Publications related to Learning anisotropic filters on product graphs

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

EPFL2023

Transformer Models for Vision

Jean-Baptiste Francis Marie Juliette Cordonnier

The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins. All these successful methods depend on a gradient-based learning algorithm to train a model on massive ...

EPFL2023

Equivariant Neural Architectures for Representing and Generating Graphs

Clément Arthur Yvon Vignac

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

EPFL2023

Estimating and Improving the Robustness of Attributions in Text

Ádám Dániel Ivánkay

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 ...

EPFL2023

A multichannel versatile brain activity classification and closed loop neuromodulation system, device and method using a highly multiplexed mixed-signal front-end

Mahsa Shoaran, Uisub Shin, Bingzhao Zhu

A closed-loop neuromodulation system, including an electrode array that is implantable to a brain of a subject, analog front-end device (AFD) for selectively selecting and reading a plurality of channels from electrode array, a finite impulse response (FIR ...

2023

LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection

Bo Du, Jiancheng Yang, Rui Xu

Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection. However, existing CNN based pulmonary nodule detection methods lack the ability to capture long-range dependencies, which is vita ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Learnable filter-banks for CNN-based audio applications

Nicolas Aspert, Benjamin Ricaud, Laurent Colbois, Helena Peic Tukuljac

We investigate the design of a convolutional layer where kernels are parameterized functions. This layer aims at being the input layer of convolutional neural networks for audio applications or applications involving time-series. The kernels are defined as ...

2022

Ultra-Small Bent Meta-Waveguide Filters

Maliheh Khatibi Moghaddam, Romain Christophe Rémy Fleury

This paper demonstrates the capability of locally resonant metamaterials (LRMs) for creating compact metallic waveguide E- and H-bends, which are simultaneously working as bandpass filters. For creating these bent meta-filters, we use composite pin-pipe wa ...

IEEE2022

Unsupervised Voice Activity Detection by Modeling Source and System Information using Zero Frequency Filtering

Mathew Magimai Doss, Eklavya Sarkar

Voice activity detection (VAD) is an important pre-processing step for speech technology applications. The task consists of deriving segment boundaries of audio signals which contain voicing information. In recent years, it has been shown that voice source ...

ISCA2022

Ultra-Compact Ka-band Metamaterial Waveguide Filters, Fabricated by Lost-Wax Casting

Maliheh Khatibi Moghaddam, Romain Christophe Rémy Fleury

This paper proposes a novel technique for the design of miniaturized Ka-band filters based on locally resonant metamaterials (LRMs), which are fabricated monolithically using lost wax casting. We implement ultra-small metamaterial filters by exploiting a n ...

IEEE2021

Towards a N77 Electroacoustic Filter Using Thin Films of Crystalline Y-cut Lithium Niobate

Victor Plessky, Luis Guillermo Villanueva Torrijo, Soumya Yandrapalli

This paper presents the use of a novel type of resonators made of a thin membrane of Y-cut Lithium Niobate that has an array of interdigitated electrodes on the top, and a floating metal on the bottom. Each electrode gives rise to a quasi-independent reson ...

IEEE2021

Figlearn: Filter And Graph Learning Using Optimal Transport

Pascal Frossard, Matthias Minder, Mireille El Gheche, Zahra Farsijani

In many applications, a dataset can be considered as a set of observed signals that live on an unknown underlying graph structure. Some of these signals may be seen as white noise that has been filtered on the graph topology by a graph filter. Hence, the k ...

IEEE2021

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Fernando Perez Cruz

The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss ...

2021

Loss landscape and symmetries in Neural Networks

Mario Geiger

Neural networks (NNs) have been very successful in a variety of tasks ranging from machine translation to image classification. Despite their success, the reasons for their performance are still not well-understood. This thesis explores two main themes: lo ...

EPFL2021

Coupled VO2 oscillators circuit as analog first layer filter in convolutional neural networks

Mihai Adrian Ionescu, Kirsten Emilie Moselund, Elisabetta Corti, Siegfried Karg

In this work we present an in-memory computing platform based on coupled VO2 oscillators fabricated in a crossbar configuration on silicon. Compared to existing platforms, the crossbar configuration promises significant improvements in terms of area densit ...

2021

Learning Temporary Block-Based Bidirectional Incongruity-Aware Correlation Filters for Efficient UAV Object Tracking

Yujie He

In the field of UAV object tracking, correlation filter based approaches have received lots of attention due to their computational efficiency. The methods learn filters by the ridge regression and generate response maps to distinguish the specified target ...

2020

Crowding and the Architecture of the Visual System

Adrien Christophe Doerig

Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural ...

EPFL2020

A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras

Auke Ijspeert, Alessandro Crespi, Alexandre Bernardino, Mehmet Hasan Mutlu, Simon Lukas Hauser, Jorg Conradt, Ibrahim Youssef Youssef

The computational load associated with computer vision is often prohibitive, and limits the capacity for on-board image analysis in compact mobile robots. Replicating the kind of feature detection and neural processing that animals excel at remains a chall ...

2020

Understanding and Improving Fast Adversarial Training

Maksym Andriushchenko, Nicolas Henri Bernard Flammarion

A recent line of work focused on making adversarial training computationally efficient for deep learning models. In particular, Wong et al. (2020) showed that ℓ∞-adversarial training with fast gradient sign method (FGSM) can fail due to a phenomenon called ...

2020

A Subwavelength Microwave Bandpass Filter Based on a Chiral Waveguide

Maliheh Khatibi Moghaddam, Romain Christophe Rémy Fleury

Wave manipulation at a subwavelength scale has recently attracted significant interest, especially for low-frequency wave propagation, from acoustic to microwave devices. Recently, a few methods achieving subwavelength routing using locally resonant metama ...

IEEE Xplore2020