Non Negative

Non-negative methods in machine learning focus on developing models and algorithms that utilize only non-negative values, addressing limitations of traditional approaches that rely on negative weights or embeddings. Current research explores this constraint within various architectures, including autoencoders, capsule networks, and similarity matching frameworks, often aiming to improve efficiency, biological plausibility, or address specific challenges like open-set recognition and backdoor attacks in vision-language models. The significance lies in enhancing model interpretability, improving performance on specific hardware (like photonic neuromorphic accelerators), and potentially unlocking new learning paradigms inspired by biological systems.