Practical Federated Learning without a Server

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Atomistic machine learning has revolutionized atomistic modeling. Traditionally, molecular dynamics simulations relied on classical force fields or electronic structure calculations like Density Functional Theory. Classical force fields are fast but less p ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Traditional example-based learning methods are often limited by static, expert-created content. Hence, they face challenges in scalability, engagement, and effectiveness, as some learners might struggle to relate to the examples or find them relevant. To a ...

The increasing availability of multimodal data from electronic health records (EHR) has paved the way for deep learning methods to improve diagnosis accuracy. However, deep learning models are data-driven, requiring large-scale datasets to achieve high gen ...

Modern automobiles are equipped with advanced computing capabilities, allowing them to become powerful computing units capable of processing a large amount of data and training machine learning models. However, machine learning algorithms typically require ...

We present the identification of the non-linear dynamics of a novel hovercraft design, employing end-to-end deep learning techniques. Our experimental setup consists of a hovercraft propelled by racing drone propellers mounted on a lightweight foam base, a ...

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Learning a locomotion policy for quadruped robots has traditionally been constrained to a specific robot morphology, mass, and size. The learning process must usually be repeated for every new robot, where hyperparameters and reward function weights must b ...

Face recognition systems use the templates (extracted from users' face images) stored in the system's database for recognition. In a template inversion attack, the adversary gains access to the stored templates and tries to enter the system using images re ...

Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architectures can ...

Endogenous and exogenous uncertainties exert significant influences on energy planning. In this study, we propose a systematic methodology to excavate the uncertainty space, by combining mix-integer linear programming (MILP), Monte Carlo simulation, and ma ...

Most of our lives are conducted in the cyberspace. The human notion of privacy translates into a cyber notion of privacy on many functions that take place in the cyberspace. This article focuses on three such functions: how to privately retrieve informatio ...

We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and the cost is convex, the System Level Synthesis (SLS) approach offers an effe ...

It was recently shown that almost all solutions in the symmetric binary perceptron are isolated, even at low constraint densities, suggesting that finding typical solutions is hard. In contrast, some algorithms have been shown empirically to succeed in fin ...

Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence ...

How does the geometric representation of a dataset change after the application of each randomly initialized layer of a neural network? The celebrated Johnson-Lindenstrauss lemma answers this question for linear fully-connected neural networks (FNNs), stat ...

Air traffic management and specifically air-traffic control (ATC) rely mostly on voice communications between Air Traffic Controllers (ATCos) and pilots. In most cases, these voice communications follow a well-defined grammar that could be leveraged in Aut ...

In the literature, the task of dysarthric speech intelligibility assessment has been approached through development of different low-level feature representations, subspace modeling, phone confidence estimation or measurement of automatic speech recognitio ...

Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/explod- ing gradients during weight optimization. To avoid this problem, we propose a class of DNNs stemming from the time discretization of Hamiltonian systems. The t ...

Mixed logit models with unobserved inter-and intra-individual heterogeneity hierarchically extend standard mixed logit models by allowing tastes to vary randomly both across individuals and across choice situations encountered by the same individual. Recen ...

Practical Federated Learning without a Server

Chat with Graph Search

Advancing understanding and practical performance of machine learning interatomic potentials

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

GELEX: Generative AI-Hybrid System for Example-Based Learning

CARER - ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk Prediction

Hierarchical Federated Learning in MEC Networks with Knowledge Distillation

On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots

Vulnerability of State-of-the-Art Face Recognition Models to Template Inversion Attack

Overflow-free Compute Memories for Edge AI Acceleration

Deep excavation of the impact from endogenous and exogenous uncertainties on long-term energy planning

Private Retrieval, Computing, and Learning: Recent Progress and Future Challenges

Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs

Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems

Utterance Verification-Based Dysarthric Speech Intelligibility Assessment Using Phonetic Posterior Features

A unified framework for Hamiltonian deep neural networks

Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity