2 papers
cs.CV2026
FTerViT: Fully Ternary Vision Transformer
Szymon RuciÅski, Pietro Bonazzi, Engin Türetken +3
Ternary Vision Transformers offer substantial model compression, however state-of-the-art methods only ternarize the encoder layers, leaving patch embeddings, LayerNorm parameters,…
cs.LG2025
SAND: One-Shot Feature Selection with Additive Noise Distortion
Pedram Pad, Hadi Hammoud, Mohamad Dia +2
Feature selection is a critical step in data-driven applications, reducing input dimensionality to enhance learning accuracy, computational efficiency, and interpretability. Existi…