Dataset augmentation, the practice of applying a wide array of domain-specific transformations to synthetically expand a training set, is a standard tool in supervised learning. While effective in tasks such as visual recognition, the set of transformations must be carefully designed, implemented, and tested for every new domain, limiting its re-use and generality. In this paper, we adopt a simpler, domain-agnostic approach to dataset augmentation. We start with existing data points and apply simple transformations such as adding noise, interpolating, or extrapolating between them. Our main insight is to perform the transformation not in input space, but in a learned feature space. A re-kindling of interest in unsupervised representation learning makes this technique timely and more effective. It is a simple proposal, but to-date one that has not been tested empirically. Working in the space of context vectors generated by sequence-to-sequence models, we demonstrate a technique that is effective for both static and sequential data.

There has been a lot of recent interest in trying to characterize the error surface of deep models. This stems from a long standing question. Given that deep networks are highly nonlinear systems optimized by local gradient methods, why do they not seem to be affected by bad local minima? It is widely believed that training of deep models using gradient methods works so well because the error surface either has no local minima, or if they exist they need to be close in value to the global minimum. It is known that such results hold under very strong assumptions which are not satisfied by real models. In this paper we present examples showing that for such theorem to be true additional assumptions on the data, initialization schemes and/or the model classes have to be made. We look at the particular case of finite size datasets. We demonstrate that in this scenario one can construct counter-examples (datasets or initialization schemes) when the network does become susceptible to bad local minima over the weight space.

Much of the massive amount of data today is generated by automated systems, and harnessing this information to create value is central to modern technology and business strategies. Machine learning has emerged as a valuable method for many applications--image recognition, natural language processing, robotic control, and much more. By applying machine learning to system-generated debugging logs, we've gained key insights and transformed these logs into critically valuable data sources. Most software products generate logs that are used for root-cause analysis and troubleshooting. Though these logs offer useful insights into real-time performance, mining them for actionable knowledge is challenging.

Google's artificial intelligence subsidiary DeepMind is pitting AI agents against one another to test how they interact with each other and how they would react in various "social dilemmas". In a new study, researchers said they used two video games – Wolfpack and Gathering – to examine how AI agents change the way they behave based on the environment and situation they are in using social sciences and game theory principles. "The question of how and under what circumstances selfish agents cooperate is one of the fundamental questions in the social sciences," DeepMind researchers wrote in a blog post. "One of the simplest and most elegant models to describe this phenomenon is the well-known game of Prisoner's Dilemma from game theory." This well-known principle is based on the scenario where two arrested suspects jointly accused of a crime are questioned separately.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. There are many reasons why the mathematics of Machine Learning is important and I'll highlight some of them below: The main question when trying to understand an interdisciplinary field such as Machine Learning is the amount of maths necessary and the level of maths needed to understand these techniques.

Visual animal AI: https://www.youtube.com/watch?v DgPaC... Hi, welcome to ColdFusion (formally known as ColdfusTion). Experience the cutting edge of the world around us in a fun relaxed atmosphere. Eliza Doolittle) (Bicep Remix) Stumbleine - Glacier Sundra - Drifting in the Sea of Dreams (Chapter 2) Dakent - Noon (Mindthings Rework) Hnrk - fjarlæg Dr Meaker - Don't Think It's Love (Real Connoisseur Remix) Sweetheart of Kairi - Last Summer Song (ft.

Artificial intelligence (AI) can be understood as a science, engineering and deployment of machines, which perform tasks with intelligence as similar to humans. Since its inception 60 years ago, AI has observed significant growth in recent years. Initially, AI was considered as topic for academicians, though in recent years with development of various technologies, AI has turned into reality and is influencing many lives and businesses. Additionally, evolution of various other supplementary technologies such as cloud computing, machine learning and cognitive computing are collectively paving the growth of the market for AI. According to Mr. Sachin Garg - Associate Director at MarketsandMarkets who tracks the global semiconductor market, the global artificial intelligence chipset market is expected to be worth USD 16.06 Billion by 2022, growing at a CAGR of 62.9% between 2016 and 2022.

I want to talk to you today about the concerns of Non-Equilibrium Information Dynamics and how an understanding of its features lead us to a better intuition about Deep Learning systems or learning systems in general. Allow me to recap my observation from a previous post on "Deep Learning in Non-Equilibrium Dynamics". In our study of Deep Learning, practitioners derive their intuition from the mathematics of physical systems. However, since these are not a physical system that we study but rather information systems, we apply information-theoretic principles. Now, information theory has its origins also in mathematics that describe physics (i.e.

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Although we don't know how brain functions yet, we have the feeling that it must have a logic unit and a memory unit. We make decisions by reasoning and by experience. So do computers, we have the logic units, CPUs and GPUs and we also have memories. But when you look at a neural network, it functions like a black box. You feed in some inputs from one side, you receive some outputs from the other side.