Probabilistic programming has emerged as a powerful paradigm that integrates uncertainty directly into computational models. By embedding probabilistic constructs into conventional programming ...

You can’t cheaply recompute without re-running the whole model – so KV cache starts piling up Feature Large language model ...

This brute-force scaling approach is slowly fading and giving way to innovations in inference engines rooted in core computer ...

(Nanowerk News) We are in a fascinating era where even low-resource devices, such as Internet of Things (IoT) sensors, can use deep learning algorithms to tackle complex problems such as image ...

Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design” was published by researchers at ...

We show how the notion ofmessage passing can be used to streamline the algebra and computer coding for fast approximate inference in large Bayesian semiparametric regression models. In particular, ...

The agent acquires a vocabulary of neuro-symbolic concepts for objects, relations, and actions, represented through a ...

Energy is no longer a background input but a defining constraint and increasingly, a performance metric, shaping how AI ...