What are some good topics on machine learning and neural.

My thesis is focused on learning video segmentation from limited labelled data. I did an internship in Nvidia Deep Learning for Autonomous vehicles research, where I was working on a human-in-the-loop free-space annotation tool using a multi-resolution column-wise regression with a convolutional LSTM. I was a member in a team of 4 in the KUKA Innovation Challenge 2018. Our team was one of the.

Uncertainty in Deep Learning (PhD Thesis) October 13th, 2016 (Updated: June 4th, 2017) Tweet. Share. Function draws from a dropout neural network. This new visualisation technique depicts the distribution over functions rather than the predictive distribution (see demo below). So I finally submitted my PhD thesis (given below). In it I organised the already published results on how to obtain.

What could dissertation topic related to deep learning?

The new model family introduced in this thesis is summarized under the term Recursive Deep Learning. The models in this family are variations and extensions of unsupervised and supervised recursive neural networks (RNNs) which generalize deep and feature learning ideas to hierarchical structures. The RNN models of this thesis.Master’s Thesis Deep Learning for Visual Recognition Remi Cadene Supervised by Nicolas Thome and Matthieu Cord arXiv:1610.05567v1 (cs.CV) 18 Oct 2016 Wednesday 7th September, 2016.This example shows how to classify each time step of sequence data using a long short-term memory (LSTM) network. To train a deep neural network to classify each time step of sequence data, you can use a sequence-to-sequence LSTM network.A sequence-to-sequence LSTM network enables you to make different predictions for each individual time step of the sequence data.


Deep Q-Networks, based around Deep Learning and Reinforcement Learning, have been demonstrated to be able to play Go at the level of a master and to play Atari 2600 games without human intervention. The aim of this research is to develop an extension to Deep Q-Networks by acquiring a model of an environment using features extracted by the network. At each moment in time, the neural network.Being a PhD student in the machine learning group of the University of Toronto was lots of fun, and joining it was one of the best decisions that I have ever made. I want to thank my adviser, Geoff Hinton. Geoff taught me how to really do research and our meetings were the highlight of my week. He is an excellent mentor who gave me the freedom and the encouragement to pursue my own ideas and.

That's ridiculous. Work on the k-means algorithm has appeared at the last two ICMLs ()(), and there was a paper on the perceptron at NIPS 2010.If those topics can still be published on, certainly there's more left in Deep Learning. DL has mostly only been applied to dense, raw signals (images and speech); there are many, many more applications left to tackle.

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Ph.D. Thesis - Scalable Human Identification with Deep Learning human-identification person-reidentification person-search deep-learning thesis-template computer-vision 42 commits.

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In this thesis, to explore more advantages of RGB-D data, we use some popular RGB-D datasets for deep feature learning algorithms evaluation, hyper-parameter optimization, local multi-modal feature learning, RGB-D data fusion and recognizing RGB information from RGB-D images: i)With the success of Deep Neural Network in computer vision, deep features from fused RGB-D data can be proved to gain.

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This thesis is intended to broaden the usage of machine learning in quantitative finance and consists of the three chapters. Chapter 1 aims to perform multi-input and multi-output (MIMO) nonlinear regression, applicable to multi-step-ahead financial forecasting (e.g. Ticlavilca et al. (2010) and Bao et al. (2014)), in short computation time. Both Chapter 2 and Chapter 3 aim to maximise.

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PhD thesis, University of T. In this paper, we investigate a variety of deep learning models such as Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM and Autoencoder.

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Emotion Classification Using Advanced Machine Learning Techniques Applied to Wearable Physiological Signals Data Bahareh Nakisa Master of Science in Computer Science and Information Technology A thesis by Publication submitted in fulfilment of the required for the degree of Doctor of Philosophy (Ph.D) School of Electrical Engineering and Computer Science Science and Engineering Faculty.

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The most frequently deep learning methods are Deep neural network (DNN), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). In this paper, we design a novel plug and play device to detect various attacks based on deep learning model in ad-hoc networks. This plug and play device combines with function of data crawling, data processing and data detection. Firstly, this novel.

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Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT'14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM.

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Kekempanos, L (2019) Parallelised Bayesian Optimisation for Deep Learning. PhD thesis, University of Liverpool. Mckenzie, J (2019) Longitudinal Beam Characterisation on VELA using a Transverse Deflecting Cavity. PhD thesis, University of Liverpool. Stafford, CN (2019) 'The worst of drunkards': female drunkenness in mid-Victorian Lancashire. PhD thesis, University of Liverpool. Almaleki, FH.

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In this thesis, several deep learning architectures are compared to traditional techniques for the classification of visually evoked EEG signals. We found that deep learning architectures using long short-term memory units (LSTMs) outperform traditional methods, while small convolutional architectures performed comparably to traditional methods. We also explored the use of transfer learning by.

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