10 papers
Beyond Neural Networks: Symbolic Reasoning over Wavelet Logic Graph Signals
Andrew Kiruluta, Andreas Lemos, Priscilla Burity
We present a fully non neural learning framework based on Graph Laplacian Wavelet Transforms (GLWT). Unlike traditional architectures that rely on convolutional, recurrent, or atte…
Operator-Based Machine Intelligence: A Hilbert Space Framework for Spectral Learning and Symbolic Reasoning
Andrew Kiruluta, Andreas Lemos, Priscilla Burity
Traditional machine learning models, particularly neural networks, are rooted in finite-dimensional parameter spaces and nonlinear function approximations. This report explores an…
History-Aware Cross-Attention Reinforcement: Self-Supervised Multi Turn and Chain-of-Thought Fine-Tuning with vLLM
Andrew Kiruluta, Andreas Lemos, Priscilla Burity
We present CAGSR-vLLM-MTC, an extension of our Self-Supervised Cross-Attention-Guided Reinforcement (CAGSR) framework, now implemented on the high-performance vLLM runtime, to addr…
FourierNAT: A Fourier-Mixing-Based Non-Autoregressive Transformer for Parallel Sequence Generation
Andrew Kiruluta, Eric Lundy, Andreas Lemos
We present FourierNAT, a novel non-autoregressive Transformer (NAT) architecture that employs Fourier-based mixing in the decoder to generate output sequences in parallel. While tr…
A Hybrid Wavelet-Fourier Method for Next-Generation Conditional Diffusion Models
Andrew Kiruluta, Andreas Lemos
We present a novel generative modeling framework,Wavelet-Fourier-Diffusion, which adapts the diffusion paradigm to hybrid frequency representations in order to synthesize high-qual…
State Fourier Diffusion Language Model (SFDLM): A Scalable, Novel Iterative Approach to Language Modeling
Andrew Kiruluta, Andreas Lemos
In recent years, diffusion based methods have emerged as a powerful paradigm for generative modeling. Although discrete diffusion for natural language processing has been explored…
A Self-Supervised Reinforcement Learning Approach for Fine-Tuning Large Language Models Using Cross-Attention Signals
Andrew Kiruluta, Andreas Lemos, Priscilla Burity
We propose a novel reinforcement learning framework for post training large language models that does not rely on human in the loop feedback. Instead, our approach uses cross atten…
Hierarchical Attention Diffusion Networks with Object Priors for Video Change Detection
Andrew Kiruluta, Eric Lundy, Andreas Lemos
We present a unified change detection pipeline that combines instance level masking, multi\-scale attention within a denoising diffusion model, and per pixel semantic classificatio…
Unsupervised Machine Learning Hybrid Approach Integrating Linear Programming in Loss Function: A Robust Optimization Technique
Andrew Kiruluta, Andreas Lemos
This paper presents a novel hybrid approach that integrates linear programming (LP) within the loss function of an unsupervised machine learning model. By leveraging the strengths…
Beyond Self Attention: A Subquadratic Fourier Wavelet Transformer with Multi Modal Fusion
Andrew Kiruluta, Andreas Lemos, Eric Lundy
We revisit the use of spectral techniques to replaces the attention mechanism in Transformers through Fourier Transform based token mixing, and present a comprehensive and novel re…