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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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4 results for “logarithm-determinant”

Robust Subspace Clustering via Smoothed Rank Approximationcs.CV

Proposes a new rank approximation method for improved subspace clustering accuracy.

problem Improving rank approximation for better subspace clustering accuracy in real-world applications.

method Smoothed rank approximation using Logarithm-Determinant for robust subspace clustering.

result The proposed method outperforms state-of-the-art algorithms in face clustering and motion segmentation tasks.

subspace clustering rank approximation logarithm-determinant nuclear norm optimization

2015-08-18abs ↗pdf ↗

Understanding Neural Networks with Logarithm Determinant Entropy Estimatorcs.LG

LogDet estimator improves entropy estimation in neural networks.

problem Inconsistent observations and diversified interpretation in neural networks.

method Proposes LogDet estimator for reliable entropy approximation.

result LogDet estimator overcomes distributional diversity issues.

LogDet estimator entropy estimation neural networks information bottleneck shallow vs deep layers

2021-05-08abs ↗pdf ↗

PAC-Bayes Analysis of Multi-view Learningcs.LG

This paper presents eight PAC-Bayes bounds to analyze the generalization performance of multi-view classifiers. These bounds adopt data dependent Gaussian priors which emphasize classifiers with high view agreements. The center of the prior for the first two bounds is the origin, while the center of the prior for the t…

2014-06-21abs ↗pdf ↗

SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systemsq-fin.CP

Proposes a new deep learning framework for financial stock trading.

problem Lack of effective techniques to fuse multi-channel financial time-series data.

method Inspired by convolution transform learning, SDCF processes channels through 1-D convolutions, fuses outputs with fully-connected layers, and applies softmax classification.

result Proposed framework yields better results than state-of-the-art techniques for stock trading.

financial trading stock market deep learning convolutional transform multi-channel data

2020-11-09abs ↗pdf ↗