Learning Image Descriptors with Boosting

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Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

MapPool is a dataset of 75 million potential maps and textual captions. It has been derived from CommonPool, a dataset consisting of 12 billion text-image pairs from the Internet. The images have been encoded by a vision transformer and classified into map ...

In this work, we investigate the effectiveness of deep-learning-based password guessing models for targeted attacks on human-chosen passwords. In recent years, service providers have increased the level of security of users' passwords. This is done by requ ...

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze at ...

Over the years, clinical institutes accumulated large amounts of digital slides from resected tissue specimens. These digital images, called whole slide images (WSIs), are high-resolution tissue snapshots that depict the complex interaction of cells at the ...

In this thesis, we reveal that supervised learning and inverse problems share similar mathematical foundations. Consequently, we are able to present a unified variational view of these tasks that we formulate as optimization problems posed over infinite-di ...

The ab initio determination of electronic excited state (ES) properties is the cornerstone of theoretical photochemistry. Yet, traditional ES methods become impractical when applied to fairly large molecules, or when used on thousands of systems. Machine l ...

Time series with missing data are signals encountered in important settings for machine learning. Some of the most successful prior approaches for modeling such time series are based on recurrent neural networks that transform the input and previous state ...

As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training. One popular communication-compression method for data-para ...

In the last few years, the field of lightweight cryptography has seen an influx in the number of block ciphers and hash functions being proposed. In the past there have been numerous papers that have looked at circuit level implementation of block ciphers ...

We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for the study of moving organs such as the heart. We introduce a generalized version of t ...

Although different vehicle sharing systems (VSSs) use different vehicle types, the management challenges and optimization problems to be solved are similar or even the same. This observation led us to create a generalized and holistic VSS management framew ...

Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision ...

Unmanned Aerial Vehicles are becoming increasingly popular for a broad variety of tasks ranging from aerial imagery to objects delivery. With the expansion of the areas, where drones can be efficiently used, the collision risk with other flying objects inc ...

Time travel has always been a fascinating topic in literature and physics. In cryptography, one may wonder how to keep data confidential for some time. In this dissertation, we will study how to make private information travel to the future. This dissertat ...

Detection of curvilinear structures has long been of interest due to its wide range of applications. Large amounts of imaging data could be readily used in many fields, but it is practically not possible to analyze them manually. Hence, the need for automa ...

Data from animal-borne inertial sensors are widely used to investigate several aspects of an animal's life, such as energy expenditure, daily activity patterns and behaviour. Accelerometer data used in conjunction with machine learning algorithms have b ...

Humans have a remarkable way of learning, adapting and mastering new manipulation tasks. With the current advances in Machine Learning (ML), the promise of having robots with such capabilities seems to be on the cusp of reality. Transferring human-level sk ...

We introduce OPTIK, a new practical design pattern for designing and implementing fast and scalable concurrent data structures. OPTIK relies on the commonly-used technique of version numbers for detecting conflicting concurrent operations. We show how to i ...

This thesis develops optimization-based techniques for the control of distributed energy resources to provide multiple services to the power network. It is divided into three parts. The first part of this thesis focuses on the development of a framework f ...

Learning Image Descriptors with Boosting

Chat with Graph Search

Enabling Uncertainty Estimation in Iterative Neural Networks

MapPool -Bubbling up an extremely large corpus of maps for AI

Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning

Machine Learning Security in Industry: A Quantitative Survey

Self-Supervised Learning for Patient Stratification and Survival Analysis in Computational Pathology: An Application to Colorectal Cancer

Optimization Over Banach Spaces: A Unified View on Supervised Learning and Inverse Problems

Learning the Exciton Properties of Azo-dyes

Recursive Input And State Estimation: A General Framework For Learning From Time Series With Missing Data

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization

A Deeper Look at the Energy Consumption of Lightweight Block Ciphers

Time-Dependent Deep Image Prior for Dynamic MRI

Vehicle sharing systems: A review and a holistic management framework

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Vision-based detection of aircrafts and UAVs

Time in cryptography

Learning Approach to Delineation of Curvilinear Structures in 2D and 3D Images

A novel biomechanical approach for animal behaviour recognition using accelerometers

From High-Level to Low-Level Robot Learning of Complex Tasks: Leveraging Priors, Metrics and Dynamical Systems

Optimistic Concurrency with OPTIK

Model-based predictive control methods for distributed energy resources in smart grids