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Found 6 papers, 2 papers with code
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning
In this paper, we propose a novel framework, dubbed Detaux, whereby a weakly supervised disentanglement procedure is used to discover new unrelated classification tasks and the associated labels that can be exploited with the principal task in any Multi-Task Learning (MTL) model.
A Machine Learning-oriented Survey on Tiny Machine Learning
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures.
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0
Industry 4. 0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity.
Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications
Many recent pattern recognition applications rely on complex distributed architectures in which sensing and computational nodes interact together through a communication network.
I-SPLIT: Deep Network Interpretability for Split Computing
A neuron is important if its gradient with respect to the correct class decision is high.
Toward Smart Doors: A Position Paper
In particular, a smart door system predicts the intention of people near the door based on the social context of the surrounding environment and then makes rational decisions about whether or not to open the door.
