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Found 13 papers, 2 papers with code
Invariant Representations in Deep Learning for Optoacoustic Imaging
Image reconstruction in optoacoustic tomography (OAT) is a trending learning task highly dependent on measured physical magnitudes present at sensing time.
An Asymptotically Equivalent GLRT Test for Distributed Detection in Wireless Sensor Networks
Nevertheless, its asymptotic performance is proved to be identical to the original GLRT, showing that the statistically dependence of the measurements has no impact on the detection performance in the asymptotic scenario.
Cross-domain Sentiment Classification in Spanish
In this work we perform a study on the ability of a classification system trained with a large database of product reviews to generalize to different Spanish domains.
An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks
When the source is present, the computation of the joint PDF of the energy measurements at the nodes is a challenging problem.
Combining band-frequency separation and deep neural networks for optoacoustic imaging
In this paper we consider the problem of image reconstruction in optoacoustic tomography.
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning
Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data is ever exchanged either between the clients or between the clients and the central server.
PACMAN: PAC-style bounds accounting for the Mismatch between Accuracy and Negative log-loss
In this work, we introduce an analysis based on point-wise PAC approach over the generalization gap considering the mismatch of testing based on the accuracy metric and training on the negative log-loss.
The Role of Mutual Information in Variational Classifiers
In practice, this behaviour is controlled by various--sometimes heuristics--regularization techniques, which are motivated by developing upper bounds to the generalization error.
On fully-distributed composite tests with general parametric data distributions in sensor networks
Interestingly, despite the fact that the L-MP is simpler and more efficient to implement than the GLR test, we obtain some conditions under which the L-MP has superior asymptotic performance to the GLR test.
Understanding the Behaviour of the Empirical Cross-Entropy Beyond the Training Distribution
Our main result is that the testing gap between the empirical cross-entropy and its statistical expectation (measured with respect to the testing probability law) can be bounded with high probability by the mutual information between the input testing samples and the corresponding representations, generated by the encoder obtained at training time.
The Role of Information Complexity and Randomization in Representation Learning
This paper presents a sample-dependent bound on the generalization gap of the cross-entropy loss that scales with the information complexity (IC) of the representations, meaning the mutual information between inputs and their representations.
Compression-Based Regularization with an Application to Multi-Task Learning
This paper investigates, from information theoretic grounds, a learning problem based on the principle that any regularity in a given dataset can be exploited to extract compact features from data, i. e., using fewer bits than needed to fully describe the data itself, in order to build meaningful representations of a relevant content (multiple labels).
Collaborative Information Bottleneck
On the other hand, in CDIB there are two cooperating encoders which separately observe $X_1$ and $X_2$ and a third node which can listen to the exchanges between the two encoders in order to obtain information about a hidden variable $Y$.
