Low-latency processing

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

8 shown of 8 papers with code (21 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• ReSpec: Relevance and Specificity Grounded Online Filtering for Learning on Video-Text Data Streams
  21 Apr 2025 1 repository listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  We propose Relevance and Specificity-based online filtering framework (ReSpec) that selects data based on four criteria: (i) modality alignment for clean data, (ii) task relevance for target focused data, (iii)…
• SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry
  29 Mar 2025 1 repository listed
  Event cameras asynchronously output low-latency event streams, promising for state estimation in high-speed motion and challenging lighting conditions.
• Wandering around: A bioinspired approach to visual attention through object motion sensitivity
  10 Feb 2025 1 repository listed
  Active vision enables dynamic visual perception, offering an alternative to static feedforward architectures in computer vision, which rely on large datasets and high computational resources.
• Decoding finger velocity from cortical spike trains with recurrent spiking neural networks
  3 Sep 2024 1 repository listed
  First, we found that a large RSNN model outperformed existing feedforward spiking neural networks (SNNs) and artificial neural networks (ANNs) in terms of their decoding accuracy.
• Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation
  13 May 2024 1 repository listed
  It is able to handle general bushy join trees instead of resorting to simpler left-deep variants as compared to approaches based on quantum(-inspired) optimisation, yet requires multiple orders of magnitudes fewer…
• Machine Learning 5G Attack Detection in Programmable Logic
  4 Dec 2022 1 repository listed
  Machine learning-assisted network security may significantly contribute to securing 5G components.
• High-Fidelity and Low-Latency Universal Neural Vocoder based on Multiband WaveRNN with Data-Driven Linear Prediction for Discrete Waveform Modeling
  20 May 2021 1 repository listed
  This paper presents a novel high-fidelity and low-latency universal neural vocoder framework based on multiband WaveRNN with data-driven linear prediction for discrete waveform modeling (MWDLP).
• A Multi-Phase Gammatone Filterbank for Speech Separation via TasNet
  25 Oct 2019 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  In this work, we investigate if the learned encoder of the end-to-end convolutional time domain audio separation network (Conv-TasNet) is the key to its recent success, or if the encoder can just as well be replaced by…

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.