Conference Information

MLSP 2022: IEEE International Workshop on Machine Learning for Signal Processing

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Submission Date:
2022-04-14 Extended
Notification Date:
2022-06-01
Conference Date:
2022-08-22
Location:
Xi an, China
Years:
32
Viewed: 12702   Tracked: 0   Attend: 2

Conference Partner Index (CP-I)

50.9 / 100
Ranked #1,392 of 5,687 conferences · Top 25%

#72 of 510 in Electrical & Electronic Engineering #128 of 741 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%)
94
Community attention (10%)
8
Public record completeness (15%)
25

Inputs used: Editions on record: 32 · 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 2026-09-25

Call For Papers

MLSP 2022 (IEEE International Workshop on Machine Learning for Signal Processing) is an academic conference held in Xi an, China on 2022-08-22. The paper submission deadline is 2022-04-14 (extended). Acceptance notifications are sent on 2022-06-01.

The 32nd MLSP workshop, an annual event organized by the IEEE Signal Processing Society MLSP Technical Committee, will present the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, special and regular single-track sessions, and demonstration sessions. The workshop will be held in a physical manner or a hybrid manner, depending on the global pandemic situation. Prospective authors are invited to submit papers on relevant algorithms and applications including, but not limited to: Cognitive information learning Deep learning techniques Dictionary learning Graphical and kernel methods Matrix factorization/completion Independent component analysis Information-theoretic learning Learning theory and algorithms Learning form multimodal data ML over wireless networks Applications in music and audio Pattern recognition and classification Subspace and manifold learning Sequential learning Distributed/Federated learning Reinforcement learning Transfer learning Self/semi-supervised learning
Last updated by Dou Sun on

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