DeepNC: Deep Generative Network Completion

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Testing graph cluster structure has been a central object of study in property testing since the foundational work of Goldreich and Ron [STOC’96] on expansion testing, i.e. the problem of distinguishing between a single cluster (an expander) and a graph th ...

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although rec ...

This paper investigates the potential impact of deep generative models on the work of creative professionals. We argue that current generative modeling tools lack critical features that would make them useful creativity support tools, and introduce our own ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Generative SBDD methods leverage structural data of drugs with their protein targets to propose new dru ...

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

In engineering, oscillatory instabilities and resonances are often considered undesirable flow features and measures are taken to avoid them. This may include avoiding certain parametric regions or implementing control and mitigation strategies. However, t ...

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

Touchscreens are nowadays the preferred choice for user interfaces in consumer electronics. Significant technological advances have been made in terms of touch sensing and visual quality. However, the haptic feedback offered by commercial products is still ...

Author summaryIn recent years, the application of deep learning represented a breakthrough in the mass spectrometry (MS) field by improving the assignment of the correct sequence of amino acids from observable MS spectra without prior knowledge, also known ...

Bioaerosols are emitted from various sources into the indoor environment and can positively and negatively impact human health. Humans are the major source of bioaerosol emissions indoors, specifically for bacteria. However, efficient sampling to guarantee ...

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 ...

In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning-a collaborative machine learning framework aimed at addressing the main limitations of federated learning. We introduce a suite of novel attacks for both passive and ...

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals. Approach. EEG recordings of 17 heal ...

Most modern imaging systems incorporate a computational pipeline to infer the image of interest from acquired measurements. The Bayesian approach to solve such ill-posed inverse problems involves the characterization of the posterior distribution of the im ...

DeepNC: Deep Generative Network Completion

Chat with Graph Search

Approximating Dasgupta Cost in Sublinear Time from a Few Random Seeds

Text-to-Microstructure Generation Using Generative Deep Learning

Fashioning Creative Expertise with Generative AI: Graphical Interfaces for GAN-Based Design Space Exploration Better Support Ideation Than Text Prompts for Diffusion Models

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Structure-based drug design with equivariant diffusion models

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Understanding generalization and robustness in modern deep learning

Random matrix methods for high-dimensional machine learning models

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Special Issue: Smart Resilient Manufacturing

Self-sustained dynamics and forced resonant oscillations in flows: cross-junction jets and sloshing liquids

Robust Outlier Rejection for 3D Registration with Variational Bayes

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Deep Learning for Localized-Haptic Feedback in Tactile Surfaces

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Comparison of two methods for bioaerosol sampling and characterization in a low-biomass chamber environment

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

On the (In)security of Peer-to-Peer Decentralized Machine Learning

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Bayesian Inversion for Nonlinear Imaging Models Using Deep Generative Priors