Catégorie
Information engineering
Publications associées (1 000)
Aggregating Spatial and Photometric Context for Photometric Stereo
Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...
EPFL2024Robust machine learning for neuroscientific inference
Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...
EPFL2024Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning
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 ...
EPFL2024Deep Learning Theory Through the Lens of Diagonal Linear Networks
In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of -layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...
EPFL2024Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation
Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power.Simultaneously, a critical pain point arises as several computer vision applications are deployed ...
EPFL2024Understanding generalization and robustness in modern deep learning
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 ...
EPFL2024Data-Driven Control Synthesis using Koopman Operator: A Robust Approach
Alireza Karimi, Mert Eyuboglu, Nathan Russell Powell
This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...
2024Robust Data-Driven Controller Design with Finite Frequency Samples
Alireza Karimi, Philippe Louis Schuchert
Modern control synthesis methods rely on accurate models to derive a performant controller. Obtaining a good model is often a costly step, and has led to a renewed interest in data-driven synthesis methods. Frequency-response-based synthesis methods have b ...
2024Topics in statistical physics of high-dimensional machine learning
In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...
EPFL2024Data-driven LPV Control for Micro-disturbance Rejection in a Hybrid Isolation Platform
Alireza Karimi, Elias Sebastian Klauser
A novel approach for linear parameter-varying (LPV) controller synthesis for adaptive rejection of time-varying sinusoidal disturbances is proposed. Only the frequency response data of a linear time-invariant (LTI) multiple-input multiple-output (MIMO) sys ...
2024