SST vs. GaLore: The Battle for the Most Efficient AI Brain
30 Oct 2025
SST outperforms GaLore in compressing large language models, maintaining accuracy and efficiency for next-gen AI inference.
Here’s Why AI Researchers Are Talking About Sparse Spectral Training
30 Oct 2025
Discover how Sparse Spectral Training (SST) enhances deep learning with low-rank optimization and zero-gradient distortion.
Can Sparse Spectral Training Make AI More Accessible?
30 Oct 2025
Efficient, eco-friendly, and powerful — Sparse Spectral Training boosts LLM performance while cutting memory use and training costs.
SST vs LoRA: A Leaner, Smarter Way to Train AI Models
30 Oct 2025
SST delivers full-rank performance with fewer parameters, outperforming LoRA across NLP and graph tasks.
Generalizing Sparse Spectral Training Across Euclidean and Hyperbolic Architectures
29 Oct 2025
Sparse Spectral Training boosts transformer stability and efficiency, outperforming LoRA and ReLoRA across neural network architectures.
Why Sparse Spectral Training Might Replace LoRA in AI Model Optimization
29 Oct 2025
Sparse Spectral Training (SST) boosts AI efficiency with selective spectral updates—balancing speed, accuracy, and memory use.
Breaking Down Low-Rank Adaptation and Its Next Evolution, ReLoRA
29 Oct 2025
Learn how LoRA and ReLoRA improve AI model training by cutting memory use and boosting efficiency without full-rank computation.
New Training Method Cuts Neural Network Memory Costs Without Losing Accuracy
29 Oct 2025
A new AI training method, Sparse Spectral Training (SST), reduces memory use while matching full-rank performance in large language models.
Improving Deep Learning with Lorentzian Geometry: Results from LHIER Experiments
28 Oct 2025
With improved accuracy, stability, and speed of training, new Lorentz hyperbolic approaches (LHIER+) improve AI performance on classification and hierarchy task