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Found 20 papers, 7 papers with code
Text-to-Image Synthesis for Any Artistic Styles: Advancements in Personalized Artistic Image Generation via Subdivision and Dual Binding
By using around 15-20 images of the target style, the approach establishes a foundational binding of a unique token identifier with a broad range of the target style.
Bayesian Continual Learning via Spiking Neural Networks
In this paper, we take steps towards the design of neuromorphic systems that are capable of adaptation to changing learning tasks, while producing well-calibrated uncertainty quantification estimates.
Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck
One of the key challenges in training Spiking Neural Networks (SNNs) is that target outputs typically come in the form of natural signals, such as labels for classification or images for generative models, and need to be encoded into spikes.
Multi-Sample Online Learning for Spiking Neural Networks based on Generalized Expectation Maximization
Spiking Neural Networks (SNNs) offer a novel computational paradigm that captures some of the efficiency of biological brains by processing through binary neural dynamic activations.
BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning
Artificial Neural Network (ANN)-based inference on battery-powered devices can be made more energy-efficient by restricting the synaptic weights to be binary, hence eliminating the need to perform multiplications.
Spiking Neural Networks -- Part III: Neuromorphic Communications
Synergies between wireless communications and artificial intelligence are increasingly motivating research at the intersection of the two fields.
Spiking Neural Networks -- Part II: Detecting Spatio-Temporal Patterns
Inspired by the operation of biological brains, Spiking Neural Networks (SNNs) have the unique ability to detect information encoded in spatio-temporal patterns of spiking signals.
Spiking Neural Networks -- Part I: Detecting Spatial Patterns
Then, we review learning algorithms and applications for SNNs that aim at mimicking the functionality of ANNs by detecting or generating spatial patterns in rate-encoded spiking signals.
End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence
This paper introduces a novel "all-spike" low-power solution for remote wireless inference that is based on neuromorphic sensing, Impulse Radio (IR), and Spiking Neural Networks (SNNs).
Multi-Sample Online Learning for Probabilistic Spiking Neural Networks
It is shown that the multiple generated output samples can be used during inference to robustify decisions and to quantify uncertainty -- a feature that deterministic SNN models cannot provide.
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits
Networks of spiking neurons and Winner-Take-All spiking circuits (WTA-SNNs) can detect information encoded in spatio-temporal multi-valued events.
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence
To this end, we introduce an online FL-based learning rule for networked on-device SNNs, which we refer to as FL-SNN.
An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be leveraged by energy-efficient hardware implementations, which can offer significant energy reductions as compared to conventional artificial neural networks (ANNs).
Solving Continual Combinatorial Selection via Deep Reinforcement Learning
IS-MDP decomposes a joint action of selecting K items simultaneously into K iterative selections resulting in the decrease of actions at the expense of an exponential increase of states.
Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning
This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel.
Learning How to Demodulate from Few Pilots via Meta-Learning
Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols.
An Introduction to Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
This paper aims at providing an introduction to SNNs by focusing on a probabilistic signal processing methodology that enables the direct derivation of learning rules leveraging the unique time encoding capabilities of SNNs.
Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing
In this work, a probabilistic model is introduced for a generalized set-up in which the synaptic time series can take values in an arbitrary alphabet and are characterized by both causal and instantaneous statistical dependencies.
Learning Data Dependency with Communication Cost
In this paper, we consider the problem of recovering a graph that represents the statistical data dependency among nodes for a set of data samples generated by nodes, which provides the basic structure to perform an inference task, such as MAP (maximum a posteriori).
Adiabatic Persistent Contrastive Divergence Learning
This paper studies the problem of parameter learning in probabilistic graphical models having latent variables, where the standard approach is the expectation maximization algorithm alternating expectation (E) and maximization (M) steps.
