arXiv Machine Learning

Binary Black Hole Parameter Estimation with Hybrid CNN-Transformer Neural Networks

arXiv:2606. 13941v1 Announce Type: cross Abstract: The detection of gravitational waves has revolutionized our ability to explore fundamental aspects of the Universe.

arXiv Machine Learning

Jun 25

Flexible Gravitational-Wave Parameter Estimation with Transformers

arXiv:2512. 02968v2 Announce Type: replace-cross Abstract: Gravitational-wave data analysis relies on accurate and efficient methods to extract physical information from noisy detector signals, yet the increasing rate and complexity of observations represent a growing challenge.

By Annalena Kofler, Maximilian Dax, Stephen R. Green, Jonas Wildberger, Nihar Gupte, Jakob H. Macke, Jonathan Gair, Alessandra Buonanno, Bernhard Sch\"olkopf

llms

arXiv Machine Learning

Jul 7

Transformers with Physics-Informed Encodings and Simulation-Based Inference for Robust Detection of Eccentric Binary Black Holes in Pulsar Timing Array Data

arXiv:2607. 03904v1 Announce Type: new Abstract: Pulsar timing arrays (PTAs) provide a unique window into nanohertz gravitational waves (GWs), but extracting astrophysical parameters from noisy, long-baseline timing residuals remains computationally challenging with traditional Bayesian techniques due to the high dimensionality of the parameter space, complex and correlated noise models, and the cost of repeated likelihood evaluations.

By Subhajit Dandapat, Alvin J. K. Chua

llms

arXiv AI

Jul 3

AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity

arXiv:2607. 01372v1 Announce Type: cross Abstract: Gravitational Waves (GWs) represent the newest window of astronomy, furthering our understanding of compact objects like black holes and neutron stars in the Universe.

By Bhavya Gupta, Deep Chatterjee, William Benoit, Ethan Marx, Christina Reissel, Seiya Tsukamoto, Kyungseop Yoon, Michael W. Coughlin, Philip Harris, Erik Katsavounidis

arXiv AI

Aug 12

Energy and Performance Benchmarking of Deep Learning Models for Breast Cancer Detection

arXiv:2608. 09996v1 Announce Type: cross Abstract: Recent advances in machine learning have greatly improved breast cancer detection, enabling more accurate and timely diagnosis.

By Samar Garrab, Ghada Achour

llms benchmarks

arXiv Machine Learning

Jun 15

Multi-Variable Stellar Parameter Estimation Using Residual Multitask Neural Networks

arXiv:2606. 13868v1 Announce Type: cross Abstract: We present an end-to-end pipeline for estimating stellar parameters from Sloan Digital Sky Survey Data Release 12 spectra using a fully connected multitask neural network with residual blocks, whose hyperparameters are tuned via Bayesian optimization.

By Bruno Santos Meneses Barreto, Marcio Eisencraft

arXiv Machine Learning

Jul 15

Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices

arXiv:2607. 12599v1 Announce Type: new Abstract: Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate under strict constraints on memory, latency, and power consumption.

By Raheen Junaid Wani, Smruti R. Sarangi

efficiency benchmarks

arXiv Machine Learning

Jun 24

Machine Learning and Deep Learning for Exoplanet Detection and Atmospheric Characterization with JWST and the Upcoming Ariel Mission

arXiv:2606. 23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the upcoming Ariel mission.

By Muallim Yakubu, Vwavware Oruaode Jude

llms diffusion benchmarks safety

arXiv AI

Jun 12

Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video

arXiv:2606. 13302v1 Announce Type: new Abstract: Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience.

By Abubakar Hamisu Kamagata, Dharm Singh Jat, Attlee Munyaradzi Gamundani, Abhishek Srivastava, Paramasivam Saravanakumar

llms robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 9

Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics

arXiv:2604. 09787v2 Announce Type: replace-cross Abstract: Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-dependent artifacts from the sensor or instrument.

By Pablo Mercader-Perez, Carolina Cuesta-Lazaro, Daniel Muthukrishna, Jeroen Audenaert, V. Ashley Villar, David W. Hogg, Marc Huertas-Company, William T. Freeman

multimodal

arXiv AI

Jul 10

Deep Learning Method for Stationary Distribution of Reflected Brownian Motion

arXiv:2607. 08091v1 Announce Type: cross Abstract: The stationary distribution of reflected Brownian motion (RBM) plays an important role in the analysis of high-dimensional stochastic systems, yet closed-form solutions are known only for a few special cases.

By Jim Dai, Zhanhao Zhang

arXiv Machine Learning

Jun 2

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP

arXiv:2605. 27527v2 Announce Type: replace-cross Abstract: Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves.

By Siddharth Chaini, Federica B. Bianco, Ashish Mahabal

benchmarks

arXiv AI

Jul 29

Long-Term PM2.5 Forecasting Using a DTW-Enhanced CNN-GRU Model

arXiv:2510. 22863v2 Announce Type: replace-cross Abstract: Reliable long-term forecasting of PM2.

By Amirali Ataee Naeini, Arshia Ataee Naeini, Fatemeh Karami Mohammadi, Omid Ghaffarpasand

llms benchmarks

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