Publications related to PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers

Related publications (20)

How to minimize the annotation effort in aerial wildlife surveys

Devis Tuia, Emanuele Dalsasso, Giacomo Günter May

Aircraft-based monitoring of wildlife is a popular way among conservation practitioners to obtain animal population counts over large areas. Nowadays, these aerial censuses are becoming increasingly scalable due to the advent of drone technology, which is ...

2025

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data

Yulun Jiang

The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today’s world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increas ...

2024

Probing the effects of broken symmetries in machine learning

Michele Ceriotti, Sergey Pozdnyakov, Marcel Florin Langer

Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properti ...

2024

Robust Training and Verification of Deep Neural Networks

Fabian Ricardo Latorre Gomez

According to the proposed Artificial Intelligence Act by the European Comission (expected to pass at the end of 2023), the class of High-Risk AI Systems (Title III) comprises several important applications of Deep Learning like autonomous driving vehicles ...

EPFL2023

The PAU Survey and Euclid: Improving broadband photometric redshifts with multi-task learning star

Frédéric Courbin, Georges Meylan

Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how t ...

EDP SCIENCES S A2023

Automated image-based generation of finite element models for masonry buildings

Katrin Beyer, Radhakrishna Achanta, Bryan German Pantoja Rosero

To predict the response of masonry buildings to various types of loads, engineers use finite element models, specifically solid-element and macro-element models. For predicting masonry responses to seismic events in particular, equivalent frame models-a su ...

SPRINGER2023

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Indaco Biazzo

Autoregressive Neural Networks (ARNNs) have shown exceptional results in generation tasks across image, language, and scientific domains. Despite their success, ARNN architectures often operate as black boxes without a clear connection to underlying physic ...

Nature Portfolio2023

A recipe for cracking the quantum scaling limit with machine learned electron densities

Mario Geiger

A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on tr ...

IOP Publishing Ltd2023

Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models

Sokratis Anagnostopoulos

Wind farm modelling is an area of rapidly increasing interest with numerous analytical and computational-based approaches developed to extend the margins of wind farm efficiency and maximise power production. In this work, we present the novel ML framework ...

Pergamon-Elsevier Science Ltd2023

STATNet: Spatial-temporal attention in the traffic prediction

Alexandre Massoud Alahi

Recent traffic flow prediction methods are lacking abilities to determine predictive features. Thus, they will propagate the error in the next timestamps. In this paper, first, we assess the role of spatial and temporal features on the traffic speed predic ...

2022

Context-Aware Image Super-Resolution Using Deep Neural Networks

Mohammad Saeed Rad

Image super-resolution is a classic ill-posed computer vision and image processing problem, addressing the question of how to reconstruct a high-resolution image from its low-resolution counterpart. Current state-of-the-art methods have improved the perfor ...

EPFL2021

Architecture Exploration and Optimization of Heterogeneous Many-Core Compute and Memory Architectures with Architectural Extensions

Yasir Mahmood Qureshi

The expeditious proliferation of Internet connectivity and the growing adoption of digital products have transformed various spheres of our everyday lives. This increased digitization of society has led to the emergence of new applications, which are deplo ...

EPFL2021

Unsupervised Stereo Matching Using Confidential Correspondence Consistency

Seungryong Kim

Stereo matching aims to perceive the 3D geometric configuration of scenes and facilitates a variety of computer vision in advanced driver assistance systems (ADAS) applications. Recently, deep convolutional neural networks (CNNs) have shown dramatic perfor ...

IEEE Institute of Electrical and Electronics Engineers2020

A cell fitness selection model for neuronal survival during development

Gioele La Manno

Developmental cell death plays an important role in the construction of functional neural circuits. In vertebrates, the canonical view proposes a selection of the surviving neurons through stochastic competition for target-derived neurotrophic signals, imp ...

Nature Research2019

Resistive Coupled VO2 Oscillators for Image Recognition

Mihai Adrian Ionescu, Igor Stolichnov, Kirsten Emilie Moselund, Elisabetta Corti

Oscillator networks are known for their interesting collective behavior such as frequency locking, phase locking, and synchronization. Compared to other artificial neural network implementations, timing rather than amplitude information is used for computa ...

IEEE2019

Learning to Reconstruct Texture-less Deformable Surfaces from a Single View

Pascal Fua, Mathieu Salzmann, Jan Bednarík

Recent years have seen the development of mature solutions for reconstructing deformable surfaces from a single image, provided that they are relatively well-textured. By contrast, recovering the 3D shape of texture-less surfaces remains an open problem, a ...

2018

Exponentially long orbits in Hopfield neural networks

Wulfram Gerstner, Johanni Michael Brea, Samuel Pavio Muscinelli

We show that Hopfield neural networks with synchronous dynamics and asymmetric weights admit stable orbits that form sequences of maximal length. For N units, these sequences have length T = 2^N; that is, they cover the full state space. We present a mathe ...

Massachusetts Institute of Technology Press2017

Inverse solutions for brain-computer interfaces: Effects of regularisation on localisation and classification

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Mohit Kumar Goel

Estimation of intracranial sources, using inverse solutions methods, has been proposed as a mean to improve performance in non-invasive brain-computer interfaces. These methods estimate the activity of a large number of neural sources from a smaller number ...

2017

Mesoscale and clusters of synchrony in networks of bursting neurons

Martin Hasler, Igor Belykh

We study the role of network architecture in the formation of synchronous clusters in synaptically coupled networks of bursting neurons. We give a simple combinatorial algorithm that finds the largest synchronous clusters from the network topology. We demo ...

American Institute of Physics2011

Consciousness & the Small Network Argument

Michael Herzog, Wulfram Gerstner

The last decade has experienced a vivid enthusiasm to unravel the mystery of consciousness believed to be one of the major puzzles of humankind. We share this enthusiasm. Still,we feel that current models are incomplete suffering from a problem that we cal ...

Elsevier2007