8th International Workshop on Parallel and Distributed Computing for Large-Scale Machine Learning and Big Data Analytics (ParLearning 2019) https://parlearning.github.io/
August 5, 2019 Anchorage, Alaska, USA
In conjunction with the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019)
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 - Distributed algorithms and AI for Blockchain - Sampling/sketching techniques - Clustering (agglomerative techniques, graph clustering, clustering heterogeneous data) - Probabilistic inference (Bayesian networks) - Graph algorithms, graph mining and knowledge graphs - Graph neural networks - Autoencoders and variational autoencoders - Generative adversarial networks - Generative models - Deep reinforcement learning
Using - Parallel architectures/frameworks (OpenMP, CUDA etc.) - Distributed systems/frameworks (MPI, Spark, etc.) - Machine learning frameworks (TensorFlow, PyTorch etc.)
On - Various infrastructures, such as cloud, commodity clusters, GPUs, and emerging AI chips.
Important Dates - Paper submission: May 12, 2019 (Anywhere on Earth) - Author notification: June 1, 2019 - Camera-ready version: Jun 8, 2019
Organization - General Chairs: Arindam Pal (TCS Research and Innovation, Kolkata, India) and Henri Bal (Vrije Universiteit, Amsterdam, Netherlands) - Program Chairs: Azalia Mirhoseini (Google AI, Mountain View, CA, USA) and Thomas Parnell (IBM Research, Zurich, Switzerland)