Search Results for author:
Found 16 papers, 6 papers with code
Finding Regions of Interest in Whole Slide Images Using Multiple Instance Learning
Whole Slide Images (WSI), obtained by high-resolution digital scanning of microscope slides at multiple scales, are the cornerstone of modern Digital Pathology.
DE-COP: Detecting Copyrighted Content in Language Models Training Data
We are motivated by the premise that a language model is likely to identify verbatim excerpts from its training text.
DeepThought: An Architecture for Autonomous Self-motivated Systems
We argue that the internal architecture of LLMs and their finite and volatile state cannot support any of these properties.
Matching the Neuronal Representations of V1 is Necessary to Improve Robustness in CNNs with V1-like Front-ends
Here, we further explore this result and show that the neuronal representations that emerge from precisely matching the distribution of RF properties found in primate V1 is key for this improvement in robustness.
Improving Address Matching using Siamese Transformer Networks
This research introduces a deep learning-based model designed to increase the efficiency of address matching for Portuguese addresses.
Connecting metrics for shape-texture knowledge in computer vision
Finally, we observed that while in general there is a correlation between performance and shape bias, there are significant variations between architecture families.
Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement Learning
With this work, we hope to provide some insights into the representations learned by ViT during a self-supervised pretraining with observations from RL environments and which properties arise in the representations that lead to the best-performing agents.
Assessing Policy, Loss and Planning Combinations in Reinforcement Learning using a New Modular Architecture
In this work, we propose a new modular software architecture suited for these types of agents, and a set of building blocks that can be easily reused and assembled to construct new model-based reinforcement learning agents.
Model-based Reinforcement Learning
reinforcement-learning
+1
Assessing the Impact of Attention and Self-Attention Mechanisms on the Classification of Skin Lesions
In this work, we study and perform an objective comparison of a number of different attention mechanisms in a specific computer vision task, the classification of samples in the widely used Skin Cancer MNIST dataset.
Modelling neuronal behaviour with time series regression: Recurrent Neural Networks on synthetic C. elegans data
We show how the nervous system of C. elegans can be modelled and simulated with data-driven models using different neural network architectures.
Using Soft Labels to Model Uncertainty in Medical Image Segmentation
Inspired by this, we propose a simple method to obtain soft labels from the annotations of multiple physicians and train models that, for each image, produce a single well-calibrated output that can be thresholded at multiple confidence levels, according to each application's precision-recall requirements.
Modelling Neuronal Behaviour with Time Series Regression: Recurrent Neural Networks on C. Elegans Data
In this paper we show how the nervous system of C. Elegans can be modelled and simulated with data-driven models using different neural network architectures.
Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation
We adopt a better-suited clinical criterion and segment vessels according to their clinical relevance.
Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks
Europe was hit hard by the COVID-19 pandemic and Portugal was one of the most affected countries, having suffered three waves in the first twelve months.
Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks
Coronary artery disease leading up to stenosis, the partial or total blocking of coronary arteries, is a severe condition that affects millions of patients each year.
Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation
In this paper, we test whether this algorithm, which was shown to improve semantic segmentation for 2D RGB images, is able to improve segmentation quality for 3D multi-modal medical images.
