Search Results for author:
Found 126 papers, 29 papers with code
Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference
This paper proposes an extension of variational inference (VI)-based Bayesian learning that integrates calibration regularization for improved ID performance, confidence minimization for OOD detection, and selective calibration to ensure a synergistic use of calibration regularization and confidence minimization.
Neuromorphic In-Context Learning for Energy-Efficient MIMO Symbol Detection
In-context learning (ICL), a property demonstrated by transformer-based sequence models, refers to the automatic inference of an input-output mapping based on examples of the mapping provided as context.
Cell-Free Multi-User MIMO Equalization via In-Context Learning
In this work, we demonstrate that ICL can be also used to tackle the problem of multi-user equalization in cell-free MIMO systems with limited fronthaul capacity.
Neuromorphic Wireless Device-Edge Co-Inference via the Directed Information Bottleneck
An important use case of next-generation wireless systems is device-edge co-inference, where a semantic task is partitioned between a device and an edge server.
Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning
This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs.
Multi-Fidelity Bayesian Optimization With Across-Task Transferable Max-Value Entropy Search
In many applications, ranging from logistics to engineering, a designer is faced with a sequence of optimization tasks for which the objectives are in the form of black-box functions that are costly to evaluate.
Uncertainty, Calibration, and Membership Inference Attacks: An Information-Theoretic Perspective
We derive bounds on the advantage of an MIA adversary with the aim of offering insights into the impact of uncertainty and calibration on the effectiveness of MIAs.
Stochastic Spiking Attention: Accelerating Attention with Stochastic Computing in Spiking Networks
Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency.
Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning for Digital Twins
Digital twin (DT) platforms are increasingly regarded as a promising technology for controlling, optimizing, and monitoring complex engineering systems such as next-generation wireless networks.
Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis
Specifically, a quantum adversary maximizes the classifier's loss by transforming an input state $\rho(x)$ into a state $\lambda$ that is $\epsilon$-close to the original state $\rho(x)$ in $p$-Schatten distance.
Generalization and Informativeness of Conformal Prediction
A popular technique to achieve this goal is conformal prediction (CP), which transforms an arbitrary base predictor into a set predictor with coverage guarantees.
Cross-Validation Conformal Risk Control
CV-CRC is proved to offer theoretical guarantees on the average risk of the set predictor.
Low-Rank Gradient Compression with Error Feedback for MIMO Wireless Federated Learning
This paper presents a novel approach to enhance the communication efficiency of federated learning (FL) in multiple input and multiple output (MIMO) wireless systems.
Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications
Spiking neural networks (SNNs) implemented on neuromorphic processors (NPs) can enhance the energy efficiency of deployments of artificial intelligence (AI) for specific workloads.
Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates
This paper proposes a novel channel response-based scheme that, unlike the state of the art, estimates and compensates for the phase errors in the RT-generated channel responses.
ARQ for Active Learning at the Edge
Conventional retransmission (ARQ) protocols are designed with the goal of ensuring the correct reception of all the individual transmitter's packets at the receiver.
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models
In ICL, a decision on a new input is made via a direct mapping of the input and of a few examples from the given task, serving as the task's context, to the output variable.
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks
Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification.
AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory
Addressing the communication bottleneck inherent in federated learning (FL), over-the-air FL (AirFL) has emerged as a promising solution, which is, however, hampered by deep fading conditions.
Forking Uncertainties: Reliable Prediction and Model Predictive Control with Sequence Models via Conformal Risk Control
This property is leveraged to devise a novel model predictive control (MPC) framework that addresses open-loop and closed-loop control problems under general average constraints on the quality or safety of the control policy.
Towards Efficient and Trustworthy AI Through Hardware-Algorithm-Communication Co-Design
Artificial intelligence (AI) algorithms based on neural networks have been designed for decades with the goal of maximising some measure of accuracy.
Statistical Complexity of Quantum Learning
Recent years have seen significant activity on the problem of using data for the purpose of learning properties of quantum systems or of processing classical or quantum data via quantum computing.
Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing
The latest satellite communication (SatCom) missions are characterized by a fully reconfigurable on-board software-defined payload, capable of adapting radio resources to the temporal and spatial variations of the system traffic.
Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction
Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision.
Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction
Focusing on methods based on Bayesian optimization (BO), prior art has introduced an optimization scheme -- referred to as SAFEOPT -- that is guaranteed not to select any unsafe solution with a controllable probability over feedback noise as long as strict assumptions on the safety constraint function are met.
SpikeCP: Delay-Adaptive Reliable Spiking Neural Networks via Conformal Prediction
Spiking neural networks (SNNs) process time-series data via internal event-driven neural dynamics whose energy consumption depends on the number of spikes exchanged between neurons over the course of the input presentation.
