Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: Speeding up distributed machine learning using codes
Scaling up machine learning: parallel and distributed approaches
STRADS: a distributed framework for scheduled model parallel machine learning
Parallelizing Machine Learning- Functionally A Framework and Abstractions for Parallel Graph Processing
A comparison of platforms for implementing and running very large scale machine learning algorithms
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
Distributed Optimization with Arbitrary Local Solvers
Implementation of a Practical Distributed Calculation System with Browsers and JavaScript, and Application to Distributed Deep Learning
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
TF-Replicator: Distributed Machine Learning for Researchers
Improving Execution Concurrency of Large-Scale Matrix Multiplication on Distributed Data-Parallel Platforms
Parallel Machine Learning Using Concurrency Control
Distributed Deep Learning Using Synchronous Stochastic Gradient Descent
Communication Efficient Distributed Optimization using an Approximate Newton-type Method
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Distributed Coordinate Descent Method for Learning with Big Data
MLbase: A Distributed Machine-learning System
ADMM based scalable machine learning on Spark
Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms
Secure and resilient distributed machine learning under adversarial environments
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
Rank-biased precision for measurement of retrieval effectiveness
Tools and applications in synthetic biology
Automatic face caricatures synthesis and exaggeration
Android-Based Mobile Sensory System Labware for Embedded System Education
Erasure Coding in Windows Azure Storage