Socially Intelligent Networks: A framework for decision making over graphs

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Demographic fairness or equity is a crucial aspect of machine learning models, particularly in critical domains such as healthcare, where errors can have severe consequences. A fair model avoids making distinctions between groups with different sensitive o ...

In 1975, Erdős asked for the maximum number of edges that an n-vertex graph can have if it does not contain two edge-disjoint cycles on the same vertex set. It is known that Turán-type results can be used to prove an upper bound of n3/2+o(1). However, this ...

Our understanding of the generalization capabilities of neural networks (NNs) is still incomplete. Prevailing explanations are based on implicit biases of gradient descent (GD) but they cannot account for the capabilities of models from gradient-free metho ...

The sum-of-squares (SOS) approximation method is a technique used in optimization problems to derive lower bounds on the optimal value of an objective function. By representing the objective function as a sum of squares in a feature space, the SOS method t ...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al. The code to use with these da ...

A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used i ...

Despite the high number of investments for data-based models in the expansion of Industry 4.0, too little effort has been made to ensure the maintenance of those models. In a data-streaming environment, data-based models are subject to concept drifts. A co ...

In medical image segmentation, supervised deep networks' success comes at the cost of requiring abundant labeled data. While asking domain experts to annotate only one or a few of the cohort's images is feasible, annotating all available images is impracti ...

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed ...

Effective caching is crucial for performance of modern-day computing systems. A key optimization problem arising in caching – which item to evict to make room for a new item – cannot be optimally solved without knowing the future. There are many classical ...

This thesis aims to explore and exploit trade-offs in integrated circuits and systems to overcome the fundamental bottlenecks faced by future data acquisition and communication systems. Specifically, we target the emerging implantable neurotechnology and t ...

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

Using Machine Learning (ML) algorithms for classification of the existing residential neighbourhoods and their spatial characteristics (e.g. density) so as to provide plausible scenarios for designing future sustainable housing is a novel application. Here ...

In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the ...

Modern machine learning methods and their applications in computer vision are known to crave for large amounts of training data to reach their full potential. Because training data is mostly obtained through humans who manually label samples, it induces a ...

We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep convolutional neural network's learned features, where we detect hierarchical c ...

In this paper we propose a novel vertex based sampling method for k-bandlimited signals lying on arbitrary graphs, that has a reasonable computational complexity and results in low reconstruction error. Our goal is to find the smallest set of vertices that ...

Much of educational research today employs a complex mixture of qualitative and quantitative analyses, both during the exploratory and confirmatory phases. However, researchers are still stuck with tools that were developed mainly for single-perspective re ...

Socially Intelligent Networks: A framework for decision making over graphs

Chat with Graph Search

Optimizing fairness and utility in healthcare machine learning models

Edge-disjoint cycles with the same vertex set

Neural Redshift: Random Networks are not Random Functions

Theory and applications of the sum-of-squares technique

Validating functional redundancy with mixed generative adversarial networks

Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" -Part 2

Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

Data-based model maintenance in the era of industry 4.0: A methodology

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models

Robust Learning-Augmented Caching: An Experimental Study

Resource Trade-Offs in Circuits and Systems: from Neurotechnology to Communications

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Residential density classification for sustainable housing development using a machine learning approach

Beam Measurements and Machine Learning at the CERN Large Hadron Collider

Quantum Chemistry Meets Machine Learning

Learning to Reduce Annotation Load

Deep Feature Factorization for Concept Discovery

A Novel Method for Sampling Bandlimited Graph Signals

Towards Novel Researcher Tooling Based on Multimodal Analytics