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping
For additive white Gaussian noise (AWGN) channels with instantaneous per-device power constraints, prior work has demonstrated the optimality of a power control mechanism based on norm clipping.
Calibration-Aware Bayesian Learning
Deep learning models, including modern systems like large language models, are well known to offer unreliable estimates of the uncertainty of their decisions.
Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics
The recent development of scalable Bayesian inference methods has renewed interest in the adoption of Bayesian learning as an alternative to conventional frequentist learning that offers improved model calibration via uncertainty quantification.
Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning
In this work, we aim at augmenting the decisions output by quantum models with "error bars" that provide finite-sample coverage guarantees.
Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction
The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services, such as enhanced mobile broadband (eMBB) devices, by only allocating resources when necessary.
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler for reliable AI systems.
Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gating
Adaptive gating plays a key role in temporal data processing via classical recurrent neural networks (RNN), as it facilitates retention of past information necessary to predict the future, providing a mechanism that preserves invariance to time warping transformations.
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management
Optimal resource allocation in modern communication networks calls for the optimization of objective functions that are only accessible via costly separate evaluations for each candidate solution.
Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems
Type-based multiple access (TBMA) is a semantics-aware multiple access protocol for remote inference.
Calibrating AI Models for Wireless Communications via Conformal Prediction
This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees.
Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels
Simulating quantum channels is a fundamental primitive in quantum computing, since quantum channels define general (trace-preserving) quantum operations.
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems
A key challenge in the deployment of DT systems is to ensure that virtual control optimization, monitoring, and analysis at the DT are safe and reliable, avoiding incorrect decisions caused by "model exploitation".
Channel-driven Decentralized Bayesian Federated Learning for Trustworthy Decision Making in D2D Networks
Bayesian Federated Learning (FL) offers a principled framework to account for the uncertainty caused by limitations in the data available at the nodes implementing collaborative training.
Network Topology Inference based on Timing Meta-Data
If the data timings and ACK timings of two nodes -- say node 1 and node 2, respectively -- are causally related, this may be taken as evidence that node 1 is communicating to node 2 (which sends back ACK packets to node 1).
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning
Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, "open", communication systems, which play the role of the physical twin (PT).
Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction
We propose to leverage the conformal prediction framework to obtain data-driven set predictions whose calibration properties hold irrespective of the data distribution.
Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction
Conventional frequentist learning is known to yield poorly calibrated models that fail to reliably quantify the uncertainty of their decisions.
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems
This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications.
Neuromorphic Integrated Sensing and Communications
Neuromorphic computing is an emerging technology that support event-driven data processing for applications requiring efficient online inference and/or control.
Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis
It is shown that quantum gate noise induces a non-zero error-floor on the convergence error of SGD (evaluated with respect to a reference noiseless PQC), which depends on the number of noisy gates, the strength of the noise, as well as the eigenspectrum of the observable being measured and minimized.
Compressed Particle-Based Federated Bayesian Learning and Unlearning
Conventional frequentist FL schemes are known to yield overconfident decisions.
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.
Quantum Machine Learning for Distributed Quantum Protocols with Local Operations and Noisy Classical Communications
Distributed quantum information processing protocols such as quantum entanglement distillation and quantum state discrimination rely on local operations and classical communications (LOCC).
Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks
Unmanned aerial base stations (UABSs) can be deployed in vehicular wireless networks to support applications such as extended sensing via vehicle-to-everything (V2X) services.
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
In this context, we explore the application of the framework of robust Bayesian learning.
AI-Based Channel Prediction in D2D Links: An Empirical Validation
It is observed that GRUs and LSTMs present equivalent performance, and both are superior when compared to CNNs, FFNs and linear regression.
Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference
In order to enable adaptation of the receiver to the fading channel conditions, we introduce a hypernetwork to control the weights of the decoding SNN using pilots.
Learning Quantum Entanglement Distillation with Noisy Classical Communications
Therefore, an important primitive for quantum networking is entanglement distillation, whose goal is to enhance the fidelity of entangled qubits through local operations and classical communication (LOCC).
An Introduction to Quantum Machine Learning for Engineers
In the current noisy intermediate-scale quantum (NISQ) era, quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers.
Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines
Near-term noisy intermediate-scale quantum circuits can efficiently implement implicit probabilistic models in discrete spaces, supporting distributions that are practically infeasible to sample from using classical means.
Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition
An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols.
Robust Design of Rate-Splitting Multiple Access With Imperfect CSI for Cell-Free MIMO Systems
Rate-Splitting Multiple Access (RSMA) for multi-user downlink operates by splitting the message for each user equipment (UE) into a private message and a set of common messages, which are simultaneously transmitted by means of superposition coding.
Robust PAC$^m$: Training Ensemble Models Under Misspecification and Outliers
Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers.
Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization
In this work, we study a two-layer hybrid classical-quantum classifier in which a first layer of quantum stochastic neurons implementing generalized linear models (QGLMs) is followed by a second classical combining layer.
