All You Need
In One Single
Theme.
Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat
Search here:

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 teamThose who will join us will meet a small family where everyone helps each other, learn and enjoys many informal moments with colleaguesFor 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.

Already affiliated with the following Universities

Newest Thesis Proposal

Thesis Still Available

Introduction

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

With the IBM Watson™ Conversation Service, you can create a solution that can understand native language inputs and use auto-learning to respond in a way that simulates a conversation between humans.

Who we’re looking for

Students that are about to get their master or bachelor degree in Computer Science.

Skills: SaaS, REST APIs, Python, Java, preferred development experience on ChatBots,  social media applications (preferably developed with BotKit)

Duration of this Projects: 6-8 months.

How to contact us

Directly by email to: [email protected]

By LinkedIn: linkedin.com/in/cannavò-sonia-66a95467

Planned Activities

The purpose of this thesis is to make a working prototype of a system (ChatBot) that is able to conduct a preliminary interview with a generic candidate.

The developed ChatBot must be able to request the candidate to upload data and documents, parse the uploaded documents and to normalise the data.

The main tools used will be IBM Bluemix Conversation and IBM Bluemix Natural Language Understanding engine.

The system will integrate external services (like topcoder, CodeForces and/or InterviewBit) to engage the candidates on technical tests.

The system will provide candidates with ongoing updates, feedback, and guidance throughout the process and will answer their questions in real-time.

Introduction

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.

The difficulties in making this comparison arise from the fact that different algorithms are based on different Feature Extractors (VGGs, Residual Networks, etc.), different base resolution and different implementation on specific platforms.

For this study Addfor S.p.A. will provide advanced technical support and computing platforms available at the moment:

Training Platforms:  TITAN Xp, GTX1080 Ti and IBM PowerAI NVIDIA P100 / V100 servers.

Inference Platforms: NVIDIA Drive PX2 and Intel Movidius.

Planned Activities

The goal of this thesis is to define objective methods for measuring the performance of image and video understanding algorithms in real datasets. The first will be a research work on state-of-the-art for Object Detection and Image Segmentation.

The second part of this dissertation project will be devoted to the study of existing metrics for the evaluation and comparison of these algorithms in terms of accuracy and time of calculation.

Addfor will provide data sets of real-time image sequences obtained in different weather conditions and in non-optimal conditions (flickering and jittering) that represent the common difficulties found on real computer vision tasks.

The final purpose of the thesis will be to define a workflow for testing these new algorithms and evaluating them on similar real datasets.

Who we’re looking for

Students that are about to get their master degree in: Computer Science, Mathematics

Skills: Python, C/C++, math, exp. w. at least one among  TensorFlow / PyTorch / BigDL

Duration of this Projects: 6-8 months

How to contact us

Directly by email to: [email protected]

By LinkedIn: linkedin.com/in/cannavò-sonia-66a95467

Check these Links before moving on

https://arxiv.org/abs/1611.10012

Introduction

NVIDIA TensorRT 3™ is an Inference Optimizer and runtime that allows low latency and high throughput for Deep-Learning applications. TensorRT can be used to optimize, validate and deploy neural networks both on hyperscale data centers as well as for embedded or automotive platforms.

For this study Addfor S.p.A. will provide advanced technical support and computing platforms available at the moment:

Training Platforms:  TITAN Xp, GTX1080 Ti and IBM PowerAI NVIDIA P100 / V100 servers.

Inference Platforms: NVIDIA Pegasus and Intel Movidius.

Check these Links before moving on

https://developer.nvidia.com/tensorrt

https://www.tensorflow.org/performance/quantization

https://github.com/tensorflow/models/tree/master/research/object_detection 

How to contact us

Directly by email to: [email protected]

By LinkedIn: linkedin.com/in/cannavò-sonia-66a95467

Planned Activities

When Optimizing a Deep Learning model for inference there are some “tricks” to apply to make the model smaller and faster: lowering the floating-point precision from Float32 to Float16, or Int8, doing a “pruning” of the model by removing the less important features. Some layers (convolution, bias, and ReLu operations) can be merged together.

This work can be done manually, but today there are tools like NVIDIA TensorRT 3™ that can automate most of the job. Not every frameworks is supported, however: this means that not all deep learning models can be directly optimized by TensorRT.

The goal of this thesis will be first and foremost to elaborate a workflow for the optimization of the Object Detection and Segmentation models (for specific Embedded processors such as Pascal, Volta and Xavier GPU). In the second phase of the work a subset of models will be optimized and speed and precision loss will be benchmarked.

Who we’re looking for

Students that are about to get their master degree in: Computer Science, Mathematics, Physics

Skills: Python, C/C++, CUDA C, Numerical Quantization Methods, Finite Precision Math, preferred knowledge of  TensorFlow / TensorRT 3 / NVIDIA DIGITS

Duration of this Projects: 6-8 months

Introduction

ResNet is a Deep Learning Network considered a reference for Image Classification. It is used in cases where a high classification accuracy is required. The innovative idea behind this network is the “Residual Skip Connections” that links the output of the current layer with that of a previous layer.

