Papers.

Worker-Owned Cooperative Models for Training Artificial Intelligence

Visualizing the Metabolism of a City Using Open Data

Two approaches to portable macros

Treating Microservices as a Unified, Distributed Database

Transitioning to the Polytechnic: The Game Development Aspect.

Topic Modeling: Optimizing for Human Interpretability

There and Back Again: A General Approach to Learning Sparse Models.

The Pragmatics of Indirect Commands in Collaborative Discourse

TempWeb 2017 Chairs’ Welcome & Organization

SimDex: Exploiting Model Similarity in Exact Matrix Factorization Recommendations.

Scalable Systems and Algorithms for Genomic Variant Analysis

With the cost of sequencing a human genome dropping below $1,000, population-scale sequencing has become feasible. In this dissertation, we describe the ADAM system for processing large genomic datasets using distributed computing. ADAM provides a decoupled stack-based architecture that can accommodate many data formats, deployment models, and data access patterns, and defines schemas that describe common genomic datatypes. To validate the ADAM architecture, we implemented an end-to-end variant calling pipeline using ADAM's APIs.

Scala for machine learning : data processing, ML algorithms, smart analytics, and more

Scala: Applied Machine Learning

Reactive Systems Architecture: Designing and Implementing an Entire Distributed System

Reactive Microsystems: The Evolution of Microservices at Scale

Random Projection Design for Scalable Implicit Smoothing of Randomly Observed Stochastic Processes

Prioritizing Attention in Analytic Monitoring.

Polytechnic: Taking the Plunge.

Paleo: A Performance Model for Deep Neural Networks

Optimizing Deep CNN-Based Queries over Video Streams at Scale

On the Optimization Landscape of Tensor Decompositions.

On the Ability of Neural Nets to Express Distributions.

On Java 8

NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale.

Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding

Modelling Chatbots with a Cognitive System Allows for a Differentiating User Experience

User Experience is a key differentiator in the era of Digital Disruption. Chatbots are increasingly considered as part of a delighting User Experience. But only a bright Conversation Modelling effort, which is part of holistic Enterprise Modelling, can make a Chatbot effective. In addition, best practices can be applied to achieve or even outperform user expectations. Thanks to Cognitive Systems and associated modelling tools, effective Chatbot dialogs can be developed in an agile manner, while respecting the enterprise strategy. Watson Services are used to illustrate this conclusion.

MODELDB: A System for Machine Learning Model Management

Learning One-hidden-layer Neural Networks with Landscape Design.

Kafka: The Definitive Guide - Real-Time Data and Stream Processing at Scale

Introducing Apache Mahout Scalable , commercial-friendly machine learning for building intelligent applications

Identity Matters in Deep Learning.

Human health in a modern world: can technology solve the mismatch?

High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark

Ground: A Data Context Service

Generalization and Equilibrium in Generative Adversarial Nets (GANs).

Finding Heavily-Weighted Features in Data Streams.

Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data

Efficient blind search for similar-waveform earthquakes in years of continuous seismic data

Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement.

Dhalion: Self-Regulating Stream Processing in Heron

Deep Learning: A Practitioner's Approach

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learningespecially deep neural networksmake a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, youll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4Js workflow toolLearn how to use DL4J natively on Spark and Hadoop

CADET: Computer Assisted Discovery Extraction and Translation

ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization.

Angular 4 Pocket Primer (Computing)

Angular 4 Pocket Primer

Android

Algorithmic Regularization in Over-parameterized Matrix Recovery.

Advanced Analytics with Spark, 2nd Edition

A Simple but Tough-to-Beat Baseline for Sentence Embeddings.

A Meta-Learning Perspective on Cold-Start Recommendations for Items