Publications related to Modular Object-Oriented Neural Network Simulators and Topology Generalizations

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

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 ...

EPFL2024

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Sabine Süsstrunk, Mathieu Salzmann, Tong Zhang, Yi Wu

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

2024

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

EPFL Infoscience2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell

Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...

2024

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Romain Christophe Rémy Fleury, Janez Rus

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

2024

Gibbs sampling the posterior of neural networks

Lenka Zdeborová, Emanuele Troiani, Giovanni Piccioli

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Bristol2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pakshal Narendra Bohra

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Wenlong Liao, Yi Zhang, Zhe Yang

Recently, remarkable progress has been made in the application of machine learning (ML) techniques (e.g., neural networks) to transformer fault diagnosis. However, the diagnostic processes employed by these techniques often suffer from a lack of interpreta ...

Piscataway2024

Modular Object-Oriented Neural Network Simulators and Topology Generalizations

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Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Deep Learning Theory Through the Lens of Diagonal Linear Networks

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Gibbs sampling the posterior of neural networks

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Gibbs sampling the posterior of neural networks

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts