This repository contains the source code and results for the experiments presented in Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits.
Zero-hidden neural networks that solve non-linear problems through temporal depth, not spatial layers. 90.14% MNIST with 480 parameters. Intelligence is not depth — it's resonance. Time is the ultimate hidden layer. OdyssNet proves it.
Layer-wise ablation of Whisper ASR models. How to cut 15% of model size without losing accuracy
Does the Universal/Looped-Transformer trick (reuse one block N times instead of stacking N) work on Mamba? Weight-shared depth in a state-space model, with honest per-seed results.
Tiny language-model research lab exploring Cellular Neural Network (CeNN) recurrent state cores as alternatives to Transformer layers, with distillation, shared-weight iteration, and efficiency benchmarks.
CRS-LM: Structure-aware context reduction for tiny language models under Parameter Golf constraints
This repository contains the source code and results for the experiments presented in The Impact of Parameter Count on Sarcasm Detection Using BERT-Based Models.
Comparative PyTorch study of residual, Inception, ResNeXt and hybrid CNNs on MNIST, with parameter-efficiency analysis and Fashion-MNIST transfer.
Stagewise pairwise mixing in MLX: replace a dense n-by-n layer with L sparse pairwise stages, with closed-form forward and backward passes.
Geometry-aligned coupling for Kuramoto image generation: from O(n^2) to O(n) parameters
🧬 Neuro-Symbolic Activation Discovery: Using Genetic Programming to discover domain-specific activation functions and transfer them across scientific domains. Achieves 18-21% higher parameter efficiency with 5-6× fewer parameters.
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