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Found 14 papers, 6 papers with code
On Leaky-Integrate-and Fire as Spike-Train-Quantization Operator on Dirac-Superimposed Continuous-Time Signals
Leaky-integrate-and-fire (LIF) is studied as a non-linear operator that maps an integrable signal $f$ to a sequence $\eta_f$ of discrete events, the spikes.
SNN Architecture for Differential Time Encoding Using Decoupled Processing Time
We present synthesis and performance results showing that this architecture can be implemented for networks of more than 1000 neurons with high clock speeds on a State-of-the-Art FPGA.
Quantization in Spiking Neural Networks
In spiking neural networks (SNN), at each node, an incoming sequence of weighted Dirac pulses is converted into an output sequence of weighted Dirac pulses by a leaky-integrate-and-fire (LIF) neuron model based on spike aggregation and thresholding.
Spiking Neural Networks in the Alexiewicz Topology: A New Perspective on Analysis and Error Bounds
A central question is the adequate structure for a space of spike trains and its implication for the design of error measurements of SNNs including time delay, threshold deviations, and the design of the reinitialization mode of the leaky-integrate-and-fire (LIF) neuron model.
Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation
Our method outperforms deep embedded validation (DEV) and importance weighted validation (IWV) on all datasets, setting a new state-of-the-art performance for solving parameter choice issues in unsupervised domain adaptation with theoretical error guarantees.
On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach
Privacy-utility tradeoff remains as one of the fundamental issues of differentially private machine learning.
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning
In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the last 15 years.
The balancing principle for parameter choice in distance-regularized domain adaptation
Our approach starts with the observation that the widely-used method of minimizing the source error, penalized by a distance measure between source and target feature representations, shares characteristics with regularized ill-posed inverse problems.
Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI
A variational membership-mapping Bayesian model is used for the analytical approximations of the defined information theoretic measures for privacy-leakage, interpretability, and transferability.
Membership-Mappings for Data Representation Learning: Measure Theoretic Conceptualization
An analytical approach to the variational learning of a membership-mappings based data representation model is considered.
On Data Augmentation and Adversarial Risk: An Empirical Analysis
Data augmentation techniques have become standard practice in deep learning, as it has been shown to greatly improve the generalisation abilities of models.
On generalization in moment-based domain adaptation
Domain adaptation algorithms are designed to minimize the misclassification risk of a discriminative model for a target domain with little training data by adapting a model from a source domain with a large amount of training data.
Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data
We show, that Deep SNP is capable of successfully predicting the presence or absence of a breakpoint in large genomic windows and outperforms state-of-the-art neural network models.
Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment
A novel approach for unsupervised domain adaptation for neural networks is proposed.
