We publish possible thesis or project topics in our group here. If you are interested in one of the topics please contact the according advisor directly via email.
|GPU Backend for Linear Algebra Lib||Johannes Kulick||Studienarbeit/small project|
|We want our linear algebra library to be ported to the magma library as backend. Magma uses the power of gpu and multi-cpu systems to boost the performance of linear algebra operations. Our system so far uses lapack as backend, which is fairly similar in syntax to magma, but unfortunately magma is not a simple drop-in replacement. So some adjustments must be made to make it work. This studienarbeit would also include writing unit-tests for the new backend (and thus for our linear algebra software as a whole).|
|Physical reasoning||Johannes Kulick||Master thesis|
|In one of our research projects we need situations in a physical simulator, which are stable. E.g. no floating objects, no objects penetrating each other etc.. So far we use hard coded functions to generate these situations, but it would be much more interesting to actually reason about the physics. This could be done by modeling the world as probability distributions about the poses of all objects. Then techniques like rejection sampling could be used, but also true inference algorithms could be used.|
The master thesis would include defining the probability distributions as well as implementing inference and/or sampling algorithms on these distributions.
- No upcoming events
- Current Course Pages
- Machine Learning (SS 18)
- Reinforcement Learning (SS 18)
- Deep Reinforcement Learning (SS 18)
- Practical Course Robotics (SS 18)
2 years ago
Good Deep Learning topic for Graduate Research Thesis
I'm planning to do my graduate thesis on Deep Learning. I know the basics of Machine Learning (from Andrew Ng's Coursera Class) and Deep Learning (Nando de Freitas's Lectures). I've developed "deep" interest in this amazing field.
As a total beginner in this field, what do you guys suggest should I do my master's thesis on? What specific deep neural network architecture should I choose? What should be the task on which I should work on (considering that I'm totally new in this field)?
I still want to be a bit innovative and not exactly do what has already been done in this field. I'm eager to learn new things in this field.
So, please help me choose a good research topic to work on!
P. S. As a deep learning framework, I've chosen TensorFlow. Is it any good for newbies like me? I've quite a lot programming experience in C++, Java and a bit of Python, by the way.
6 points · 2 years ago
Do it on Deep Learning for time series forecasting!!!
2 points · 2 years ago
I think the first step is to just read a lot of the current research that's in arXiv, or coming out of Deepmind or various research labs, and to really ask yourself what kind of AI you'd love to see manifested in real life. Better vision systems? Better NLP systems? Better learning systems? Once you've narrowed that down, and have read a good amount of current literature, I think the next directions will be easy.
1 point · 2 years ago
I think you have to find out for yourself. "Interesting topics" are highly subjective. Read lots and lots of papers until you get stuck at a specific idea.
Deep Learning (DL) is another way of representing data. It is a family of Machine Learning (ML). Some researchers consider DL as an advance form of ML. The other names of DL are deep structure learning and hierarchical learning. Moreover, It has enough potential to keep us busy for a long while.
If you are looking for some good research area in DL for your master’s or PhD thesis, then please have a quick review of Ian Goodfellow, Yoshua Bengio, and Aaron Courville books available for this task. There is a complete section in it for research.
Google, Amazon, Netflix and many other big companies on the internet using DL to increase their sale rate. Such as from Google point of view, what are you looking for, what you have queried and the information available on the page is accurate or not are some areas where they optimise their results. Google has recently introduced Rank Brain which works according to it. On the other end, which movie you are watching and recommend a movie according to your taste are some areas where deep learning provides better results than the methodology adopted previously.
DL algorithms have various applications where human experts don’t produce efficient results. Other areas of DL are
- Computer Vision
- Speech Recognition
- Natural Language Processing (NLP)
- Social Network Analysis
- Recommender System
- Customer Relationship Management
- Machine Translation
Dl and Machine Learning (ML) now hot topics for Master’s student. There are many ideas related to machine learning thesis and DL.
One of the hot topics on DL is Natural Language processing. Because Overfitting and underfitting are hot topics of research. Regularization has tried to overcome such issues, but you can extend this work using deep learning approaches.
Secondly, Extreme Learning Machines (ELM) has a lot of space for researchers. ELM is a feedforward neural network which has been used for classification, regressions, clustering of data and features learning. This is a good strategy for producing generalise features performance. It is also much faster than a network which is trained through back propagation.
Reducing the size of a large neural network to something much smaller and manageable is another problem for new researchers. In case of NLP, the only text has been considered for computing results, why not to produce such a model which can handle text, audio, video and images at the same time to produce probabilistic results.
Another research area includes for the optimisations of algorithms that are more Bayesian (rather than based on a single point estimate of the best parameters), that use more non-differentiable operations.
Automatic Machine Translation, for example, conversion of one language text into another language. Simple automatic translation of the text. Google has recently introduced such a technology using DL concepts. A device reads the text from your images and help out to save time. For example, if you are adding your credit card information for payment, just place your card in hand. Your credit card number will be read by a scanner and will be placed in the required field.
Generation of new handwriting and signature, The writing is provided as a sequence of coordinates used by a pen when the handwriting samples were created. From this corpus, the relationship between the pen movement and the letters is learned, and new examples can be generated ad hoc.
The automatic caption of images, for example, a man holding a pen & paper, then train such a model which gives caption. “That man is going to write a something on paper”. He is not going put pen in pocket and making an aeroplane of paper to fly over his head. Similarly, automatic conversion of the sketch into image or painting is another hot topic.
What is the difference between Deep Learning and Machine Learning:
ML is type of Artificial Intelligence, while Deep learning is sub field of machine learning. It is concerned with the algorithms related to human brains functionality and their structure. It is a very significant neural network which needs much more data and computational power to extract results. This is a field is related to provide such artificial networks where human thoughts involve. DL works more narrowly on ML techniques and its algorithms to provide more optimise results of real world problems.
Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. Automatically learning features at multiple levels of abstraction allow a system to learn complex functions mapping the input to the output directly from data, without depending entirely on human-crafted features.
What are the limitations of DL:
- The biggest problem with DL is to have huge amount of data for better results
- Deep networks require massive computational power and resources
- Add reasoning capabilities to learning ones
- Time consuming
- Representation and Generalization
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