Tag: Machine Learning

Projects input vectors into Minkowski spacetime and computes complex-valued pseudo-distances to learned reference points, encoding causal structure through timelike vs. spacelike intervals.

Comprehensive technical analysis of the InterpolatedDensityEntropy neural network layer, including forward/backward passes, gradient derivations, stability analysis, and reference implementations.

A novel computational framework combining wavelet-decomposed geographic topology with deep neural cellular automata for learning geospatial dynamics from observational data

A novel regularization framework for large language models using spherical harmonic decomposition to control semantic frequencies and enable principled hallucination suppression.

A novel computational framework for automated discovery of analytical maximum entropy distribution families using genetic programming validated against parameterizable data generators.

A universal framework combining differentiable basis transforms with trust-region optimization for adaptive dropout regularization in neural networks

Exploring the profound parallels between quantum decoherence and neural network dropout to develop unified frameworks for robust information processing across computational paradigms processing

A novel computational paradigm proposing Probabilistic Neural Substrates (PNS) that maintain continuous probability distributions through cross-entropy optimization, enabling self-organizing recurrent intelligence with unprecedented interpretability and uncertainty quantification.

Revolutionary synthesis of geometric optimization with Probabilistic Neural Substrates, creating self-organizing intelligent systems with unprecedented mathematical elegance.

A novel dual-constraint training methodology that preserves intellectual diversity while enabling continued learning in neural networks through adaptive anomaly preservation and trust region approaches.

A theoretical framework proposing that neural network dropout functions as cognitive analog to quantum decoherence through epistemic filtering

A framework exploiting neural network permutation symmetries for post-training optimization, enabling structured pruning and improved interpretability

A novel hierarchical ensemble architecture for modeling semantic drift as a dynamic, multi-agent ecosystem with specialized interpretive agents.

Comprehensive analysis of quantum field theory generalizations using Taylor expansion frameworks, covering effective field theory, experimental constraints, and machine learning applications.

Comprehensive software framework for implementing trust region methods in neural network optimization with Java MindsEye library

Novel technique for generating mathematically symmetric textures using neural networks with geometric constraints, exploring Euclidean, spherical, and hyperbolic geometries.

A novel optimization algorithm that improves deep neural network training by decomposing gradients into layer-wise components and using meta-optimization to find optimal combinations.

Novel optimization algorithm hybridizing L-BFGS with gradient descent through quadratic interpolation and magnitude-based normalization

Analysis of the overlooked MindsEye deep learning framework and its implications for training data bias in AI systems

Novel method for modeling probability distributions using volumetric density trees with quadratic polynomial constraints, addressing complex geometric boundaries in 2-4D spaces.

Explore alternative loss functions for regression beyond least-squares, including zero-loss zones, robust methods, and practical applications in engineering and ML.

Novel extension to decision tree methodology that models joint probability distributions using cross-entropy optimization between prior and posterior distributions.

A novel approach to compressing large-scale n-gram language models using hierarchical structural expectations

A comprehensive methodology for implementing scalable 2D convolution layers in neural networks, addressing GPU memory constraints through dynamic partitioning

A novel framework unifying compression-based text classification with entropy-optimized data structures for efficient, interpretable AI systems