Consider a setting in which devices and a server share a pre-trained mod...
Black-box zero-th order optimization is a central primitive for applicat...
Spiking neural networks (SNNs) process time-series data via internal
eve...
Deep learning models, including modern systems like large language model...
Artificial intelligence (AI) is envisioned to play a key role in future
...
Quantum machine learning is a promising programming paradigm for the
opt...
The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC)...
In the design of wireless receivers, DNNs can be combined with tradition...
When used in complex engineered systems, such as communication networks,...
Federated learning (FL) aims at optimizing a shared global model over
mu...
While being an effective framework of learning a shared model across mul...
AI tools can be useful to address model deficits in the design of
commun...
Conventional frequentist learning is known to yield poorly calibrated mo...
Unmanned aerial base stations (UABSs) can be deployed in vehicular wirel...
This work takes a critical look at the application of conventional machi...
Recent years have witnessed growing interest in the application of deep
...
An efficient data-driven prediction strategy for multi-antenna
frequency...
Standard Bayesian learning is known to have suboptimal generalization
ca...
We present the development of a cable-based passive forearm exoskeleton,...
We present a tendon-driven, active-extension thumb exoskeleton adding
op...
Predicting fading channels is a classical problem with a vast array of
a...
Meta-learning, or learning to learn, offers a principled framework for
f...
The overall predictive uncertainty of a trained predictor can be decompo...
Deep neural networks (DNNs) based digital receivers can potentially oper...
Most existing object detectors suffer from class imbalance problems that...
In order to provide therapy in a functional context, controls for wearab...
When a channel model is not available, the end-to-end training of encode...
Machine learning methods adapt the parameters of a model, constrained to...
We studied the performance of a robotic orthosis designed to assist the
...
When a channel model is available, learning how to communicate on fading...
We propose a condition-adaptive representation learning framework for th...
This paper considers an Internet-of-Things (IoT) scenario in which devic...
Consider an Internet-of-Things (IoT) scenario in which devices transmit
...
Wearable robotic hand rehabilitation devices can allow greater freedom a...
Tendon-driven hand orthoses have advantages over exoskeletons with respe...
Wearable orthoses can function both as assistive devices, which allow th...
Fully wearable hand rehabilitation and assistive devices could extend
tr...