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Towards Generalized Entropic Sparsification for Convolutional Neural Networks
Convolutional neural networks (CNNs) are reported to be overparametrized.
Gauge-optimal approximate learning for small data classification problems
As a potential solution to this problem, here we exploit the idea of reducing and rotating the feature space in a lower-dimensional gauge and propose the Gauge-Optimal Approximate Learning (GOAL) algorithm, which provides an analytically tractable joint solution to the dimension reduction, feature segmentation and classification problems for small data learning problems.
On existence, uniqueness and scalability of adversarial robustness measures for AI classifiers
Simply-verifiable mathematical conditions for existence, uniqueness and explicit analytical computation of minimal adversarial paths (MAP) and minimal adversarial distances (MAD) for (locally) uniquely-invertible classifiers, for generalized linear models (GLM), and for entropic AI (EAI) are formulated and proven.
Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction
In many data science applications, the objective is to extract appropriately-ordered smooth low-dimensional data patterns from high-dimensional data sets.
Robust learning of data anomalies with analytically-solvable entropic outlier sparsification
Entropic Outlier Sparsification (EOS) is proposed as a robust computational strategy for the detection of data anomalies in a broad class of learning methods, including the unsupervised problems (like detection of non-Gaussian outliers in mostly-Gaussian data) and in the supervised learning with mislabeled data.
On a scalable entropic breaching of the overfitting barrier in machine learning
Overfitting and treatment of "small data" are among the most challenging problems in the machine learning (ML), when a relatively small data statistics size $T$ is not enough to provide a robust ML fit for a relatively large data feature dimension $D$.
