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Jul 20, 2018   |   Peter Tang

Toward Higher-Order Training: A Progressive Batching L-BFGS Method

Stochastic Gradient Descent and its variants, referred here collectively as SGD, have been the de facto methods in training neural networks. These methods aim to minimize a network-specific loss function F(x) whose lower values correspond to better-trained versions of the neural network in question. To find a minimal point x*, SGD relies solely on knowing the…

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Jul 13, 2018   |   Michal Karzynski, Adam Rogowiec, Tomasz Socha, Leona Cook

Adaptable Deep Learning Solutions with nGraph™ Compiler and ONNX*

Artificial intelligence methods and deep learning techniques based on neural networks continue to gain adoption in more industries. As neural networks’ architectures grow in complexity, they gain new capabilities, and the number of possible solutions for which they may be used also grows at an increasing rate.  With so many constantly-changing variables at play, finding…

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Jun 21, 2018   |   Neta Zmora, Guy Jacob, Gal Novik

Compressing Deep Learning Models with Neural Network Distiller

Deep Learning (DL) and Artificial Intelligence (AI) are quickly becoming ubiquitous. Naveen Rao, Intel's Artificial Intelligence Products Group's GM, recently stated that "there is a vast explosion of [AI] applications," and Andrew Ng calls AI “the new electricity”. Deep learning applications already exist in the cloud, home, car, our mobile devices, and various embedded IoT…

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Jun 14, 2018   |   Mahmoud Abuzaina, Mohammad Ashraf Bhuiyan, Wei Wang

Using Intel® Xeon® for Multi-node Scaling of TensorFlow* with Horovod*

TensorFlow* is one of the leading deep learning and machine learning frameworks today. Earlier in 2017, Intel worked with Google* to incorporate optimizations for Intel® Xeon® processor-based platforms using Intel® Math Kernel Library (Intel® MKL) [1].  Optimizations such as these with multiple popular frameworks have led to orders of magnitude improvement in performance. Intel has…

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Jun 07, 2018   |   Dina Suehiro

MLT: The Keras of Kubernetes*

Running distributed machine learning workloads has been a hot topic lately.  Intel has shared documents walk through the process of using Kubeflow* to run distributed TensorFlow* jobs with Kubernetes, as well as a blog on using the Volume Controller for Kubernetes (KVC) for data management on clusters, and a blog describing a real-world use case…

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May 24, 2018   |   Yurong Chen, Daniel Cobb

Analyzing and Understanding Visual Data

Currently, more than 75% of all internet traffic is visual (video/images). Total traffic is exploding, projected to jump from 1.2 zettabytes per year in 2016, to 3.3 zettabytes in 2021, and visual data will comprise roughly 2.6 zettabytes of that. A major challenge for applications is how to process and understand this visual data, a…

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May 23, 2018   |   Yinyin Liu, Moshe Wasserblat

Introducing NLP Architect by Intel AI Lab

Many advances in Natural Language Processing (NLP) and Natural Language Understanding (NLU) in recent years have been driven by advancements in the field of deep learning with more powerful compute resources, greater access to useful data sets, and advances in neural network topologies and training paradigms. At Intel AI Lab, our team of NLP researchers…

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May 22, 2018   |   Hanlin Tang

Deep Learning for Remote Sensing

A remarkable aspect of deep learning is that its neural networks are powerful learning machines that generalize to different domains. For example, semantic segmentation model performs the underlying task of classifying pixels, whether those pixels are photons from a camera mounted on a self-driving car, radiodensities from X-ray tubes in a CT scanner, or seismic…

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Mar 27, 2018   |   Balaji Subramaniam, Ajay Deshpande

Kubernetes Volume Controller (KVC): Data Management Tailored for Machine Learning Workloads in Kubernetes

In this blog post, we describe Kubernetes Volume Controller (KVC). It is an open source project we’ve developed, which provides basic volume and data management in Kubernetes tailored towards machine learning (ML) workloads and pipelines.   Why Should I Care? Data is an important component in ML workloads and pipelines. Typically, data scientists and ML…

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Mar 13, 2018   |   Casimir Wierzynski

Deep Learning to Study the Brain to Improve...Deep Learning

The interplay between AI and neuroscience is one of my favorite topics, and one that several of us on the AI team at Intel have personally worked on. I'm excited to share news about a new collaboration between Intel and MIT research labs designed to advance both of these fields. Before I do, I'd like to…

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