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Found 8 papers, 7 papers with code
Incremental procedural and sensorimotor learning in cognitive humanoid robots
The increasing agent's cognitive complexity is managed by adding new terms to the reward function for each learning phase.
A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
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.
CIDEr-R: Robust Consensus-based Image Description Evaluation
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.
Attention, please! A survey of Neural Attention Models in Deep Learning
For the last six years, this property has been widely explored in deep neural networks.
#PraCegoVer: A Large Dataset for Image Captioning in Portuguese
Thus, inspired by this movement, we have proposed the #PraCegoVer, a multi-modal dataset with Portuguese captions based on posts from Instagram.
Using Soft Actor-Critic for Low-Level UAV Control
However, recently, model-free reinforcement learning has been successfully used for controlling drones without any prior knowledge of the robot model.
Parkinson’s Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer
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.
Parkinson’s Disease EMG Signal Prediction Using Neural Networks
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.
