Search Results for author:
Found 9 papers, 1 papers with code
Deep multi-scale architectures for monocular depth estimation
This paper aims at understanding the role of multi-scale information in the estimation of depth from monocular images.
MERCI: A NEW METRIC TO EVALUATE THE CORRELATION BETWEEN PREDICTIVE UNCERTAINTY AND TRUE ERROR
One contribution of this article is to draw attention on existing metrics developed in the forecast community, designed to evaluate both the sharpness and the calibration of predictive uncertainty.
Dense Classification and Implanting for Few-Shot Learning
Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks.
n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error
As deep learning applications are becoming more and more pervasive in robotics, the question of evaluating the reliability of inferences becomes a central question in the robotics community.
Few-Shot Few-Shot Learning and the role of Spatial Attention
This motivates us to study a problem where the representation is obtained from a classifier pre-trained on a large-scale dataset of a different domain, assuming no access to its training process, while the base class data are limited to few examples per class and their role is to adapt the representation to the domain at hand rather than learn from scratch.
Ensuring Dataset Quality for Machine Learning Certification
In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems.
Local Propagation for Few-Shot Learning
The challenge in few-shot learning is that available data is not enough to capture the underlying distribution.
Dataset Definition Standard (DDS)
This document gives a set of recommendations to build and manipulate the datasets used to develop and/or validate machine learning models such as deep neural networks.
White Paper Machine Learning in Certified Systems
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc.
