Author: Zoltán Lőrincz

Introduction

Throughout the previous semesters, I used Imitation Learning to carry out lane following in the Duckietown¹ environment. The agents were trained in the Duckietown simulator using different Imitation Learning methods such as Behavioral Cloning², Dataset Aggregation³ (DAgger) and Generative Adversarial Imitation Learning⁴ (GAIL). …


Author: Robert Moni

Sorry for the clickbait, but no, we aren’t the engineers that put together the Perseverance Mars rover, although we do possess the right amount of perseverance to achieve our goals.

This blog post attempts to provide a summary of our latest research achievements under the aegis of…


Author: Zsombor Tóth

Part 1 of this blog post can be found here.

Introduction

Deep Learning is one of the most important techniques of autonomous vehicles nowadays. There are many components of a self-driving vehicle that can be realized with the help of deep neural networks (e.g. car and pedestrian detection…


Author: Gábor Lant

One of the key strength of deep learning is that it can work with a large amount of data to train machine learning models. These models can be trained to find the underlying structure of the data. On the other hand, this also means that data is…


Author: Tamás Illés

Introduction

In Machine Learning (ML), we typically care about optimizing for a particular metric, whether this is a score on a certain benchmark or a business Key Performance Indicator (KPI). In order to do this, we generally train a single model or an ensemble of models to perform…


Author: András Béres

Introduction

Recently the topic of self-driving cars has received great attention both from academia and the public. While Deep Learning can provide us tools for processing vast amounts of sensor data, Reinforcement Learning promises us the ability to take the right actions in complex interactive environments. …


Author: Péter Almási

I set the goal to create a method for controlling vehicles to perform autonomous lane following using deep reinforcement learning. The agent is trained in a simulated environment without any real-world data and is tested in the real world. …


Author: Márton Tim

In Deep Reinforcement Learning (DRL), convergence and low performance of the resulting agent is often an issue that just intensifies as the problem becomes increasingly complex and complicated. …


Author: Dániel Unyi

Link prediction is to predict whether two components in a network are likely to interact with each other. It’s a fundamental task in network science, with a wide variety of real-world applications. Examples include predicting friendship links on social media, identifying hidden communities, or discovering drug-drug interactions…


Author: Robert Moni

This year we competed with 6 different solutions at the 5th edition of the AI Driving Olympics (AIDO) which was part of the 34th conference on Neural Information Processing Systems (NeurIPS). …

SmartLab AI

Deep Learning and AI solutions from Budapest University of Technology and Economics. http://smartlab.tmit.bme.hu/

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