1 code implementation • 30 Apr 2023 • Leonardo de Lellis Rossi, Leticia Mara Berto, Eric Rohmer, Paula Paro Costa, Ricardo Ribeiro Gudwin, Esther Luna Colombini, Alexandre da Silva Simoes
The increasing agent's cognitive complexity is managed by adding new terms to the reward function for each learning phase.
1 code implementation • 2 Mar 2022 • Rafael Figueiredo Prudencio, Marcos R. O. A. Maximo, Esther Luna Colombini
With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations, sustaining conversations with humans, and controlling robotic agents.
1 code implementation • WNUT (ACL) 2021 • Gabriel Oliveira dos Santos, Esther Luna Colombini, Sandra Avila
This paper shows that CIDEr-D, a traditional evaluation metric for image description, does not work properly on datasets where the number of words in the sentence is significantly greater than those in the MS COCO Captions dataset.
no code implementations • 31 Mar 2021 • Alana de Santana Correia, Esther Luna Colombini
For the last six years, this property has been widely explored in deep neural networks.
2 code implementations • 21 Mar 2021 • Gabriel Oliveira dos Santos, Esther Luna Colombini, Sandra Avila
Thus, inspired by this movement, we have proposed the #PraCegoVer, a multi-modal dataset with Portuguese captions based on posts from Instagram.
1 code implementation • 5 Oct 2020 • Gabriel Moraes Barros, Esther Luna Colombini
However, recently, model-free reinforcement learning has been successfully used for controlling drones without any prior knowledge of the robot model.
2 code implementations • 3 May 2020 • Rafael Anicet Zanini, Esther Luna Colombini
This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease (PD) electromyography (EMG) signals.
1 code implementation • 6 Oct 2019 • Rafael Anicet Zanini, Esther Luna Colombini, Maria Claudia Ferrari de Castro
This paper proposes a comparison between different neural network models, using multilayer perceptron (MLPs) and recurrent neural network (RNN) models, for predicting Parkinson's disease electromyography (EMG) signals, to anticipate resulting resting tremor patterns.