Distributed and Component Oriented Tools for Communication Networks Using Web Services

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This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayesoptimal test error for classification while obtaining (nearly) zero-trai ...

The current advances in the Internet of Things (IoT) and the solutions being offered by this technology have accounted IoT among the top ten technologies that will transform the global economy by 2030. IoT is a state-of-the-art paradigm that has developed ...

We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dime ...

The landscape of computing is changing, thanks to the advent of modern networking equipment that allows machines to exchange information in as little as one microsecond. Such advancement has enabled microsecond-scale distributed computing, where entire dis ...

Knowledge of a program's input format is essential for effective input generation in fuzzing. Automated input format reverse engineering represents an attractive but challenging approach to learning the format. In this paper, we address several challenges ...

The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial intelligence, the training phase rema ...

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

We investigate methods for manipulating classifier explanations while keeping the predictions unchanged. Our focus is on using a sparse attack, which seeks to alter only a minimal number of input features. We present an efficient and novel algorithm for co ...

This paper studies the infinite-width limit of deep linear neural networks (NNs) initialized with random parameters. We obtain that, when the number of parameters diverges, the training dynamics converge (in a precise sense) to the dynamics obtained from a ...

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

Meta-learning is the core capability that enables intelligent systems to rapidly generalize their prior ex-perience to learn new tasks. In general, the optimization-based methods formalize the meta-learning as a bi-level optimization problem, that is a nes ...

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

Deep digital twins (DDTs) are deep neural networks that encode the behavior of complex physical systems. DDTs are excellent system representations due to their ability to continuously adapt to operational changes and their capability to capture complex rel ...

Distributed and Component Oriented Tools for Communication Networks Using Web Services

Chat with Graph Search

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Benign Overfitting in Deep Neural Networks under Lazy Training

A review of the security vulnerabilities and countermeasures in the Internet of Things solutions: A bright future for the Blockchain

ReLU Neural Network Galerkin BEM

Reliable Microsecond-Scale Distributed Computing

AIFORE: Smart Fuzzing Based on Automatic Input Format Reverse Engineering

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

Deep Learning Generalization with Limited and Noisy Labels

On the number of regions of piecewise linear neural networks

Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

Infinite-width limit of deep linear neural networks

Privacy-preserving federated neural network training and inference

Gibbs sampling the posterior of neural networks

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Safe Deep Neural Networks

RMAML: Riemannian meta-learning with orthogonality constraints

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Text Representation Learning for Low Cost Natural Language Understanding

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Digital Twin Generalization with Meta and Geometric Deep Learning