Federated Learning for Ranking Browser History Suggestions. (arXiv:1911.11807v1 [cs.LG])

Federated Learning is a new subfield of machine learning that allows fitting models without collecting the training data itself. Instead of sharing data, users collaboratively train a model by only sending weight updates to a server. To improve the ranking of suggestions in the Firefox URL bar, we make use of Federated Learning to train…

Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines. (arXiv:1911.11819v1 [q-fin.TR])

Few assets in financial history have been as notoriously volatile as cryptocurrencies. While the long term outlook for this asset class remains unclear, we are successful in making short term price predictions for several major crypto assets. Using historical data from July 2015 to November 2019, we develop a large number of technical indicators to…

Neural Percussive Synthesis Parameterised by High-Level Timbral Features. (arXiv:1911.11853v1 [eess.AS])

We present a deep neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds. This approach allows for intuitive control of a synthesizer, enabling the user to shape sounds without extensive knowledge of signal processing. We use a feedforward convolutional neural network-based architecture, which is able to map input…

Asymmetric Correntropy for Robust Adaptive Filtering. (arXiv:1911.11855v1 [eess.SP])

In recent years, correntropy has been seccessfully applied to robust adaptive filtering to eliminate adverse effects of impulsive noises or outliers. Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables. This definition is reasonable when the error between the two random variables is symmetrically distributed around zero. For the…

Approximating the Permanent by Sampling from Adaptive Partitions. (arXiv:1911.11856v1 [cs.LG])

Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics. However, this problem is also #P-complete, which leaves little hope for finding an exact solution that can be computed efficiently. While the problem admits a fully polynomial randomized approximation scheme, this method has seen…

Potential of deep features for opinion-unaware, distortion-unaware, no-reference image quality assessment. (arXiv:1911.11903v1 [eess.IV])

Image Quality Assessment algorithms predict a quality score for a pristine or distorted input image, such that it correlates with human opinion. Traditional methods required a non-distorted “reference” version of the input image to compare with, in order to predict this score. However, recent “No-reference” methods circumvent this requirement by modelling the distribution of clean…

Data Augmentation Using Adversarial Training for Construction-Equipment Classification. (arXiv:1911.11916v1 [eess.IV])

Deep learning-based construction-site image analysis has recently made great progress with regard to accuracy and speed, but it requires a large amount of data. Acquiring sufficient amount of labeled construction-image data is a prerequisite for deep learning-based construction-image recognition and requires considerable time and effort. In this paper, we propose a “data augmentation” scheme based…

Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations. (arXiv:1911.11927v1 [eess.AS])

Suicide is a major societal challenge globally, with a wide range of risk factors, from individual health, psychological and behavioral elements to socio-economic aspects. Military personnel, in particular, are at especially high risk. Crisis resources, while helpful, are often constrained by access to clinical visits or therapist availability, especially when needed in a timely manner.…

AIPNet: Generative Adversarial Pre-training of Accent-invariant Networks for End-to-end Speech Recognition. (arXiv:1911.11935v1 [cs.CL])

As one of the major sources in speech variability, accents have posed a grand challenge to the robustness of speech recognition systems. In this paper, our goal is to build a unified end-to-end speech recognition system that generalizes well across accents. For this purpose, we propose a novel pre-training framework AIPNet based on generative adversarial…

Generalised Linear Models for Dependent Binary Outcomes with Applications to Household Stratified Pandemic Influenza Data. (arXiv:1911.12115v1 [q-bio.PE])

Much traditional statistical modelling assumes that the outcome variables of interest are independent of each other when conditioned on the explanatory variables. This assumption is strongly violated in the case of infectious diseases, particularly in close-contact settings such as households, where each individual’s probability of infection is strongly influenced by whether other household members experience…