Publications related to Learning Image Descriptors with Boosting

Related publications (34)

Enabling Uncertainty Estimation in Iterative Neural Networks

Pascal Fua, Minh Hieu Lê, Doruk Oner, Nikita Durasov

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 ...

Curran Associates2024

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

Raimund Thies Schnürer

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 ...

2024

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

Dario Pasquini

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 ...

IEEE COMPUTER SOC2023

Machine Learning Security in Industry: A Quantitative Survey

Kathrin Grosse

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

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

Christian Robert Abbet

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 ...

EPFL2023

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

Shayan Aziznejad

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 ...

EPFL2022

Learning the Exciton Properties of Azo-dyes

Anne-Clémence Corminboeuf, Alberto Fabrizio, Sergio Vela Llausi, Ksenia Briling

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 ...

AMER CHEMICAL SOC2021

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

Robert West, Alberto Garcia Duran

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 ...

IEEE2021

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

Dan Alistarh

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 ...

Microtome Publishing2021

A Deeper Look at the Energy Consumption of Lightweight Block Ciphers

Francesco Regazzoni, Subhadeep Banik, Andrea Felice Caforio, Muhammed Fatih Balli

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 ...

IEEE2021

Time-Dependent Deep Image Prior for Dynamic MRI

Michaël Unser, Harshit Gupta, Kyong Hwan Jin, Jaejun Yoo

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 ...

Institute of Electrical and Electronics Engineers2021

Vehicle sharing systems: A review and a holistic management framework

Michel Bierlaire, Selin Ataç, Nikola Obrenovic

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 ...

2021

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Mahsa Shoaran, Bingzhao Zhu

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Vision-based detection of aircrafts and UAVs

Artem Rozantsev

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 ...

EPFL2017

Time in cryptography

Gwangbae Choi

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 ...

EPFL2020

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

Agata Justyna Mosinska

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 ...

EPFL2019

A novel biomechanical approach for animal behaviour recognition using accelerometers

Kamiar Aminian, Pritish Chakravarty

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 ...

2019

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

Nadia Barbara Figueroa Fernandez

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 ...

EPFL2019

Optimistic Concurrency with OPTIK

Rachid Guerraoui, Vasileios Trigonakis

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 ...

ACM Press2016

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

Luca Fabietti

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 ...

EPFL2019