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www.cs.toronto.edu/~hinton
1a - Why do we need machine learning 1b - What are neural networks 1c - Some simple models of neurons 1d - A simple example of learning 1e - Three types of learning 2a - An overview of the main types of network architecture 2b - Perceptrons 2c - A geometrical view of perceptrons 2d - Why the learning works 2e - What perceptrons can not do 3a - Learning the weights of a linear neuron 3b - The error
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We learn to predict interactive polygonal annotations of objects to make human annotation of segmentation datasets much faster. Manually labeling datasets with object masks is extremely time consuming. In this work, we follow the idea of PolygonRNN to produce polygonal annotations of objects interactively using humans-in-the-loop. We introduce several important improvements to the model: 1) we des
www.cs.toronto.edu/~rgrosse
CSC 321 Winter 2018 Intro to Neural Networks and Machine Learning Source: CycleGAN. You will implement this model for Assignment 4. Overview Machine learning is a powerful set of techniques that allow computers to learn from data rather than having a human expert program a behavior by hand. Neural networks are a class of machine learning algorithm originally inspired by the brain, but which have r
www.cs.toronto.edu/~duvenaud
The Kernel Cookbook: Advice on Covariance functions by David Duvenaud Update: I've turned this page into a chapter of my thesis. If you've ever asked yourself: "How do I choose the covariance function for a Gaussian process?" this is the page for you. Here you'll find concrete advice on how to choose a covariance function for your problem, or better yet, make your own. If you're looking for softwa
www.cs.toronto.edu/~jsnell
I am a postdoctoral researcher working with Thomas Griffiths at the Department of Computer Science, Princeton University. I am supported by a DataX postdoctoral fellowship. I received my Ph.D. in Computer Science in 2021 from the University of Toronto under the supervision of Richard Zemel, where I also completed a postdoctoral fellowship. My recent research interests include nonparametric Bayesia
www.cs.toronto.edu/~kriz
Baseline results You can find some baseline replicable results on this dataset on the project page for cuda-convnet. These results were obtained with a convolutional neural network. Briefly, they are 18% test error without data augmentation and 11% with. Additionally, Jasper Snoek has a new paper in which he used Bayesian hyperparameter optimization to find nice settings of the weight decay and ot
www.cs.toronto.edu/~rkiros
PhD (Defended) Machine Learning Group Department of Computer Science University of Toronto Advisors: Dr. Ruslan Salakhutdinov Dr. Richard Zemel Links: Google Scholar
www.cs.toronto.edu/~tijmen
www.cs.toronto.edu/~nitish
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www.cs.toronto.edu/~graves
www.cs.toronto.edu/~rsalakhu
Deep Learning KDD 2014 Tutorial 9:00am-12:30am, Sunday August 24, 2014 Video of the tutorial is available here Overview Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many AI related tasks, including visual object or pattern recognition, speech perception, and language understanding. Theoretical
UCL Tutorial on: Deep Belief Nets (An updated and extended version of my 2007 NIPS tutorial) Geoffrey Hinton Canadian Institute for Advanced Research & Department of Computer Science University of Toronto Schedule for the Tutorial • 2.00 – 3.30 Tutorial part 1 • 3.30 – 3.45 Questions • 3.45 - 4.15 Tea Break • 4.15 – 5.45 Tutorial part 2 • 5.45 – 6.00 Questions Some things you will learn in this tu
www.cs.toronto.edu/~fritz
CSC321 Winter 2014 - Calendar Announcements (check these at least once a week) April 3, 3:40 pm. Exam preparation ideas: On Tuesday April 8, i.e. in the study period and two days before the final exam, there's a study session for whoever is interested. When we did this for the midterm, it was a success. It will take place in BA 5256, 1pm-3pm. Try the optional quiz that was the final exam when this
Information for prospective students, postdocs and visitors: I will not be taking any more students, postdocs or visitors. Basic papers on deep learning LeCun, Y., Bengio, Y. and Hinton, G. E. (2015) Deep Learning Nature, Vol. 521, pp 436-444. [pdf] Hinton, G. E., Osindero, S. and Teh, Y. (2006) A fast learning algorithm for deep belief nets. Neural Computation, 18, pp 1527-1554. [pdf] Movies of t
The setup for measuring the SHG is described in the supporting online material (22). We expect that the SHG strongly depends on the resonance that is excited. Obviously, the incident polariza- tion and the detuning of the laser wavelength from the resonance are of particular interest. One possibility for controlling the detuning is to change the laser wavelength for a given sample, which is diffic
Note that Gnumpy is probably no longer relevant because it was last updated in 2013 and more advanced tools are available now. (There are translations of Gnumpy into various other languages; if you're looking for a translation I suggest a web search) Gnumpy is free software, but if you use it in scientific work that gets published, you should cite this tech report in your publication. Download: gn
www.cs.toronto.edu/~jmartens
Deep Learning via Hessian-free Optimization James Martens University of Toronto June 29, 2010 UNIVERSITY OF TORONTO Computer Science James Martens (U of T) Deep Learning via HF June 29, 2010 1 / 23 Gradient descent is bad at learning deep nets The common experience: gradient descent gets much slower as the depth increases James Martens (U of T) Deep Learning via HF June 29, 2010 2 / 23 Gradient de
In the following, we first list some papers published since 2008, to reflect the new research activities since the last deep learning workshop held at NIPS, Dec 2007, and then list some earlier papers as well. Papers published since 2008 Analysis and understanding of deep belief networks [Le Roux & Bengio, 2008,Salakhutdinov & Murray, 2008] Biologically-inspired models [Karklin & Lewicki, 2008] Na
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