International Workshop on Parallel and Distributed Computing for Large-Scale Machine Learning and Big Data Analytics

摘要截稿:

全文截稿: 2019-05-05

开会时间: 2019-08-05

会议难度:

CCF分类: 无

会议地点: Anchorage, Alaska, USA

Overview

Scaling up machine-learning (ML), data mining (DM) and reasoning algorithms from Artificial Intelligence (AI) for massive datasets is a major technical challenge in the time of "Big Data". The past ten years have seen the rise of multi-core and GPU based computing. In parallel and distributed computing, several frameworks such as OpenMP, OpenCL, and Spark continue to facilitate scaling up ML/DM/AI algorithms using higher levels of abstraction. We invite novel works that advance the trio-fields of ML/DM/AI through development of scalable algorithms or computing frameworks. Ideal submissions should describe methods for scaling up X using Y on Z, where potential choices for X, Y and Z are provided below.

Scaling up

Recommender systems
Optimization algorithms (gradient descent, Newton methods)
Deep learning
Sampling/sketching techniques
Clustering (agglomerative techniques, graph clustering, clustering heterogeneous data)
Classification (SVM and other classifiers)
SVD and other matrix computations
Probabilistic inference (Bayesian networks)
Logical reasoning
Graph algorithms/graph mining and knowledge graphs
Semi-supervised learning
Online/streaming learning
Generative adversarial networks
Using