The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization

Non-negative matrix factorization (NMF) has become a popular machine learning approach to many problems in text mining, speech and image processing, bio-informatics and seismic data analysis to name a few. In NMF, a matrix of non-negative data is approximated by the low-rank product of two matrices with non-negative entries. In this paper, the approximation quality is measured by the Kullback-Leibler divergence between the data and its low-rank reconstruction. The existence of the simple multiplicative update (MU) algorithm for computing the matrix factors has contributed to the success of NMF. Despite the availability of algorithms showing faster convergence, MU remains popular due to its simplicity. In this paper, a diagonalized Newton algorithm (DNA) is proposed showing faster convergence while the implementation remains simple and suitable for high-rank problems. The DNA algorithm is applied to various publicly available data sets, showing a substantial speed-up on modern hardware.Comment: 8 pages + references; International Conference on Learning Representations, 201

Character-Word LSTM Language Models

We present a Character-Word Long Short-Term Memory Language Model which both reduces the perplexity with respect to a baseline word-level language model and reduces the number of parameters of the model. Character information can reveal structural (dis)similarities between words and can even be used when a word is out-of-vocabulary, thus improving the modeling of infrequent and unknown words. By concatenating word and character embeddings, we achieve up to 2.77% relative improvement on English compared to a baseline model with a similar amount of parameters and 4.57% on Dutch. Moreover, we also outperform baseline word-level models with a larger number of parameters

Radar and video as the perfect match : a cooperative method for sensor fusion

Accurate detection and tracking of road users is essential for driverless cars and many other smart mobility applications. As no single sensor can provide the required accuracy and robustness, the output from several sensors needs to be combined. Especially radar and video are a good match, because their weaknesses and strengths complement each other. Researchers from IPI – an imec research group at Ghent University – developed a new technique to optimize radar-video fusion by exchanging information at an earlier stage

Improving Source Separation via Multi-Speaker Representations

Lately there have been novel developments in deep learning towards solving the cocktail party problem. Initial results are very promising and allow for more research in the domain. One technique that has not yet been explored in the neural network approach to this task is speaker adaptation. Intuitively, information on the speakers that we are trying to separate seems fundamentally important for the speaker separation task. However, retrieving this speaker information is challenging since the speaker identities are not known a priori and multiple speakers are simultaneously active. There is thus some sort of chicken and egg problem. To tackle this, source signals and i-vectors are estimated alternately. We show that blind multi-speaker adaptation improves the results of the network and that (in our case) the network is not capable of adequately retrieving this useful speaker information itself