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Found 7 papers, 3 papers with code
A preferential interpretation of MultiLayer Perceptrons in a conditional logic with typicality
In this paper we investigate the relationships between a multipreferential semantics for defeasible reasoning in knowledge representation and a multilayer neural network model.
Benchmarking FedAvg and FedCurv for Image Classification Tasks
Data carry a value that might vanish when shared with others; the ability to avoid sharing the data enables industrial applications where security and privacy are of paramount importance, making it possible to train global models by implementing only local policies which can be run independently and even on air-gapped data centres.
Experimenting with Normalization Layers in Federated Learning on non-IID scenarios
Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions.
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning
Federated Learning (FL) and Edge Inference are examples of DML.
Invariant Representations with Stochastically Quantized Neural Networks
In this paper, we propose a methodology for direct computation of the mutual information between a neural layer and a sensitive attribute.
Fair Interpretable Representation Learning with Correction Vectors
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information.
Partitioned Least Squares
In this paper we propose a variant of the linear least squares model allowing practitioners to partition the input features into groups of variables that they require to contribute similarly to the final result.
