gradient-free

Official implementation of Score-CAM in PyTorch

My optimization(With and Without gradient) library

Code for FLEX, a fast, adaptive and flexible model-based reinforcement learning exploration algorithm.

Continuous domain Particle Swarm Optimization (PSO) with inertia weight decay and cognitive/social acceleration.

Continuous domain Particle Swarm Optimization (PSO) with inertia weight decay and cognitive/social acceleration.

Fast runtime for Random Optimization at scale.

Your language model forgets everything. Sillage gives it a 4 MB memory that persists across sessions, no gradients, no fine-tuning, no growing index. Beats an unbounded kNN-LM at 1/13 the storage (perplexity 31 → 17). CPU-only, fully reproducible, four preprints with Zenodo DOIs.

Similarity Differences and Uniqueness Explainable AI method

Tensorflow Optimizer using the SPSA method

This repo shows a MATLAB implementation of score-CAM (Wang et al., CVPR workshop, 2020). This method is the first gradient-free CAM-based visualization method for explaining CNN decision.

Repository containing code to run Score-CAM algorithm available on https://arxiv.org/pdf/1910.01279v1.pdf.

ADMM approach to train neural networks without gradient.

VRAXION public SDK and boundary docs for the private engine.

A Julia implementation of Simultaneous Perturbation Stochastic Approximation

Transformer alternatives on minimal hardware: a ternary {-1,0,+1} state-space LM with a custom C/Vulkan training pipeline (no PyTorch, no CUDA), and BSR, a brain-inspired gradient-free model that learns by counting and one-shot memory writes on a laptop CPU.

Drift-Bounded Spectral Updates for Deep Local Learning

DC-GEM: Differentiable Core + Gradient-Free Episodic Memory. Hybrid CLS architecture (pre-trained transformer core + episodic memory) for fact recall and coding tasks. Demonstrates GEM enables recall the core cannot do alone, with zero catastrophic forgetting. Stability-Plasticity Dilemma documented at 0.6B scale.

Gradient-free RL for LunarLander-v3 with population search, zeroth-order optimization, JAX, and parallel runs.

Deep neural network trained with Particle Swarm Optimization (gradient-free), with the dnnpso package and benchmarks