Transfer Learning for Quantum Classifiers: An Information-Theoretic Generalization Analysis
An upper bound on the optimality gap is derived in terms of the proposed task (dis)similarity measure, two R$\'e$nyi mutual information terms between classical input and quantum embedding under source and target tasks, as well as a measure of complexity of the combined space of quantum embeddings and classifiers under the source task.
Adaptive Worker Grouping For Communication-Efficient and Straggler-Tolerant Distributed SGD
Wall-clock convergence time and communication load are key performance metrics for the distributed implementation of stochastic gradient descent (SGD) in parameter server settings.
Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlo
This paper studies distributed Bayesian learning in a setting encompassing a central server and multiple workers by focusing on the problem of mitigating the impact of stragglers.
Fast Data-Driven Adaptation of Radar Detection via Meta-Learning
One approach is based on transfer learning: it first pre-trains a detector such that it works well on data collected in previously observed environments, and then it adapts the pre-trained detector to the specific current environment.
Forget-SVGD: Particle-Based Bayesian Federated Unlearning
Variational particle-based Bayesian learning methods have the advantage of not being limited by the bias affecting more conventional parametric techniques.
Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning
Accordingly, a central problem in neuromorphic computing is training spiking neural networks (SNNs) to reproduce spatio-temporal spiking patterns in response to given spiking stimuli.
Bayesian Active Meta-Learning for Black-Box Optimization
Data-efficient learning algorithms are essential in many practical applications for which data collection is expensive, e. g., for the optimal deployment of wireless systems in unknown propagation scenarios.
An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints
In vertical federated learning (FL), the features of a data sample are distributed across multiple agents.
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation
This paper proposes to leverage meta-learning in order to mitigate the requirements in terms of training data for channel fading prediction.
Multiscale Wavelet Transfer Entropy with Application to Corticomuscular Coupling Analysis
Results: Our experiments with neurophysiological signals substantiate the potential of the developed methodologies for detecting and quantifying information flow between EEG and EMG signals for subjects with and without significant CMC or GC, including non-linear cross-frequency interactions, and interactions across different temporal scales.
Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks
In this paper, we consider the problem of power control for a wireless network with an arbitrarily time-varying topology, including the possible addition or removal of nodes.
Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization
Bayesian active meta-learning is seen in experiments to significantly reduce the number of frames required to obtain efficient adaptation procedure for new frames.
Transfer Bayesian Meta-learning via Weighted Free Energy Minimization
Meta-learning optimizes the hyperparameters of a training procedure, such as its initialization, kernel, or learning rate, based on data sampled from a number of auxiliary tasks.
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.
Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning
Under a Bayesian formulation, assuming a well-specified model, the two contributions can be exactly expressed (for the log-loss) or bounded (for more general losses) in terms of information-theoretic quantities (Xu and Raginsky, 2020).
A unified PAC-Bayesian framework for machine unlearning via information risk minimization
Machine unlearning refers to mechanisms that can remove the influence of a subset of training data upon request from a trained model without incurring the cost of re-training from scratch.
Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks
Power control in decentralized wireless networks poses a complex stochastic optimization problem when formulated as the maximization of the average sum rate for arbitrary interference graphs.
Bayesian Variational Federated Learning and Unlearning in Decentralized Networks
Federated Bayesian learning offers a principled framework for the definition of collaborative training algorithms that are able to quantify epistemic uncertainty and to produce trustworthy decisions.
Collaborative Cloud and Edge Mobile Computing in C-RAN Systems with Minimal End-to-End Latency
Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers.
Fast On-Device Adaptation for Spiking Neural Networks via Online-Within-Online Meta-Learning
Spiking Neural Networks (SNNs) have recently gained popularity as machine learning models for on-device edge intelligence for applications such as mobile healthcare management and natural language processing due to their low power profile.
Joint Design of Radar Waveform and Detector via End-to-end Learning with Waveform Constraints
In the second approach, the transmitter and detector are trained simultaneously.
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.
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis
The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL).
An Information-Theoretic Analysis of the Impact of Task Similarity on Meta-Learning
The goal of the meta-learner is to ensure that the hyperparameters obtain a small loss when applied for training of a new task sampled from the task environment.
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.
Free Energy Minimization: A Unified Framework for Modelling, Inference, Learning,and Optimization
The goal of these lecture notes is to review the problem of free energy minimization as a unified framework underlying the definition of maximum entropy modelling, generalized Bayesian inference, learning with latent variables, statistical learning analysis of generalization, and local optimization.
Computation capacities of a broad class of signaling networks are higher than their communication capacities
In this paper, computation capacity of signaling networks is introduced as a fundamental limit on signaling capability and performance of such networks.