Temporal-ResNet (T-ResNet) is a state-of-the-art algorithm for Video Scene Classification: starting from the ResNet structure it adds Residual Connections between video frames. In this way it adds to the so-called “Spatial Awareness”, also a “Temporal Awareness”.

Check these Links before moving on

Planned Activities

The goal of this thesis is to reimplement the original T-ResNet algorithm written in MATLAB, in TensorFlow and to apply it to specific datasets provided by Addfor S.p.A. and measure its performance.

It is required that the candidate has significant mathematical foundations, analytical skills and excellent programming skills, especially in Python.

The candidate will be supported by highly qualified personnel and, when necessary, he will be enabled to access the company computing resources

(IBM PowerAI equipped with NVIDIA P100 and V100 cards).

Who we’re looking for

Students that are about to get their master degree in: Computer Science, Mathematics, Physics

Skills: Python, C/C++, math, exp. w. at least one among  TensorFlow / PyTorch / BigDL, excellent skills in mathematical analysis, ability to analyse and research scientific publications

Duration of this Projects: 6-8 months

How to contact us

Directly by email to: [email protected]

By LinkedIn: linkedin.com/in/cannavò-sonia-66a95467

Introduction

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

An important implementation of the theories proposed in the literature will be the extension of the methodology to multi-camera systems.

Check these Links before moving on

Planned Activities

The initial part of this work will consist of a research in literature on the state of the art for multiple object tracking.

The second part of this thesis project will be dedicated to the study of the existing metrics for the evaluation and comparison of these algorithms in terms of accuracy and calculation times.

The final aim will be the definition of a workflow for the testing of these new algorithms and the evaluation of them on similar real datasets.

It is required that the candidate has significant mathematical foundations, analytical skills and excellent programming skills, especially in Python.

The candidate will be supported by highly qualified personnel and when necessary will be able to access the company computing resources (IBM PowerAI equipped with NVIDIA P100 and V100 cards).

Who we’re looking for

Students that are about to get their master degree in: Computer Science, Mathematics

Skills: Python, C/C++, math, preferred knowledge of  TensorFlow / PyTorch / BigDL

Duration of this Projects: 6-8 months

How to contact us

Directly by email to: [email protected]

By LinkedIn: linkedin.com/in/cannavò-sonia-66a95467

Thesis Completed – No more Available

Introduction

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

This publication compares the new algorithm with the latest CNNs. The authors hypothesize that the human brain has modules called “capsules”. These capsules are particularly suitable for handling different types of visual stimuli and coding things like pose (position, size, orientation), deformation, speed, etc. It is assumed that the brain has a mechanism for “routing” low-level visual information to the “capsule” best suited to managing that data.

The new architecture proposed by Hinton seems to allow CapsNet networks to learn internal representations (such as posing of an object) to better generalize the class for 3D objects.

Check these Links before moving on

Planned Activities

The beginning of this dissertation will consist of a research work in literature on both the convex networks and the new theories of Capsule Networks. Work will continue with code implementation and objective measurement of network performance and comparison with the results obtained from the latest CNN implementations.

The company’s interest is to identify the benefits and potential CapsNet practical applications for artificial vision. This includes the use of CapsNet for image segmentation and the reduction of the number of instances needed during the training phase.

It requires that the candidate has considerable mathematical backgrounds, analytical skills and good programming skills especially in Python.

The candidate will be supported by highly qualified staff and will be able to access business computing resources (IBM PowerAI equipped with NVIDIA P100 and V100 cards) when needed.

Who we’re looking for

Students that are about to get their master degree in: Computer Science, Mathematics

Skills: Python, C/C++, math, preferred knowledge of  TensorFlow / PyTorch / BigDL

Duration of this Projects: 6-8 months

How to contact us

Directly by email to: [email protected]

By LinkedIn: linkedin.com/in/cannavò-sonia-66a95467

Introduction

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.

The availability of a suitable dataset is fundamental for the training and validation of Deep Neural Networks.

How to contact us

Directly by email to: [email protected]

By LinkedIn: linkedin.com/in/cannavò-sonia-66a95467

Planned Activities

The aim of this thesis is to use the same approach adopted by the publication in question, focusing on the improvement of a real-image dataset “augmented” by synthetic objects.  We would like to find an answer to the following questions:

  • Using the increased dataset, does the accuracy of the system increase for all classes of objects, only for some of them, or does it not increase at all?
  • Is there a limit number of synthetic objects beyond which the accuracy of the training does not increase anymore?
  • Considering a set of Object Detection algorithms, what effect does the use of the increased dataset have on them? Are there any algorithms that are more influenced than others by this technique?

It is required that the candidate has significant mathematical foundations, analytical skills and excellent programming skills, especially in Python.

The candidate will be supported by highly qualified personnel and when necessary will be able to access the company’s computing resources (IBM PowerAI equipped with NVIDIA P100 and V100 cards).

Who we’re looking for

Students that are about to get their master degree in: Computer Science, Mathematics, Physics

Skills: Python, C/C++, math, exp. w. at least one among  TensorFlow / PyTorch / BigDL, excellent skills in mathematical analysis, ability to analyse and research scientific publications

Duration of this Projects: 6-8 months

Check these Links before moving on

 Lasted Thesis in Addfor