Search Results for author:
Found 5 papers, 1 papers with code
Knowledge Distillation for Feature Extraction in Underwater VSLAM
This paper proposes a cross-modal knowledge distillation framework for training an underwater feature detection and matching network (UFEN).
Analysis of functional neural codes of deep learning models: Functional Telescope Hypothesis
Deep neural networks (DNNs), the agents of deep learning (DL), require a massive number of parallel/sequential operations.
Library network, a possible path to explainable neural networks
Deep neural networks (DNNs) may outperform human brains in complex tasks, but the lack of transparency in their decision-making processes makes us question whether we could fully trust DNNs with high stakes problems.
Prediction of a Movie's Success From Plot Summaries Using Deep Learning Models
As the size of investment for movie production grows bigger, the need for predicting a movie{'}s success in early stages has increased.
Complementary reinforcement learning towards explainable agents
Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples.
