Publications related to Categorical distribution

From trees to barcodes and back again II: Combinatorial and probabilistic aspects of a topological inverse problem

Kathryn Hess Bellwald, Lida Kanari, Adélie Eliane Garin

In this paper we consider two aspects of the inverse problem of how to construct merge trees realizing a given barcode. Much of our investigation exploits a recently discovered connection between the symmetric group and barcodes in general position, based ...

2024

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Mathieu Salzmann, Alexandre Massoud Alahi, Megh Hiren Shukla

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

2024

VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

Alexandre Massoud Alahi, Megh Hiren Shukla

Advances in computing have enabled widespread access to pose estimation, creating new sources of data streams. Unlike mock set-ups for data collection, tapping into these data streams through on-device active learning allows us to directly sample from the ...

BMVA Press2022

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses

Frédéric Courbin, Aymeric Alexandre Galan, Austin Chandler Peel, Georgios Vernardos

Modeling the mass distribution of galaxy-scale strong gravitational lenses is a task of increasing difficulty. The high-resolution and depth of imaging data now available render simple analytical forms ineffective at capturing lens structures spanning a la ...

EDP SCIENCES S A2022

Bayesian Inversion for Nonlinear Imaging Models Using Deep Generative Priors

Michaël Unser, Thanh-An Michel Pham, Pakshal Narendra Bohra, Jonathan Yuelin Dong

Most modern imaging systems incorporate a computational pipeline to infer the image of interest from acquired measurements. The Bayesian approach to solve such ill-posed inverse problems involves the characterization of the posterior distribution of the im ...

2022

Revisiting the General Identifiability Problem

Negar Kiyavash, Ehsan Mokhtarian, Yaroslav Kivva, Seyed Jalal Etesami

We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of observational distribution to the original definition of the problem. We show t ...

2022

Network Classifiers Based On Social Learning

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to ...

IEEE2021

Deep Domain Adaptation in Earth Observation

Devis Tuia, Benjamin Alexander Kellenberger, Nicolas Courty

When applied to new datasets, acquired at different time moments, with different sensors or under different acquisition conditions, deep learning models might fail spectacularly. This is because they have learned from the data distribution observed during ...

Wiley2021

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Categorical distribution

Extensions of Peer Prediction Incentive Mechanisms

From trees to barcodes and back again II: Combinatorial and probabilistic aspects of a topological inverse problem

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses

Bayesian Inversion for Nonlinear Imaging Models Using Deep Generative Priors

Revisiting the General Identifiability Problem

Network Classifiers Based On Social Learning

Deep Domain Adaptation in Earth Observation

Graph Chatbot

Chat with Graph Search

From trees to barcodes and back again II: Combinatorial and probabilistic aspects of a topological inverse problem

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

Network Classifiers Based On Social Learning

VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

Bayesian Inversion for Nonlinear Imaging Models Using Deep Generative Priors

Revisiting the General Identifiability Problem

Deep Domain Adaptation in Earth Observation