We propose HAMSI (Hessian Approximated Multiple Subsets Iteration), which is a provably convergent, second order incremental algorithm for solving large-scale partially separable optimization problems. The algorithm is based on a local quadratic approximation, and hence, allows incorporating curvature information to speed-up the convergence. HAMSI is inherently parallel and it scales nicely with the number of processors. Combined with techniques for effectively utilizing modern parallel computer architectures, we illustrate that the proposed method converges more rapidly than a parallel stochastic gradient descent when both methods are used to solve large-scale matrix factorization problems. This performance gain comes only at the expense of using memory that scales linearly with the total size of the optimization variables. We conclude that HAMSI may be considered as a viable alternative in many large scale problems, where first order methods based on variants of stochastic gradient descent are applicable.

Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256x256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions.

NEW YORK--(BUSINESS WIRE)--Axiom, the leading alternative legal services provider, today announced the market launch of AxiomAI – a program that leverages Artificial Intelligence (AI) to improve the efficiency and quality of contracts work. Over the last 4 years, Axiom has collaborated with, and conducted numerous tests, of the leading AI contract data extraction providers and has leveraged those learnings on a number of client engagements. Axiom will initially embed Kira into its leading M&A Diligence and Integration offering to source relevant clauses from those contracts, thus enabling more efficient interpretation and structuring of contract data, which underlies the insights Axiom provides to its clients' legal and business users. The goal is to move from finding clauses to interpreting clauses, which promises to dramatically improve the speed of contract analysis, enable more powerful insights, and ultimately deliver the capability of creating new bodies of contracts faster, and with higher quality.

I joined Analytics Vidhya as an intern last summer. I had no clue what was in store for me. I had been following the blog for some time and liked the community, but did not know what to expect as an intern. The initial few days were good – all the interns were smart, motivated and fun to be around. We played cricket in office, did internal hackathons over weekends and learnt a lot of data science.

BigDL runs natively on Apache Spark, and because Qubole offers a greatly enhanced and optimized Spark as a service, it makes for a perfect deployment platform. In this Part 1 of a two-part series, you will learn how to get started with distributed Deep Learning library BigDL on Qubole. By the end, you will have BigDL installed on a Spark cluster with a distributed Deep Learning library readily available for you to use in your Deep Learning applications running on Qubole. In Part 2, you will learn how to write a Deep Learning application on Qubole that uses BigDL to identify handwritten digits (0 to 9) using a LeNet-5 (Convolutional Neural Networks) model that you will train and validate using MNIST database. Before we get started, here's some introduction and background on the technologies involved.

The future of AI lies in neuroscience. So says Google DeepMind's founder Demis Hassabis in a review paper published last week in the prestigious journal Neuron. Hassabis is no stranger to both fields. Armed with a PhD in neuroscience, the computer maverick launched London-based DeepMind to recreate intelligence in silicon. In 2014, Google snagged up the company for over $500 million.

Deep neural networks have many, many learnable parameters that are used to make inferences. Often, this poses a problem in two ways: Sometimes, the model does not make very accurate predictions. It also takes a long time to train them. In a previous post, we covered Train your Deep Learning model faster and sharper: Snapshot Ensembling -- M models for the cost of 1. The authors of this paper propose a method to increase training speed by freezing layers.

With more than $5 billion in 605 deals of VC investment over last 2 years, artificial intelligence (AI) is poised to have a transformative effect on consumer, enterprise, and government markets around the world. While there are certainly obstacles to overcome, consumers believe that AI has the potential to assist in medical breakthroughs, democratize costly services, elevate poor customer service, and even free up an overburdened workforce. We dug deeper into those perceptions through an online survey of consumers and business decision makers, and an expert salon with thought leaders in the field. This original research unpacks key ways AI may impact our world, delving into its implications for society, service, and management.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display The car is displayed at Downing Street, when the team visited David Cameron to demonstrate the project. Artist's rendering of Nasa's LRO spacecraft, which will have to withstand a rapid drop in temperature during an upcoming lunar eclipse that could lead to it shutting down. The regulator will now charge far more to phone companies for using the mobile spectrum -- and though it says that fee will not be passed on to customers, experts have said that prices are likely to go up. Apple has released a bright pink new iPhone 6s -- likely the only way that you'll be able to tell that someone has the new handset. The company released the new phone with much fanfare, but almost all of the changes -- a new camera and pressure-sensitive display -- were on the inside. The only new noticeable addition to the phone's look is the very pink rose gold colour, and a tiny "S" on the back.

Feature selection is one of the core topics in machine learning. In statistical science, it is called variable reduction or selection. Our scientist published a methodology to automate this process and efficiently handle la large number of features (called variables by statisticians). Here, we mention an article published by Isabelle Guyon and Andre Elisseeff in Journal of Machine Learning Research. While published in 2003, it is still one of the best ML papers on feature selection. Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available.