Transfer Meta-Learning: Information-Theoretic Bounds and Information Meta-Risk Minimization
In this paper, we introduce the problem of transfer meta-learning, in which tasks are drawn from a target task environment during meta-testing that may differ from the source task environment observed during meta-training.
Calibration-Aided Edge Inference Offloading via Adaptive Model Partitioning of Deep Neural Networks
This work shows that the employment of a miscalibrated early-exit DNN for offloading via model partitioning can significantly decrease inference accuracy.
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.
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 III: Neuromorphic Communications
Synergies between wireless communications and artificial intelligence are increasingly motivating research at the intersection of the two fields.
Conditional Mutual Information-Based Generalization Bound for Meta Learning
Meta-learning optimizes an inductive bias---typically in the form of the hyperparameters of a base-learning algorithm---by observing data from a finite number of related tasks.
Information-Theoretic Bounds on Transfer Generalization Gap Based on Jensen-Shannon Divergence
In transfer learning, training and testing data sets are drawn from different data distributions.
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent
This paper introduces Distributed Stein Variational Gradient Descent (DSVGD), a non-parametric generalized Bayesian inference framework for federated learning.
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).
SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks
We discuss a high-performance and high-throughput hardware accelerator for probabilistic Spiking Neural Networks (SNNs) based on Generalized Linear Model (GLM) neurons, that uses binary STT-RAM devices as synapses and digital CMOS logic for neurons.
Human Activity Recognition
Vocal Bursts Intensity Prediction
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.
Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control
When FL is implemented in wireless systems via uncoded transmission, the channel noise can directly act as a privacy-inducing mechanism.
Information Theory Networking and Internet Architecture Signal Processing Information Theory
Information-Theoretic Generalization Bounds for Meta-Learning and Applications
Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data.
Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing
Machine learning, particularly in the form of deep learning, has driven most of the recent fundamental developments in artificial intelligence.
Optimizing Over-the-Air Computation in IRS-Aided C-RAN Systems
Over-the-air computation (AirComp) is an efficient solution to enable federated learning on wireless channels.
Signal Processing Information Theory Information Theory
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.
End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning
The proposed approach is based on a meta-training phase in which the online gradient-based meta-learning of the decoder is coupled with the joint training of the encoder via the transmission of pilots and the use of a feedback link.
Decentralized Federated Learning via SGD over Wireless D2D Networks
In this paper, wireless protocols are proposed that implement DSGD by accounting for the presence of path loss, fading, blockages, and mutual interference.
Information Theory Networking and Internet Architecture Signal Processing Information Theory
Cooperative Learning via Federated Distillation over Fading Channels
Cooperative training methods for distributed machine learning are typically based on the exchange of local gradients or local model parameters.
Signal Processing Distributed, Parallel, and Cluster Computing Information Theory Information Theory
From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems
Machine learning methods adapt the parameters of a model, constrained to lie in a given model class, by using a fixed learning procedure based on data or active observations.
ITENE: Intrinsic Transfer Entropy Neural Estimator
Hence, the TE quantifies the improvement, as measured by the log-loss, in the prediction of the target sequence $Y$ that can be accrued when, in addition to the past of $Y$, one also has available past samples from $X$.
End-to-end Learning of Waveform Generation and Detection for Radar Systems
An end-to-end learning approach is proposed for the joint design of transmitted waveform and detector in a radar system.
Signal Processing
Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels
When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade of encoder, channel, and decoder.
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).
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.
Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data
Cooperative training methods for distributed machine learning typically assume noiseless and ideal communication channels.
Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning
Consider a device that is connected to an edge processor via a communication channel.
LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning
Gradient-based distributed learning in Parameter Server (PS) computing architectures is subject to random delays due to straggling worker nodes, as well as to possible communication bottlenecks between PS and workers.
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.
Coded Federated Computing in Wireless Networks with Straggling Devices and Imperfect CSI
Distributed computing platforms typically assume the availability of reliable and dedicated connections among the processors.
Information Theory Information Theory
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.
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients
In this work, the use of SNNs as stochastic policies is explored under an energy-efficient first-to-spike action rule, whereby the action taken by the RL agent is determined by the occurrence of the first spike among the output neurons.
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-Based Physical Layer Communications for Multi-agent Collaboration
In this work, it is demonstrated that the performance of the collaborative task can be improved if the agents learn jointly how to communicate and to act, even in the presence of a delay in the communication channel.
Information Theory Multiagent Systems Information Theory
Adversarial Training for Probabilistic Spiking Neural Networks
Due to the prominence of Artificial Neural Networks (ANNs) as classifiers, their sensitivity to adversarial examples, as well as robust training schemes, have been recently the subject of intense investigation.
Training Probabilistic Spiking Neural Networks with First-to-spike Decoding
Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes.
A Brief Introduction to Machine Learning for Engineers
This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning.
Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network
A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels.
