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Neural network is heterogeneous: Phase matters more

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Yuqi Nie, Hui Yuan

Machine Learning · Submitted 3 Nov 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We find a heterogeneity in both complex and real valued neural networks with the insight from wave optics, claiming a much more important role of phase than its amplitude counterpart in the weight matrix.

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arXiv ID2111.02014 (version 2)
Pith IDNDGWC5HA, Pith's permanent record of this paper and its review
CitationHui Yuan, Yuqi Nie. "Neural network is heterogeneous: Phase matters more." arXiv:2111.02014 (2021). https://pith.science/paper/NDGWC5HA
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,573 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.06 · human participants 0.02 · controlled comparison 0.47 · randomized assignment 0.02 · states quantitative result 0.07 · compares with prior results 0.54 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We find a heterogeneity in both complex and real valued neural networks with the insight from wave optics, claiming a much more important role of phase than its amplitude counterpart in the weight matrix. In complex-valued neural networks, we show that among different types of pruning, the weight matrix with only phase information preserved achieves the best accuracy, which holds robustly under various settings of depth and width. The conclusion can be generalized to real-valued neural networks, where signs take the place of phases. These inspiring findings enrich the techniques of network pruning and binary computation.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We find a heterogeneity in both complex and real valued neural networks with the insight from wave optics, claiming a much more important role of phase than its amplitude counterpart in the weight matrix. result 0.88 0.93 0.78 asserted 1.00
s1 In complex-valued neural networks, we show that among different types of pruning, the weight matrix with only phase information preserved achieves the best accuracy, which holds robustly under various settings of depth and width. result 1.00 0.95 0.45 asserted 1.00
s2 The conclusion can be generalized to real-valued neural networks, where signs take the place of phases. result 0.50 0.71 0.18 asserted 0.96
s3 These inspiring findings enrich the techniques of network pruning and binary computation. interpretation 0.95 0.31 0.04 asserted 0.91

What kind of paper the classifier reports this is: empirical study 0.86 · evidence from simulation or computation 0.63.

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  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We find a heterogeneity in both complex and real valued neural networks with the insight from wave optics, claiming a much more important role of phase than its amplitude counterpart in the weight matrix. In complex-valued neural networks, we show that among different types of pruning, the weight matrix with only phase information preserved achieves the best accuracy, which holds robustly under various settings of depth and width. The conclusion can be generalized to real-valued neural networks, where signs take the place of phases. These inspiring findings enrich the techniques of network pruning and binary computation.

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References
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4 arXiv papers cite this in 5 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 3 sentences without a confident label

Cites it as support 1 paper
Cites it as background 1 paper
  • Complex-valued data can exist in: (1) natural form, involving applications using Synthetic Aperture Radar (SAR) [ref] , Magnetic Resonance Imaging (MRI), and telecommunication, (2) complex-valued representation of real-valued data, such as Fourier transform [ref] and spectrum-based methods [ref] , and (3) physics application such as optical physics [ref] , where both amplitude and phase information are essential.

    Fully complex-valued deep learning model for visual perception · Introduction · 0.85

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