#frequency-domain News & Analysis

5 articles tagged with #frequency-domain. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · Jun 197/10

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Frequency-Aware Flow Matching for Continuous and Consistent Robotic Action Generation

Researchers propose Frequency-Aware Flow Matching (FAFM), a new method for robotic action generation that produces continuous, temporally consistent movements by transforming discrete action sequences into the frequency domain using discrete cosine transform. The approach demonstrates improved performance across multiple benchmarks and real-world robot deployment by handling heterogeneous control frequencies and reducing abrupt action changes.

AIBullisharXiv – CS AI · Mar 47/103

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FAST: Topology-Aware Frequency-Domain Distribution Matching for Coreset Selection

Researchers propose FAST, a new DNN-free framework for coreset selection that compresses large datasets into representative subsets for training deep neural networks. The method uses frequency-domain distribution matching and achieves 9.12% average accuracy improvement while reducing power consumption by 96.57% compared to existing methods.

AINeutralarXiv – CS AI · Jun 95/10

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Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation

Researchers introduce FLaG, a novel token aggregation module that applies frequency-domain analysis via FFT to improve how transformer models combine token representations into predictions. The method shows notable performance gains on protein structure prediction and image classification tasks while maintaining competitiveness on text benchmarks.

AINeutralarXiv – CS AI · Jun 56/10

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Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights

Researchers introduce FreqX, a novel interpretability method for machine learning models that leverages signal processing and information theory to address challenges in personalized federated learning. The approach achieves 10x faster performance than existing methods while providing both attribution and concept information while maintaining privacy.

AINeutralarXiv – CS AI · May 126/10

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Text-Guided Multi-Scale Frequency Representation Adaptation

Researchers introduce FreqAdapter, a parameter-efficient fine-tuning method that operates in the frequency domain rather than signal space to adapt pre-trained models like CLIP and LLaVA. The approach uses multi-scale adaptation strategies and text-guided prompts to improve model efficiency and performance with minimal training parameters and fast convergence.