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

Panangadan, Anand apanangadan at Fullerton.edu
Wed May 8 22:51:57 CDT 2019

8th International Workshop on Parallel and Distributed Computing for Large-Scale Machine Learning and Big Data Analytics (ParLearning 2019)

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

- Parallel architectures/frameworks (OpenMP, CUDA etc.)
- Distributed systems/frameworks (MPI, Spark, etc.)
- Machine learning frameworks (TensorFlow, PyTorch etc.)

- 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

- 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)

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