MOOC
Introduction to Object-Oriented Programming in C++
Related publications (217)
Sparse Autoencoders for Speech Modeling and Recognition
Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...
EPFL2023Deep Learning for 3D Surface Modelling and Reconstruction
In recent years, there has been a significant revolution in the field of deep learning, which has demonstrated its effectiveness in automatically capturing intricate patterns from large datasets. However, the majority of these successes in Computer Vision ...
EPFL2023Rigidity-Aware Detection for 6D Object Pose Estimation
Mathieu Salzmann, Yinlin Hu, Jingyu Li, Rui Song
Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor ...
Los Alamitos2023Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning
We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns, elimina ...
2023BLACK-BOX ATTACKS ON IMAGE ACTIVITY PREDICTION AND ITS NATURAL LANGUAGE EXPLANATIONS
Explainable AI (XAI) methods aim to describe the decision process of deep neural networks. Early XAI methods produced visual explanations, whereas more recent techniques generate multimodal explanations that include textual information and visual represent ...
Los Alamitos2023Estimating and Improving the Robustness of Attributions in Text
End-to-end learning methods like deep neural networks have been the driving force in the remarkable progress of machine learning in recent years. However, despite their success, the deployment process of such networks in safety-critical use cases, such as ...
EPFL2023CLIP the Gap: A Single Domain Generalization Approach for Object Detection
Mathieu Salzmann, Martin Pierre Engilberge, Vidit Vidit
Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection remai ...
Los Alamitos2023Text Representation Learning for Low Cost Natural Language Understanding
Jan Frederik Jonas Florian Mai
Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...
EPFL2023Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring
Christian Leinenbach, Sergey Shevchik, Rafal Wróbel, Marc Leparoux
This study presents a self-supervised Bayesian Neural Network (BNN) framework using air-borne Acoustic Emission (AE) to identify different Laser Powder Bed Fusion (LPBF) process regimes such as Lack of Fusion, conduction mode, and keyhole without ground-tr ...
London2023Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models
Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...
EPFL2022