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Scattering Networks Optimized for Low-Dimensional Data Analysis · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have published a paper detailing methods to enhance the separation capacity of scattering networks for low-dimensional datasets. The study focuses on optimizing network architectures by adjusting filter frames, proposing two key design criteria: ensuring filters adequately capture data frequencies and maintaining well-conditioned matrices that link frames to data geometry. These findings aim to improve feature extraction for datasets with inherent low dimensionality. AI

IMPACT Provides theoretical groundwork for improving feature extraction in machine learning models designed for low-dimensional data.

RANK_REASON The cluster contains an academic paper published on arXiv detailing research findings.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Scattering Networks Optimized for Low-Dimensional Data Analysis · 2 sources tracked

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Konstantin H\"aberle, Helmut B\"olcskei ·

    Separation Capacity of Scattering Networks on Low-Dimensional Datasets

    arXiv:2607.06048v1 Announce Type: new Abstract: We aim to identify scattering network architectures that maximize the separation capacity on data with low intrinsic dimension. The networks we consider employ a fixed monomial nonlinearity and no pooling, so that the only design va…

  2. arXiv stat.ML TIER_1 English(EN) · Helmut Bölcskei ·

    Separation Capacity of Scattering Networks on Low-Dimensional Datasets

    We aim to identify scattering network architectures that maximize the separation capacity on data with low intrinsic dimension. The networks we consider employ a fixed monomial nonlinearity and no pooling, so that the only design variable is the frame generated by the network fil…

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