Search Results for author:
Found 9 papers, 5 papers with code
FlatNAS: optimizing Flatness in Neural Architecture Search for Out-of-Distribution Robustness
FlatNAS achieves a good trade-off between performance, OOD generalization, and the number of parameters, by using only in-distribution data in the NAS exploration.
NACHOS: Neural Architecture Search for Hardware Constrained Early Exit Neural Networks
To this end, this work presents Neural Architecture Search for Hardware Constrained Early Exit Neural Networks (NACHOS), the first NAS framework for the design of optimal EENNs satisfying constraints on the accuracy and the number of Multiply and Accumulate (MAC) operations performed by the EENNs at inference time.
A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
In real-world applications, the process generating the data might suffer from nonstationary effects (e. g., due to seasonality, faults affecting sensors or actuators, and changes in the users' behaviour).
Tiny Machine Learning for Concept Drift
For the first time in the literature, this paper introduces a Tiny Machine Learning for Concept Drift (TML-CD) solution based on deep learning feature extractors and a k-nearest neighbors classifier integrating a hybrid adaptation module able to deal with concept drift affecting the data-generating process.
RECKONition: a NLP-based system for Industrial Accidents at Work Prevention
RECKONition, which is meant to provide Natural Language Understanding, Clustering and Inference, is the result of a joint partnership with the Italian National Institute for Insurance against Accidents at Work (INAIL).
Time-Variant Variational Transfer for Value Functions
In most of the transfer learning approaches to reinforcement learning (RL) the distribution over the tasks is assumed to be stationary.
A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service
Deep-learning-as-a-service is a novel and promising computing paradigm aiming at providing machine/deep learning solutions and mechanisms through Cloud-based computing infrastructures.
Distributed Deep Convolutional Neural Networks for the Internet-of-Things
Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load.
Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss
We address the problem of detecting changes in multivariate datastreams, and we investigate the intrinsic difficulty that change-detection methods have to face when the data dimension scales.
