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

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Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coordinates the learning process by iteratively aggregating the trained models ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Self-supervised learning (SSL) has the potential to ben- efit many applications, particularly those where manually annotating data is cumbersome. One such situation is the semantic segmentation of point clouds. In this context, ex- isting methods employ co ...

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 ...

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 ...

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 ...

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 ...

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 ...

With the rapid development of the Internet of things (IoT) technology, how to effectively predict the network security situation of the IoT has become particularly important. It is difficult to quantify the IoT network situation due to a large number of hi ...

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 ...

Detection of curvilinear structures in images has long been of interest. One of the most challenging aspects of this problem is inferring the graph representation of the curvilinear network. Most existing delineation approaches first perform binary segment ...

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

Chattez avec Graph Search

Practical Federated Learning without a Server

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

Hierarchical Federated Learning in MEC Networks with Knowledge Distillation

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

ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots

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

Overflow-free Compute Memories for Edge AI Acceleration

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

Spatiotemporal Self-supervised Learning for Point Clouds in the Wild

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

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

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

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

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

An Adaptive IoT Network Security Situation Prediction Model

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

Joint Segmentation and Path Classification of Curvilinear Structures