Search Results for author:
Found 22 papers, 8 papers with code
Padding Aware Neurons
In this work we identify Padding Aware Neurons (PANs), a type of filter that is found in most (if not all) convolutional models trained with static padding.
Exploring the Role of Explainability in AI-Assisted Embryo Selection
In Vitro Fertilization is among the most widespread treatments for infertility.
When & How to Transfer with Transfer Learning
In deep learning, transfer learning (TL) has become the de facto approach when dealing with image related tasks.
Healthy Twitter discussions? Time will tell
Studying misinformation and how to deal with unhealthy behaviours within online discussions has recently become an important field of research within social studies.
Focus! Rating XAI Methods and Finding Biases
In the field of image recognition many feature attribution methods have been proposed with the purpose of explaining a model's behavior using visual cues.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties
In this work we introduce the MAMe dataset, an image classification dataset with remarkable high resolution and variable shape properties.
Ranked #1 on
Image Classification
on MAMe
DOME: Recommendations for supervised machine learning validation in biology
Modern biology frequently relies on machine learning to provide predictions and improve decision processes.
Global Data Science Project for COVID-19
At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March.
The Impact of COVID-19 on Flight Networks
As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections.
What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
The outbreak of coronavirus disease 2019 (COVID-19) recently has affected human life to a great extent.
Obstruction level detection of sewer videos using convolutional neural networks
In this work, we design a methodology to train a Convolutional Neural Network for identifying the level of obstruction in pipes, thus reducing the human effort required on such a frequent and repetitive task.
Random Forest as a Tumour Genetic Marker Extractor
Finding tumour genetic markers is essential to biomedicine due to their relevance for cancer detection and therapy development.
MetH: A family of high-resolution and variable-shape image challenges
In sight of the increasing importance of problems that can benefit from exploiting high-resolution (HR) and variable-shape, and with the goal of promoting research in that direction, we introduce a new family of datasets (MetH).
Feature discriminativity estimation in CNNs for transfer learning
The purpose of feature extraction on convolutional neural networks is to reuse deep representations learnt for a pre-trained model to solve a new, potentially unrelated problem.
Adaptive Pattern Matching with Reinforcement Learning for Dynamic Graphs
We propose an incremental graph pattern matching algorithm to deal with time-evolving graph data and also propose an adaptive optimization system based on reinforcement learning to recompute vertices in the incremental process more efficiently.
Databases
A Visual Distance for WordNet
WordNet, which includes a wide variety of concepts associated with words (i. e., synsets), is often used as a source for computing those distances.
Building Graph Representations of Deep Vector Embeddings
Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes.
Full-Network Embedding in a Multimodal Embedding Pipeline
In this paper we evaluate the impact of using the Full-Network embedding in this setting, replacing the original image representation in a competitive multimodal embedding generation scheme.
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks
Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option.
Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm
We introduce a community detection algorithm (Fluid Communities) based on the idea of fluids interacting in an environment, expanding and contracting as a result of that interaction.
Data Structures and Algorithms Social and Information Networks Physics and Society
On the Behavior of Convolutional Nets for Feature Extraction
We seek to provide new insights into the behavior of CNN features, particularly the ones from convolutional layers, as this can be relevant for their application to knowledge representation and reasoning.
Limitations and Alternatives for the Evaluation of Large-scale Link Prediction
Link prediction, the problem of identifying missing links among a set of inter-related data entities, is a popular field of research due to its application to graph-like domains.
