121 citations · 260 across the 20 of their papers we have counts for
22 papers
SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild
Jiyong Moon, Seongsik Park
One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambi…
Bayesian Continual Learning via Spiking Neural Networks
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification. Neuromorphic engine…
Learning to Broadcast for Ultra-Reliable Communication with Differential Quality of Service via the Conditional Value at Risk
Roy Karasik, Osvaldo Simeone, Hyeryung Jang +1
Broadcast/multicast communication systems are typically designed to optimize the outage rate criterion, which neglects the performance of the fraction of clients with the worst cha…
Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck
Nicolas Skatchkovsky, Osvaldo Simeone, Hyeryung Jang
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 imag…
Multi-Sample Online Learning for Spiking Neural Networks based on Generalized Expectation Maximization
Hyeryung Jang, Osvaldo Simeone
Spiking Neural Networks (SNNs) offer a novel computational paradigm that captures some of the efficiency of biological brains by processing through binary neural dynamic activation…
BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning
Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
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…
Spiking Neural Networks -- Part III: Neuromorphic Communications
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Synergies between wireless communications and artificial intelligence are increasingly motivating research at the intersection of the two fields. On the one hand, the presence of m…
Spiking Neural Networks -- Part II: Detecting Spatio-Temporal Patterns
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
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
Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
Spiking Neural Networks (SNNs) are biologically inspired machine learning models that build on dynamic neuronal models processing binary and sparse spiking signals in an event-driv…
End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
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 (S…
Multi-Sample Online Learning for Probabilistic Spiking Neural Networks
Hyeryung Jang, Osvaldo Simeone
Spiking Neural Networks (SNNs) capture some of the efficiency of biological brains for inference and learning via the dynamic, online, event-driven processing of binary time series…
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits
Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
Networks of spiking neurons and Winner-Take-All spiking circuits (WTA-SNNs) can detect information encoded in spatio-temporal multi-valued events. These are described by the timing…
An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
Hyeryung Jang, Osvaldo Simeone, Brian Gardner +1
Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synap…
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Spiking Neural Networks (SNNs) offer a promising alternative to conventional Artificial Neural Networks (ANNs) for the implementation of on-device low-power online learning and inf…
Solving Continual Combinatorial Selection via Deep Reinforcement Learning
Hyungseok Song, Hyeryung Jang, Hai H. Tran +5
We consider the Markov Decision Process (MDP) of selecting a subset of items at each step, termed the Select-MDP (S-MDP). The large state and action spaces of S-MDPs make them intr…
Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning
Sangwoo Park, Hyeryung Jang, Osvaldo Simeone +1
This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel. Devices are characteriz…
Learning How to Demodulate from Few Pilots via Meta-Learning
Sangwoo Park, Hyeryung Jang, Osvaldo Simeone +1
Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is…
An Introduction to Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
Hyeryung Jang, Osvaldo Simeone, Brian Gardner +1
Spiking Neural Networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synap…
Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing
Hyeryung Jang, Osvaldo Simeone
Neuromorphic hardware platforms, such as Intel's Loihi chip, support the implementation of Spiking Neural Networks (SNNs) as an energy-efficient alternative to Artificial Neural Ne…
Simulation-based Distributed Coordination Maximization over Networks
Hyeryung Jang, Jinwoo Shin, Yung Yi
In various online/offline multi-agent networked environments, it is very popular that the system can benefit from coordinating actions of two interacting agents at some cost of coo…
Learning Data Dependency with Communication Cost
Hyeryung Jang, HyungSeok Song, Yung Yi
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 provide…
Adiabatic Persistent Contrastive Divergence Learning
Hyeryung Jang, Hyungwon Choi, Yung Yi +1
This paper studies the problem of parameter learning in probabilistic graphical models having latent variables, where the standard approach is the expectation maximization algorith…