Ben Keslaki

Ben Keslaki

Modern deep learning architectures excel at optimization, but only after the data has entered the network. The true bottleneck lies in preparing the right input: minimal, salient, and structured in a way that reflects the essential patterns of the data. We propose DRIFT (Data Reduction via Informative Feature Transformation), a novel preprocessing technique inspired by vibrational analysis in physical systems, to identify and extract the most resonant modes of input data prior to training. Unlike traditional models that attempt to learn amidst both signal and noise, DRIFT mimics physics perception by emphasizing informative features while discarding irrelevant elements. The result is a more compact and interpretable representation that enhances training stability and generalization performance. In DRIFT, images are projected onto a low-dimensional basis formed by spatial vibration mode shapes of plates, offering a physically grounded feature set. This enables neural networks to operate with drastically fewer input dimensions (~ 50 features on MNIST and less than 100 on CIFAR100) while achieving competitive classification accuracy. Extensive experiments across MNIST and CIFAR100 demonstrate DRIFT's superiority over standard pixel-based models and PCA in terms of training stability, resistance to overfitting, and generalization robustness. Notably, DRIFT displays minimal sensitivity to changes in batch size, network architecture, and image resolution, further establishing it as a resilient and efficient data representation strategy. This work shifts the focus from architecture engineering to input curation and underscores the power of physics-driven data transformations in advancing deep learning performance.

Ben Keslaki

Stochastic Gradient Descent (SGD) and its variants, such as ADAM, are foundational to deep learning optimization, adjusting model parameters through fixed or adaptive learning rates based on loss function gradients. However, these methods often struggle to balance adaptability and efficiency in high-dimensional, non-convex settings. This paper introduces AYLA, a novel optimization framework that enhances training dynamics via loss function transformation. AYLA applies a tunable power-law transformation to the loss, preserving critical points while scaling loss values to amplify gradient sensitivity and accelerate convergence. Additionally, we propose an effective learning rate that dynamically adapts to the transformed loss, further improving optimization efficiency. Empirical evaluations on minimizing a synthetic non-convex polynomial, solving a non-convex curve-fitting task, and performing digit classification (MNIST) and image recognition (CIFAR-100) demonstrate that AYLA consistently outperforms SGD and ADAM in both convergence speed and training stability. By reshaping the loss landscape, AYLA provides a model-agnostic enhancement to existing optimization methods, offering a promising advancement in deep neural network training.