7 papers
HAS-VQ: Hessian-Adaptive Sparse Vector Quantization for High-Fidelity LLM Compression
Vladimer Khasia
Post-training quantization is essential for deploying Large Language Models (LLMs) on resource-constrained devices. However, standard integer quantization (e.g., INT4) fundamentall…
Spectral-Window Hybrid (SWH)
Vladimer Khasia
Scaling sequence modeling to extreme contexts requires balancing computational efficiency with representational expressivity. While Transformers provide precise retrieval via the a…
Dynamic Subspace Composition: Efficient Adaptation via Contractive Basis Expansion
Vladimer Khasia
Mixture of Experts (MoE) models scale capacity but often suffer from representation collapse and gradient instability. We propose Dynamic Subspace Composition (DSC), a framework th…
DeepVekua: Geometric-Spectral Representation Learning for Physics-Informed Fields
Vladimer Khasia
We present DeepVekua, a hybrid architecture that unifies geometric deep learning with spectral analysis to solve partial differential equations (PDEs) in sparse data regimes. By le…
The Adaptive Vekua Cascade: A Differentiable Spectral-Analytic Solver for Physics-Informed Representation
Vladimer Khasia
Coordinate-based neural networks have emerged as a powerful tool for representing continuous physical fields, yet they face two fundamental pathologies: spectral bias, which hinder…
The Vekua Layer: Exact Physical Priors for Implicit Neural Representations via Generalized Analytic Functions
Vladimer Khasia
Implicit Neural Representations (INRs) have emerged as a powerful paradigm for parameterizing physical fields, yet they often suffer from spectral bias and the computational expens…