Research on the Price Prediction Algorithms of Major Cryptocurrencies and a Basic Transaction Framework
研究通过时间序列分析比特币和以太坊价格趋势，提出使用动量开收盘价描述价格走势，并设计了更安全高效的套利策略。
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Generalizable 6D Pose Estimation of Textureless Objects with Planar-based Gaussian Splatting
本文提出PG-Pose，通过结合基于平面的高斯点云重建和几何驱动的姿态优化方法，解决了无纹理物体6D姿态估计的问题。
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Unraveling the Black-box Magic: An Analysis of Neural Networks' Dynamic Local Extrema
This paper addresses the “black-box” nature and poorly understood generalization mechanisms of neural networks by proposing the Local Extrema Dynamic Mapping Hypothesis: generalization arises from a model’s ability to adaptively map training data onto local extrema of the loss landscape. Theoretical analysis reveals that the number of local extrema scales positively with the number of trainable parameters. Leveraging this insight, we design the Extrema Incremental Algorithm (EIA), which replaces conventional backpropagation with local-extremum tracking—eliminating explicit gradient computation while mitigating vanishing gradients and overfitting. We provide theoretical guarantees for EIA’s convergence and empirically validate its efficacy across multiple benchmark tasks. Results demonstrate that EIA achieves comparable or superior convergence rates and generalization performance relative to standard optimization methods. This work offers a novel perspective on the intrinsic mechanisms of neural networks and establishes a foundation for developing gradient-free, extremum-driven optimization paradigms.
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Research on the Price Prediction Algorithms of Major Cryptocurrencies and a Basic Transaction Framework
研究通过时间序列分析比特币和以太坊价格趋势，提出使用动量开收盘价描述价格走势，并设计了更安全高效的套利策略。
0 citationsRead paper
Generalizable 6D Pose Estimation of Textureless Objects with Planar-based Gaussian Splatting
本文提出PG-Pose，通过结合基于平面的高斯点云重建和几何驱动的姿态优化方法，解决了无纹理物体6D姿态估计的问题。
0 citationsRead paper
Unraveling the Black-box Magic: An Analysis of Neural Networks' Dynamic Local Extrema
This paper addresses the “black-box” nature and poorly understood generalization mechanisms of neural networks by proposing the Local Extrema Dynamic Mapping Hypothesis: generalization arises from a model’s ability to adaptively map training data onto local extrema of the loss landscape. Theoretical analysis reveals that the number of local extrema scales positively with the number of trainable parameters. Leveraging this insight, we design the Extrema Incremental Algorithm (EIA), which replaces conventional backpropagation with local-extremum tracking—eliminating explicit gradient computation while mitigating vanishing gradients and overfitting. We provide theoretical guarantees for EIA’s convergence and empirically validate its efficacy across multiple benchmark tasks. Results demonstrate that EIA achieves comparable or superior convergence rates and generalization performance relative to standard optimization methods. This work offers a novel perspective on the intrinsic mechanisms of neural networks and establishes a foundation for developing gradient-free, extremum-driven optimization paradigms.
0 citationsRead paper