Conference Information

HPML 2020: High Performance Machine Learning Workshop

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Submission Date:
2020-02-18 Extended
Notification Date:
2020-03-03
Conference Date:
2020-11-02
Location:
Melbourne, Australia
Years:
3
Viewed: 14409   Tracked: 0   Attend: 0
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HPML
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Conference Partner Index (CP-I)

39.5 / 100
Ranked #5,167 of 5,693 conferences · Top 91%

#316 of 341 in Systems & Architecture #667 of 742 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
37
Community attention (10%)
8
Public record completeness (15%)
25

Inputs used: Editions on record: 3 · Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-05

Call For Papers

HPML 2020 (High Performance Machine Learning Workshop) is an academic conference held in Melbourne, Australia on 2020-11-02. The paper submission deadline is 2020-02-18 (extended). Acceptance notifications are sent on 2020-03-03.

This workshop is intended to bring together the Machine Learning (ML), Artificial Intelligence (AI) and High Performance Computing (HPC) communities. In recent years, much progress has been made in Machine Learning and Artificial Intelligence in general. This progress required heavy use of high performance computers and accelerators. Moreover, ML and AI have become a “killer application” for HPC and, consequently, driven much research in this area. These facts point to an important cross-fertilization that this workshop intends to nourish. We invite researchers and professionals to take part in this workshop to discuss the challenges of Machine Learning, AI and HPC, and share their insights, use cases, tools and best practices. Proceedings will be published in IEEE Xplore. Topics of interest include, but are not limited to: Distributed and parallel Machine Learning (including deep learning) models Large scale Machine Learning applications Parallel statistical models Large scale data analytics Machine learning applied to HPC Accelerated Machine Learning HPC applied to Machine Learning Benchmarking, performance measurements, and analysis of ML models Hardware acceleration for ML and AI HPC infrastructure and resource management for ML Parallel Causal Models Cloud-based ML/AI
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