Publications associées à Boltzmann machine

Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction

We introduce the task of action-driven stochastic human motion prediction, which aims to predict multiple plausible future motions given a sequence of action labels and a short motion history. This differs from existing works, which predict motions that ei ...

IEEE COMPUTER SOC2022

Self-supervised Segmentation via Background Inpainting

Pascal Fua, Mathieu Salzmann, Isinsu Katircioglu, Helge Jochen Rhodin, Victor Constantin

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively e ...

2022

The Solution of the Deep Boltzmann Machine on the Nishimori Line

Emanuele Mingione, Diego Alberici

The deep Boltzmann machine on the Nishimori line with a finite number of layers is exactly solved by a theorem that expresses its pressure through a finite dimensional variational problem of min-max type. In the absence of magnetic fields the order paramet ...

SPRINGER2021

Fitting summary statistics of neural data with a differentiable spiking network simulator

Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Shuqi Wang

Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose parameters maximize the likelihood of the recorded activity. Although this is w ...

2021

Defect segmentation for multi-illumination quality control systems

Pascal Fua, Engin Türetken, Siavash Arjomand Bigdeli, David Honzátko

Thanks to recent advancements in image processing and deep learning techniques, visual surface inspection in production lines has become an automated process as long as all the defects are visible in a single or a few images. However, it is often necessary ...

2021

Robustness via Cross-Domain Ensembles

Shuqing Teresa Yeo, Amir Roshan Zamir, Oguzhan Fatih Kar

We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set of cues (called 'middle domains') and ensembling them into one strong predi ...

IEEE2021

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

Wulfram Gerstner, Bernd Albert Illing, Jean Ventura

Learning in the brain is poorly understood and learning rules that respect biological constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose a learning rule that takes inspiration from neuroscience and recent advances ...

2021

A unified framework for Hamiltonian deep neural networks

Giancarlo Ferrari Trecate, Clara Lucía Galimberti, Liang Xu

Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/explod- ing gradients during weight optimization. To avoid this problem, we propose a class of DNNs stemming from the time discretization of Hamiltonian systems. The t ...

2021

Annealing and Replica-Symmetry in Deep Boltzmann Machines

Emanuele Mingione, Diego Alberici

In this paper we study the properties of the quenched pressure of a multi-layer spin-glass model (a deep Boltzmann Machine in artificial intelligence jargon) whose pairwise interactions are allowed between spins lying in adjacent layers and not inside the ...

SPRINGER2020

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Boltzmann machine

Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction

Self-supervised Segmentation via Background Inpainting

Deep Boltzmann Machines: Rigorous Results at Arbitrary Depth

The Solution of the Deep Boltzmann Machine on the Nishimori Line

Fitting summary statistics of neural data with a differentiable spiking network simulator

Defect segmentation for multi-illumination quality control systems

Robustness via Cross-Domain Ensembles

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

A unified framework for Hamiltonian deep neural networks

Annealing and Replica-Symmetry in Deep Boltzmann Machines

Graph Chatbot

Chattez avec Graph Search

Fitting summary statistics of neural data with a differentiable spiking network simulator

Annealing and Replica-Symmetry in Deep Boltzmann Machines

A unified framework for Hamiltonian deep neural networks

Robustness via Cross-Domain Ensembles

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction

Self-supervised Segmentation via Background Inpainting

The Solution of the Deep Boltzmann Machine on the Nishimori Line

Defect segmentation for multi-illumination quality control systems

Deep Boltzmann Machines: Rigorous Results at Arbitrary Depth