Thesis Proposal

For us, looking for young talents who are passionate about the latest technologies is essential. We also put a big effort in consolidating the cooperation and the harmony within the team
Those who will join us will meet a small family where everyone helps each other, learn and enjoys many informal moments with colleagues

For us it is important to keep high the motivation of the newcomers through the continuous development of new technologies and their application on real problems. The development of a master thesis is for us an important period to evaluate the capabilities and the talents of the people that we will recruit just after graduation. For the master’s candidates this is a great opportunity to work on state-of-the art technologies, applied to real world problems.

Partners

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Thesis still available

Algorithms for Multiple Object Tracking

[Available]

The aim of this thesis is to define objective methods for measuring the performance of multiple object tracking algorithms in real datasets.

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Scene Classification in Video Streams

[Available]

ResNet is a Deep Learning Network considered a reference for Image Classification. It is used in cases where a high classification accuracy is required. RL in conjunction with Deep Learning has obtained outstanding results in Atari video games, the Go board-game and a more complex environment like StarCraft II.

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Testing Algorithms for image & Video understanding

[Available]

In the last few years, many algorithms with remarkable effectiveness for Object Detection have been published but still some comparative metrics haven’t been defined.

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Build a Chatbot for Interviews on IBM Watson

[Available]

IBM Watson™ is a SaaS engine for the development of Cognitive Computing applications.

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Comparison of Reinforcement Learning Frameworks

[Available]

Reinforcement Learning (RL) is a class of machine learning algorithms in which an agent interacts by trial-and-error in an environment.

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Safe Reinforcement Learning

[Available]

Reinforcement Learning (RL) [1] is a class of machine learning algorithms in which an agent interacts by trial-and-error in an environment.

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Unsupervised/semi-supervised video classification

[Available]

When there are hundreds or thousands of cameras producing video streams all day long it is very useful to have an algorithm that analyses such streams instead of a human.

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Deep Genomics: harnessing the power of Deep Neural Networks in the analysis of biomolecular data

[Available]

The human genome project [1], an international scientific research project with the goal of determining the sequence of nucleotide base pairs that make up human DNA, lasted roughly 15 years and cost $5 billion (adjusted for inflation).

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Algorithm Optimization on Embedded & Server GPU

[Available]

NVIDIA TensorRT5.1™ is an Inference Optimizer and runtime that allows low latency and high throughput for Deep-Learning applications.

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Model Based Reinforcement Learning

[Available]

Reinforcement Learning (RL) [1] is a class of machine learning algorithms in which an agent interacts by trial-and-error in an environment.

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Inverse Reinforcement Learning for Autonomous Driving

[Available]

Reinforcement Learning [1] (RL) is an emerging field of Artificial Intelligence (AI) that is giving extraordinary results in different applications.

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Efficient Extraction of motion vectors from h264/h265 streams

[Available]

A video encoder is a system that is able to transform a raw video (a sequence of uncompressed frames) to a more transferrable and storable format. Conversely a video decoder is a system that transforms that streams back to images. The pair (encoder, decoder) is usually called a video codec.

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Use of h264/h265 motion vectors as object motion estimation

[Available]

AVC and HEVC two of the most used codecs in video industry internally uses intra and extra frame motion vectors in order to achieve a better compression.

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Explanation of Deep Neural Networks Predictions

[Available]

Artificial Neural Networks are biologically-inspired programming paradigm which enables a computer to learn from observational data [1].

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Generative Adversarial Networks for Domain Adaptation between synthetic and real images

[Available]

Learning from as little human supervision as possible is a major challenge in Machine Learning. In the context of computer vision, Deep Learning is a class of supervised learning algorithms that require a great amount of human-labeled images in order to be trained [1].

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Thesis completed – no more available

Synthetic object Dataset Augmentation

[Expired]

A recent publication “On Pre-Trained Image Features and Synthetic Images for Deep Learning“ uses real images as background for synthetically generated objects in order to create a dataset for Deep Learning algorithms.

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Capsule Networks as alternative to DCNN

[Expired]

Geoffrey Hinton released last month a document titled “Dynamic Routing Between Capsules“ and the entire Deep Learning community was shaken by this article.

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Latest Thesis [ITA]

Latest Thesis [ENG]

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