11:30

Efficient Algorithms For Large-Scale Datasets

Milad Niknejad (Instituto de Telecomunicações), Damien Scieur (INRIA), Francis Bach (INRIA)

Recent advances in data science has led to huge-size datasets of different kinds such as ImageNet. Having a massive amount of examples can noticeably help the improvement of machine learning algorithms. However, traditional methods for machine learning are often not scalable on these large-scale datasets. In this presentation, we address these issue by presenting two methods. In the first part of our talk, we present a general method which can be implemented on any large-scale dataset. In the second part, we focus on a specific application of image restoration with large-scale dataset of image patches.

11:45

SPARTAN advances in Compressed Sensing for MRI/Hyperspectral Imaging

Wajiha Bano (Uni. Edinburgh), Konstantinos Pitas (EPFL), Lina Zhuang (Instituto de Telecomunicações), Zhongwei Xu (Noiseless Imaging), Mike Davies (Uni. Edinburgh)

Compressed sensing (CS) has emerged as an area that opens new perspectives in signal acquisition and processing. It has provided an alternative approach to the traditional sampling theory attempting to reduce the required number of samples for successful signal reconstruction. In practice, CS aims to provide reduction in sensing resources, transmission and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. Compressed Sensing and its associated sparsity models have now influenced aspects of signal processing from signal acquisition, signal/image analysis and even learning in neural networks. In this presentation we will give a rapid summary of the work that we have done in this WorkPackage progressing the state of the art from MRI and hyperspectral imaging to 3D modelling and compressing Deep Neural Network representations.

12:00

Audio Visual Sparse Representation

Alfredo Zermini (Uni. Surrey), Cian O'Brien (Uni. Surrey), Rodrigo Pena (EPFL), Lucas Borges (Noiseless Imaging), Benjamin Ricaud (EPFL), Pierre Vandergheynst (EPFL)

13:45

Novel theory and algorithms for machine learning and signal processing

Tatiana Shpakova (INRIA), Dmitry Babichev (INRIA), Junqi Tang (Uni. Edinburgh), Francis Bach (INRIA)

In this talk we briefly present our work, which is dedicated to investigate new theory and algorithms for robust and efficient machine learning and signal processing. We begin with the discussion of the learning and inference in probabilistic models, which rely on various effective optimization methods. Then, we are going to talk about methods on efficient stochastic and big data optimization algorithms which are highly-demanded in a wide range of applications. All of these domains (learning, inference, stochastic and big data optimization) are tackled by our adjacent work packages which are more application-focused.

14:00

MacSeNet advances in Brain Imaging and Analysis

Arnold Benjamin (Uni. Edinburgh), Dhritiman Das (Technische Universität München), Cagdas Ulas (Technische Universität München), Manuel Morante (Computer Technology Institute, Athens), Christos Chatzichristos (Computer Technology Institute, Athens), Mike Davies (Uni. Edinburgh)

Brain imaging, particularly through Magnetic Resonance Imaging (MRI) has had significant impact on the study and diagnosis of the Brain. A typical MRI examination must be able to address issues such as clinical time constraints and local motion (i.e. due to breathing, beating heart etc.) because its performance is constrained by a slow data acquisition process which limits the imaging speed and makes it susceptible to motion artefacts. This has led to the development of several techniques that focus on accelerating MRI data acquisition, as well as fusing such information with other fast time modalities such as EEG for functional imaging. While much recent work has focused on compressed sensing and parallel imaging based acceleration, there are emerging techniques that have a more data driven and machine learning flavour. These aim to process data and extract the salient information via appropriate data models (tensors, manifolds, decision trees, etc.). This presentation will give a quick overview of the work that we have done in this WorkPackage developing new machine learning and data driven techniques for Quantitative and dynamic MRI, MR spectroscopy, and functional brain imaging.

14:15

Beyond Linearity and Convexity in Imaging Inverse Problems

Joshin Krishnan (Instituto de Telecomunicações), Marina Ljubenovic (Instituto de Telecomunicações), Nasser Eslahi (Tampere University of Technology), Cristóvão Cruz (Noiseless Imaging), Mario Figueiredo (Instituto de Telecomunicações)

Linearity and convexity are usual assumptions in image inverse problems (IIPs). But many practical imaging scenarios cannot be efficiently modelled in a linear or a convex framework. This work focuses on pushing research in IIPs beyond such assumptions. The objectives of this workpackage are optimization methods for non-convex formulations of non-convex IIPs, algorithms for non-convex regularization, theoretical guarantees and applications to phase imaging and image super-resolution. This talk discusses four main research directions namely 1) phase imaging via sparse coding in the complex domain, 2) patch-based, non-local and dictionary-based methods for blind image deblurring, 3) improved sparse signal recovery via adaptive correlated noise model and 4) non-local HOSVD methods for denoising and super-resolution imaging.

14:30

Audio Machine Sensing

Lucas Rencker (Uni. Surrey), Iwona Sobieraj (Uni. Surrey), Stylianos Mimilakis (Fraunhofer IDMT), Gerald Schuller (Fraunhofer IDMT)

Audio machine sensing aims at analysing and understanding sounds around us, such as music, speech, or environmental sounds. This talk will address some of the recent advances in audio machine sensing, for audio-related problems such as source separation, audio restoration or audio event detection, using techniques such as sparse decomposition, matrix factorization or deep learning.

14:45

Beyond Traditional Signals

Youngjoo Seo (EPFL), Volodymyr Miz (EPFL), Benjamin Ricaud (EPFL), Pierre Vandergheynst (EPFL)

Traditional signals such as 1-d(audio), 2-d(image) and 3-d(video) possess a well-defined structure (spatial and temporal). However, many datasets that are nowadays collected and need to be analyzed, for example texts and documents or events or activity on (social) networks, are unstructured. In addition, the average size of datasets is rapidly increasing and this requires adapted tools able to process large amount of data to retrieve information. The purpose of this work package is to formulate new models and algorithms to process data beyond the tradition framework. One direction for these new approaches is to introduce graphs which are generic data representations that create a structure on the data. The graph structure makes it possible to use traditional algorithms on unstructured data and open new doors for data analysis. A second direction is to go beyond traditional signal processing by creating new signal processing methods inspired by the emerging field of machine learning. Deep learning is now the best approach for pattern recognition in images and outperform all the previous / traditional computer vision algorithms. We investigate such approaches on structured as well as unstructured signals and datasets.