Publications related to From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

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

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 ...

Berlin2023

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

Willem Anna Mark Wybo, Bernd Albert Illing, Matthias Chinyen Tsai, Walter Senn

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contex-tual sta ...

Washington2023

Neural ADMIXTURE for rapid genomic clustering

Albert Dominguez Mantes

Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster ...

SPRINGERNATURE2023

Continuous-variable neural network quantum states and the quantum rotor model

Giuseppe Carleo

We initiate the study of neural network quantum state algorithms for analyzing continuous-variable quantum systems in which the quantum degrees of freedom correspond to coordinates on a smooth manifold. A simple family of continuous-variable trial wavefunc ...

SPRINGERNATURE2023

Incremental Learning in Diagonal Linear Networks

Raphaël Jean Berthier

Diagonal linear networks (DLNs) are a toy simplification of artificial neural networks; they consist in a quadratic reparametrization of linear regression inducing a sparse implicit regularization. In this paper, we describe the trajectory of the gradient ...

Brookline2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar

There is a strong incentive to develop computational pathology models to i) ease the burden of tissue typology annotation from whole slide histological images; ii) transfer knowledge, e.g., tissue class separability from the withheld source domain to the d ...

2023

Improved calorimetric particle identification in NA62 using machine learning techniques

Alessandro Mapelli, Radoslav Marchevski, Alina Kleimenova

Measurement of the ultra-rare K+ -> pi(+)nu(nu) over bar over bar decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decis ...

New York2023

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Rachid Guerraoui, Andrei Kucharavy, Matteo Monti

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

New York2023

Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems

Olga Fink, Mengjie Zhao, Keivan Faghih Niresi

The use of Internet of Things (IoT) sensors for air pollution monitoring has significantly increased, resulting in the deployment of low-cost sensors. Despite this advancement, accurately calibrating these sensors in uncontrolled environmental conditions r ...

Institute of Electrical and Electronics Engineers2023

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

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

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

Neural ADMIXTURE for rapid genomic clustering

Continuous-variable neural network quantum states and the quantum rotor model

Incremental Learning in Diagonal Linear Networks

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

Improved calorimetric particle identification in NA62 using machine learning techniques

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems

Improved calorimetric particle identification in NA62 using machine learning techniques

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

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

Continuous-variable neural network quantum states and the quantum rotor model

Neural ADMIXTURE for rapid genomic clustering

Incremental Learning in Diagonal Linear Networks

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search