[hpc-announce] CfP: 8th Workshop on Parallel and Distributed Computing for Large-Scale Machine Learning and Big Data Analytics (ParLearning 2019)

[Panangadan, Anand]

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)