Yann LeCun on machine learning research, lecturing styles, where NIPS is going, the philosophy of science, and various other topics.
| Video and slides of a talk given at the 2007 NIPS workshop on Efficient Learning,in Vancouver, Canada, December 7, 2007. I'll probably make a few friends with that one.
Click on the image at right to view the video of the talk (with lots of questions from the audience). |
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Slides and Video of a talk given at the NIPS satellite session on deep learning.in Vancouver, Canada, December 6, 2007.
(Part 2 also contains Martin Szummer's talk) Other talks at that satellite session are available at the meeting's main web site. |
Slides of a keynote talk given at the 2007 International Conference on Document Analysis and Recognition (ICDAR), in Curitiba, Brazil, September 24, 2007.
- [Slides in DjVu(11.7MB)] [Slides in PDF (26.7MB)]
| Yann LeCun, Sumit Chopra, Marc'Aurelio Ranzato and Fu-Jie Huang: Energy-Based Models in Document Recognition and Computer Vision, Proc. International Conference on Document Analysis and Recognition (ICDAR), 2007, [key=lecun-icdar-keynote-07]. | 110KB | DjVu |
| 355KB | ||
| 551KB | PS.GZ |
Abstract:Over the last few years, the Machine Learning and Natural LanguageProcessing communities have devoted a considerable of work to learningmodels whose outputs are 'structured', such as sequences of charactersand words in a human language. The methods of choice includeConditional Random Fields, Hidden Markov SVMs, and Maximum MarginMarkov Networks. These models can be seen as un-normalized versions ofdiscriminative Hidden Markov Models. It may come to a surprise to theICDAR community that this class of models was originally developed inthe handwriting recognition community in the mid 90's to trainhandwritten recognition systems at word-level discriminatively. Thevarious approaches can be described in a unified manner through toconcept of 'Energy-Based Model' (EBM). EBMs capture depencies betweenvariables by associating a scalar energy to each configuration of thevariables. Given a set of observed variables (e.g an image), an EBMinference consists in finding configurations of unobserved variables(e.g. a recognized word or sentence) that minimize theenergy. Training an EBM consists in designing a loss function whoseminimization will shape the energy surface so that correct variableconfigurations have lower energies than incorrect configurations. Themain advantage of the EBM approach is to circumvent one of the maindifficulties associated with training probabilistic models: keepingthem properly normalized, a potentially intractable problem withcomplex models. Energy-Based learning has been applied withconsiderable success to such problems as handwriting recognition,natural language processing, biological sequence analysis, computervision (object detection and recognition), image segmentation, imagerestoration, unsupervised feature learning, and dimensionalityreduction. Several specific applications will be described (and, forsome, illustrated with real-time demonstrations) including: a checkreading system, a real-time system for simultaneously detecting humanfaces in images and estimating their pose; an unsupervised method forlearning invariant feature hierarchies; and a real-time system fordetecting and recognizing generic object categories in images, such asairplanes, cars, animal, and people.
Slides and audio podcast of a tutorial given at the 2007 IPAM Workshop on the Mathematics of Knowledge and Search Engines, September 14, 2007.
- 1. Learning Similarity Metrics [ DjVu(6.1MB)] [PDF (14.2MB)]
- 2. Supervised and Unsupervised Methods for Learning Invariant Feature Hierarchies [DjVu(13.1MB)] [PDF (29.4MB)]
- Audio Podcast: [MP3 from NYU (58MB)][MP3 from IPAM (58MB)]
Slides of a tutorial given at the 2007 International Computer Vision Summer School, July 13, 2007.
- [Slides in DjVu(12.9MB)] [Slides in PDF (30.0MB)] [Open Office .ODP (17.7MB)]
Slides and video of an invited talk given at the 2006 NIPS workshop'Learning to Compare Samples', December 8, 2006.
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- [Slides in DjVu(6.5MB)] [Slides in PDF (23.6MB)] [OpenOffice .ODP (4.9MB)] [PowerPoint .PPT (9.5MB)]
The video of the tutorial is availablefrom the NIPS website in .mov (QuickTime) format at several resolutions:
- 320x240.
- 640x480.
- 900x600.
- [Slides in DjVu(8.4MB)] [Slides in PDF (25.5MB)]
Slides of a 3-hour tutorial given by Yann LeCun at the 2006 CIARSummer School: Neural Computation & Adaptive Perception, at University of Toronto.This is new version of the tutorial based on the paper A Tutorial onEnergy-Based Learning. This is considerably reworked from2005 IPAM version.
- A Tutorial on Energy-Based Learning:[Slides in DjVu (5.2MB)][Slides in PDF (18.2MB)]
- Deep Learning for Generic Object Recognition:[Slides in DjVu (3.8MB)][Slides in PDF (11.6MB)]
Djvu Viewer For Mac
Supervised and Unsupervised Learning with Energy-Based Models: [Slides in DjVu (6.9MB)][Slides in PDF (20.6MB)]
This is a good overview of research at CBLL: energy-based learning,object recognition, face detection, unsupervised feature learning,and robot vision and navigation.
Slides and Videos of a 4-hour tutorial given by Yann LeCun at the 2005 IPAM GraduateSummer School: Intelligent Extraction of Information from Graphs andHigh Dimensional Data, at IPAM/UCLA. Streaming videos of all thetalks are available from the IPAM web site in RealVideo format.
The tutorial includes 4 talks:
- Energy-Based Models part 1, Introduction:
[Streaming Video of the Talk][Slides in DjVu (2.2MB)][Slides in PDF (4.0MB)] - Energy-Based Models part 2, Architectures and Loss functions:
[Streaming Video of the Talk][Slides in DjVu (3.7MB)][Slides in PDF (5.3MB)] - Architectures for Invariant Image Recognition, Convolutional Networks:
[Streaming Video of the Talk][Slides in DjVu (4.7MB)][Slides in PDF (11.9MB)] - Trainable Dissimilarity Metrics, Segmentation, Sequence Labeling, Graph Transformer Networks:
[Streaming Video of the Talk][Slides in DjVu (2.1MB)][Slides in PDF (5.7MB)]
Mac Os Djvu
Slides and videos of a 4-hour tutorial given by Yann LeCun at theLearning Theory SummerSchool organized by the ToyotaTechnological Institute in Chicago. The tutorial includes 3 talks.The 3 videos are available fromVideoLectures.net.
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| VideoLectures.net also has videos of two 'lunch-time debates' (or panel discussions) in which Yann was a participant, together with Rob Schapire, David McAllester, Yasemin Altun, Mikhail Belkin, Yoram Singer, and John Langford. |
Mac Djvu To Pdf
- View a Video of Yann's talk at MSRI:
- Download QuickTime MPEG4 File from MSRI (508MB)
- Download QuickTime MPEG4 File from NYU (508MB)
- Slides:[DjVu (4.8MB)][PDF (9.8MB)]
| A 1-hour talk (with a video) of a Distinguished Lecture given on October 22, 2001at the University of Illinois, Urbana-Champaign. |