Search Results for author:
Found 7 papers, 6 papers with code
Optimizing Mario Adventures in a Constrained Environment
This project proposes and compares a new way to optimise Super Mario Bros. (SMB) environment where the control is in hand of two approaches, namely, Genetic Algorithm (MarioGA) and NeuroEvolution (MarioNE).
Adversarial Attack On Yolov5 For Traffic And Road Sign Detection
This paper implements and investigates popular adversarial attacks on the YOLOv5 Object Detection algorithm.
DeepSeaNet: Improving Underwater Object Detection using EfficientDet
The aim of this research project is to study the efficiency of newer models on the same dataset and contrast them with the previous results based on accuracy and inference time.
Capturing Emerging Complexity in Lenia
The platform is important as a tool for studying artificial life and evolution, as it provides a scalable and flexible environment for creating a diverse range of organisms with varying abilities and behaviors.
RAMario: Experimental Approach to Reptile Algorithm -- Reinforcement Learning for Mario
This research paper presents an experimental approach to using the Reptile algorithm for reinforcement learning to train a neural network to play Super Mario Bros. We implement the Reptile algorithm using the Super Mario Bros Gym library and TensorFlow in Python, creating a neural network model with a single convolutional layer, a flatten layer, and a dense layer.
CQural: A Novel CNN based Hybrid Architecture for Quantum Continual Machine Learning
In this research paper, we show that it is not only possible to circumvent catastrophic forgetting in continual learning with novel hybrid classical-quantum neural networks, but also explains what features are most important to learn for classification.
ADDSL: Hand Gesture Detection and Sign Language Recognition on Annotated Danish Sign Language
Using this dataset, a one-stage ob-ject detector model (YOLOv5) was trained with the CSP-DarkNet53 backbone and YOLOv3 head to recognize letters (A-Z) and numbers (0-9) using only seven unique images per class (without augmen-tation).
