arXiv Machine Learning June 3, 2026 By Qingye Tang, Dechao An, Haoran Peng, Yuqi Ouyang

Few-Shot Prediction for Pulsar Noise with Long Short-Term Memory Network

Read the original on arXiv Machine Learning →

arXiv:2606. 03574v1 Announce Type: cross Abstract: This work proposes a novel solution to predict pulsar timing residuals with limited data, addressing the critical challenge of data scarcity across spin-frequency subgroups of millisecond pulsars in PTA datasets.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

fine-tuning

arXiv Machine Learning

Jul 21

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data

arXiv:2505. 24852v3 Announce Type: replace-cross Abstract: On-device learning at the edge enables low-latency, private personalization with improved long-term robustness and reduced maintenance costs.

By Douwe den Blanken, Charlotte Frenkel

benchmarks

arXiv AI

Aug 11

Training Variable Long Sequences with Data-Centric Parallel

arXiv:2608. 07524v1 Announce Type: new Abstract: Training deep learning models on variable long sequences poses significant computational challenges.

By Geng Zhang, Xuanlei Zhao, Kai Wang, Yang You

arXiv Machine Learning

Jun 2

SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting

arXiv:2602. 16220v2 Announce Type: replace Abstract: Modeling multiscale patterns is crucial for long-term time series forecasting (TSF).

By Xu Zhang, Qitong Wang, Peng Wang, Wei Wang

safety

arXiv Machine Learning

Jul 14

HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS

arXiv:2607. 11746v1 Announce Type: new Abstract: With deep neural networks (DNNs) increasingly deployed on edge devices, hardware (HW)-aware optimization techniques--such as HW-aware compression and HW-aware neural architecture search (HW-NAS)--have become essential.

By Shambhavi Balamuthu Sampath, Behzad Shomali, Nael Fasfous, Moritz Thoma, Judeson Anthony Fernando, Lukas Frickenstein, Pierpaolo Mori, Manoj Rohit Vemparala, Alexander Frickenstein, Walter Stechele

arXiv Machine Learning

Aug 4

Evaluating Forecasting Techniques for Hardware Errors on a Large-scale HPC System

arXiv:2608. 01648v1 Announce Type: new Abstract: Hardware error logs in high-performance computing (HPC) systems provide early signals of abnormal behavior, yet there remain challenges in effectively forecasting these errors using modern predictive methods.

By Kaiyuan Liao, Xiwei Xuan, Tanwi Mallick, Kevin Brown, Christopher D. Carothers, Kwan-Liu Ma

llms

arXiv AI

Jun 4

MesaNet: Sequence Modeling by Locally Optimal Test-Time Training

arXiv:2506. 05233v2 Announce Type: replace-cross Abstract: Sequence modeling is currently dominated by causal transformer architectures that use softmax self-attention.

By Johannes von Oswald, Nino Scherrer, Seijin Kobayashi, Luca Versari, Songlin Yang, Sarthak Mittal, Maximilian Schlegel, Kaitlin Maile, Yanick Schimpf, Oliver Sieberling, Alexander Meulemans, Rif A. Saurous, Guillaume Lajoie, Charlotte Frenkel, Razvan Pascanu, Blaise Ag\"uera y Arcas, Jo\~ao Sacramento

llms benchmarks

Related stories

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data

Training Variable Long Sequences with Data-Centric Parallel

SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting

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MesaNet: Sequence Modeling by Locally Optimal Test-Time Training