4 citations · 13 across the 6 of their papers we have counts for
6 papers
Multimodal Representations for Teacher-Guided Compositional Visual Reasoning
Wafa Aissa, Marin Ferecatu, Michel Crucianu
Neural Module Networks (NMN) are a compelling method for visual question answering, enabling the translation of a question into a program consisting of a series of reasoning sub-ta…
Curriculum Learning for Compositional Visual Reasoning
Wafa Aissa, Marin Ferecatu, Michel Crucianu
Visual Question Answering (VQA) is a complex task requiring large datasets and expensive training. Neural Module Networks (NMN) first translate the question to a reasoning path, th…
Why is the prediction wrong? Towards underfitting case explanation via meta-classification
Sheng Zhou, Pierre Blanchart, Michel Crucianu +1
In this paper we present a heuristic method to provide individual explanations for those elements in a dataset (data points) which are wrongly predicted by a given classifier. Sinc…
Efficient Autoprecoder-based deep learning for massive MU-MIMO Downlink under PA Non-Linearities
Xinying Cheng, Rafik Zayani, Marin Ferecatu +1
This paper introduces a new efficient autoprecoder (AP) based deep learning approach for massive multiple-input multiple-output (mMIMO) downlink systems in which the base station i…
Global Vertices and the Noising Paradox
Konstantinos A. Raftopoulos, Stefanos D. Kollias, Marin Ferecatu
A theoretical and experimental analysis related to the identification of vertices of unknown shapes is presented. Shapes are seen as real functions of their closed boundary. Unlike…
Incremental Noising and its Fractal Behavior
Konstantinos A. Raftopoulos, Marin Ferecatu, Dionyssios D. Sourlas +1
This manuscript is about further elucidating the concept of noising. The concept of noising first appeared in \cite{CVPR14}, in the context of curvature estimation and vertex local…