Concept

Inductive probability

Related publications (113)

A Structured Dictionary Perspective on Implicit Neural Representations

Pascal Frossard, Guillermo Ortiz Jimenez, Gizem Yüce, Beril Besbinar

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a ...

IEEE COMPUTER SOC2022

Learning Analytics for Adaptive and Self-Improving Learning Environments for Inductive Teaching

Louis Pierre Faucon

The emergence of digital technology is changing education in many ways. A particularly interesting aspect of this transformation is the development of learning environments that can automatically adapt to individual students and can collect data in order t ...

EPFL2021

Distributed Meta-Learning with Networked Agents

Ali H. Sayed, Mert Kayaalp, Stefan Vlaski

Meta-learning aims to improve efficiency of learning new tasks by exploiting the inductive biases obtained from related tasks. Previous works consider centralized or federated architectures that rely on central processors, whereas, in this paper, we propos ...

EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP2021

Does It Pay to Do Novel Science? The Selectivity Patterns in Science Funding

Fabiana Visentin, Charles Chadi Ayoubi, Michele Pezzoni

Public funding agencies aim to fund novel breakthrough research to promote the radical scientific discoveries of tomorrow. Identifying the profiles of scientists being financed to pursue their research is therefore crucial. This paper shows that the fundin ...

OXFORD UNIV PRESS2021

Self-Supervision By Prediction For Object Discovery In Videos

Pascal Frossard, Beril Besbinar

Despite their irresistible success, deep learning algorithms still heavily rely on annotated data, and unsupervised settings pose many challenges, such as finding the right inductive bias in diverse scenarios. In this paper, we propose an object-centric mo ...

IEEE2021

A Plug-and-Play Deep Image Prior

Volkan Cevher, Fabian Ricardo Latorre Gomez, Thomas Sanchez, Zhaodong Sun

Deep image priors (DIP) offer a novel approach for the regularization that leverages the inductive bias of a deep convolutional architecture in inverse problems. However, the quality of DIP approaches often degrades when the number of iterations exceeds a ...

IEEE2021

Shortest-support multi-spline bases for generalized sampling

Michaël Unser, Alexis Marie Frederic Goujon, Shayan Aziznejad, Alireza Naderi

Generalized sampling consists in the recovery of a function f, from the samples of the responses of a collection of linear shift-invariant systems to the input f . The reconstructed function is typically a member of a finitely generated integer-shift invar ...

2021

Regression Networks for Meta-Learning Few-Shot Classification

Matthias Grossglauser, Arnout Jan J Devos

We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class. In high dimensional embedding spaces the directi ...

2020

A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers

Philip Neil Garner

Neural Network (NN) classifiers can assign extreme probabilities to samples that have not appeared during training (out-of-distribution samples) resulting in erroneous and unreliable predictions. One of the causes for this unwanted behaviour lies in the us ...

IEEE2020

Context effects on probability estimation

Wei-Hsiang Lin

Many decisions rely on how we evaluate potential outcomes and estimate their corresponding probabilities of occurrence. Outcome evaluation is subjective because it requires consulting internal preferences and is sensitive to context. In contrast, probabili ...

PUBLIC LIBRARY SCIENCE2020

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