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Complex Sig: Complex deep model for signal classification

Abstract: The ability to classify signals is an important task that provides the opportunity for many different applications. In the early research for signal classification, they had to
decompose the signal using FT (Fourier transform), SIFT, MFCC or other manual methods using statistical modulation features, then classify these signals by a traditional machine learning approach. In the last few years, the process of learning deep models that lead to the automatic extraction of features has positively affected classification. Different deep-learning models with di erent depths have been proposed in the literature.
This article proposes different approaches to classify signals in different SNR conditions. ResNet-based approaches perform well for high SNRs but poorly when dealing with low SNRs. Therefore, TRansforme-based approaches were proposed for classification, reaching an average accuracy of 0.7056 in low SNR and an average of 0.9089 in high SNR.

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✨RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning

📝 Summary:
RoboCurate enhances synthetic robot learning data by evaluating action quality through simulator replay consistency. It also augments observation diversity via image editing and video transfer techniques. This leads to substantial improvements in robot task success rates compared to using real da...

🔹 Publication Date: Published on Feb 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18742
• PDF: https://arxiv.org/pdf/2602.18742
• Project Page: https://seungkukim.github.io/robocurate/

@Machine_learn

فقط نفرات ۲ و ۳ از این مقاله باقی موندن...!

Fri, 27 Feb 2026 (showing first 50 of 206 entries )
[1] arXiv:2602.23352 [pdf, html, other]
Stark localization of interacting particles
Wojciech De Roeck, Amirali Hannani, Alessio Lerose, Nathan Vandenbosch
Subjects: Mathematical Physics (math-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn)
[2] arXiv:2602.23350 [pdf, html, other]
A strengthening of the dimensional Brunn-Minkowski conjecture implies the (B)-Conjecture
Sotiris Armeniakos, Jacopo Ulivelli
Comments: Comments are welcome!
Subjects: Functional Analysis (math.FA); Metric Geometry (math.MG)
[3] arXiv:2602.23343 [pdf, html, other]
Cyclic sieving for a class of rectangular domino tableaux
Laura Colmenarejo, Bridget Eileen Tenner, Camryn E. Thompson
Comments: 17 pages
Subjects: Combinatorics (math.CO)
[4] arXiv:2602.23340 [pdf, html, other]
Combinatorial Properties of the Raisonnier Filter
Spyridon Dialiatsis, Yurii Khomskii
Subjects: Logic (math.LO)
[5] arXiv:2602.23326 [pdf, html, other]
Spin Glass Concepts in Computer Science, Statistics, and Learning
Andrea Montanari
Comments: 33 pages; 2 pdf figures
Subjects: Probability (math.PR); Disordered Systems and Neural Networks (cond-mat.dis-nn)
[6] arXiv:2602.23325 [pdf, html, other]
Spanning tight components in 4-uniform hypergraphs
Francesco Di Braccio, Brian Hearn, Joanna Lada, Mihir Neve, Lu-Ming Zhang
Comments: 24 pages, 4 figures
Subjects: Combinatorics (math.CO)
[7] arXiv:2602.23323 [pdf, html, other]
Modeling Large-Scale Adversarial Swarm Engagements using Optimal Control
Claire Walton, Isaac Kaminer, Qi Gong, Abram H. Clark, Theodoros Tsatsanifos
Comments: arXiv admin note: substantial text overlap with arXiv:2108.02311. substantial text overlap with arXiv:2108.02311
Subjects: Optimization and Control (math.OC)

@Machine_learn

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