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Off the convex pathAlgorithms off the convex path.
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Algorithms off the convex path.
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Off the convex path | offconvex.org Reviews
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Algorithms off the convex path.
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Off the convex path
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Word Embeddings: Explaining their properties. Feb 14, 2016. This is a followup to an earlier post about word embeddings, which capture the meaning of a word using a low-dimensional vector, and are ubiquitous in natural language processing. I will talk about my joint. Continue. NIPS 2015 workshop on non-convex optimization. Jan 25, 2016. Nature, Dynamical Systems and Optimization. Dec 21, 2015. Tensor Methods in Machine Learning. Dec 17, 2015. Dec 12, 2015. Why go off the convex path? Dec 11, 2015. The no...
Gradient Descent Learns Linear Dynamical Systems – Off the convex path
http://www.offconvex.org/2016/10/13/gradient-descent-learns-dynamical-systems
Gradient Descent Learns Linear Dynamical Systems. Moritz Hardt and Tengyu Ma • Oct 13, 2016 • 15 minute read. In this post, we take a step back to reflect on the mathematics of recurrent neural networks. Interpreting recurrent neural networks as dynamical systems, we will show that stochastic gradient descent successfully learns the parameters of an unknown. Here $A,B,C,D$ are linear transformations with compatible dimensions, and $ xi t$ is Gaussian noise added to the output at each time. In the lea...
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Markov Chains Through the Lens of Dynamical Systems: The Case of Evolution – Off the convex path
http://www.offconvex.org/2016/04/04/markov-chains-dynamical-systems
Markov Chains Through the Lens of Dynamical Systems: The Case of Evolution. Nisheeth Vishnoi • Apr 4, 2016 • 20 minute read. In this post, we will see the main technical ideas in the analysis of the mixing time of evolutionary Markov chains introduced in a previous post. We start by introducing the notion of the. With stochastic descent methods, popular in machine learning and optimization, and the readers interested in the latter might benefit from our techniques. Paper we refer to this as the. Dynamica...
Escaping from Saddle Points – Off the convex path
http://www.offconvex.org/2016/03/22/saddlepoints
Escaping from Saddle Points. Rong Ge • Mar 22, 2016 • 13 minute read. Convex functions are simple they usually have only one local minimum. Non-convex functions can be much more complicated. In this post we will discuss various types of. That you might encounter when you go. Off the convex path. In particular, we will see in many cases simple heuristics based on gradient descent can lead you to a. Various Types of Critical Points. Here $ eta$ is a small step size. This is the. That is also the. To distin...
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What Drives Real GDP Growth?
http://www.turingfinance.com/what-drives-real-gdp-growth-part-one
Turing Finance August 20, 2016. What Drives Real GDP Growth? What socioeconomic factors drive long term real GDP growth? What better ways are there of grouping countries together than geographically? Which countries of the world are going to experience real GDP growth from now till 2019? Update - I have completed the second part of this three part series entitled:. Monte Carlo K-Means Clustering of Countries with Python. Constructing the Data Sets. Three data sets containing absolute values were construc...
Interest - Chenye WU's Homepage
http://www.wcy.name/interest
Email: wcy AT ieee DOT org. Interesting Talks and Inspiring Ideas. James Mickens: Robust, Extensible Execution Environments for Web Applications. Hans Rosling: Stats that reshape your world-view. NIPS 2013 Tutorial - Causes and Counterfactuals: Concepts, Principles and Tools. Steven Chu: Low Cost Pathways to Grid Integration of Renewable Energy. Andrew Yao: Quantum Computing - A Great Science in the Making. Jean Yang: Axiomatic Basis for Computer Programming. Cynthia Dwork: Privacy in the land of Plenty.
Computational Finance at IEEE WCCI 2014
http://www.turingfinance.com/computational-finance-updates-ieee-world-congress-computational-intelligence
Turing Finance August 20, 2016. Computational Finance at IEEE WCCI 2014. I recently had the awesome opportunity to present my honours research at this years IEEE World Congress for Computational Intelligence conference (IEEE-WCCI) in Beijing. My trip was sponsored by the University of Pretoria's Computational Intelligence Research Group. CIRG) so I would like to send out a special thanks to supervisor, Dr. Katherine Malan, and my Professor, Andries Engelbrecht, for making the trip possible. Some of the p...
Stuart Gordon Reid
http://www.turingfinance.com/author/StuartReid
Turing Finance August 26, 2016. Posts ByStuart Gordon Reid. Lossless Compression Algorithms and Market Efficiency? April 18, 2016 StuartReid. Hacking The Random Walk Hypothesis. We applied the NIST suite of cryptographic tests for randomness to binarized daily market returns. Overall the NIST suite failed on the data. This result was taken to mean that markets are not quite the . Read More. Stock Market Prices Do Not Follow Random Walks. February 8, 2016 StuartReid. A Non-Random Walk down Wall Street.
Simulated Annealing for Portfolio Optimization
http://www.turingfinance.com/simulated-annealing-for-portfolio-optimization
Turing Finance August 31, 2016. Simulated Annealing for Portfolio Optimization. This article applies the Simulated Annealing (SA) algorithm. Basic Simulated Annealing Algorithm. This real-world concept may, at first, seem quite difficult to translate into an optimization algorithm, however, with some 'algorithmic license' the resulting algorithm is actually quite simple. The simulated annealing algorithm for portfolio optimization essentially only consists of eleven steps, only two of which are reall...
Computational Decision Making Methods
http://www.turingfinance.com/computational-decision-making-for-finance
Turing Finance August 21, 2016. Computational Decision Making Methods. Artificial intelligence is broadly defined as the ability of an agent or a model to make either optimal or satisficing decisions. How should I allocate my portfolio? Should I buy this stock or not? What should I pay for this security? This article will discuss the applications and challenges of computational decision making and present a taxonomy of computational decision making methods, their strengths, and their weaknesses. I ho...
All Models are Wrong, 7 Sources of Model Risk
http://www.turingfinance.com/perils-optimization-in-investment-management
Turing Finance August 21, 2016. All Models are Wrong, 7 Sources of Model Risk. The 2008 financial crisis. Revealed to the world (in spectacular fashion) the fragility of financial models. Since the financial crisis two words have come up time and time again:. Models are used to represent some object in the real world. We build models so that we can. There are many types of financial models but they are all used to represent. From the world of finance. An interesting challenge with financial modelling...
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North Metro TV Schedule. Bamba, Samurai, and Backwater discuss issues on everyone's mind - except they keep falling off topic in funny segues. The result is your weekly ADD-theater and pop culture extravaganza, filmed "cable access style" at North Metro TV in Blaine, Minnesota. Sponsor us at our Patreon. The crew attempts to discuss parking ramps, but quickly gets off topic, as always. Theater of the Weird. By Ryan Howard,.
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Off the convex path
Back-propagation, an introduction. Dec 20, 2016. Given the sheer number of backpropagation tutorials on the internet, is there really need for another? One of us (Sanjeev) recently taught backpropagation in undergrad AI and couldn’t find any account he was happy with. Continue. The search for biologically plausible neural computation: The conventional approach. Nov 3, 2016. Gradient Descent Learns Linear Dynamical Systems. Oct 13, 2016. Linear algebraic structure of word meanings. Jul 10, 2016. Previousl...
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