It is an understatement to say that those with knowledge of Machine Learning and Data Science are in high demand. How can they not, every single thing we use on a daily basis is supported by it. The benefit would come to not just those who are practicing the craft of building software. I really think it would benefit anyone who deals with data to aid their decision making. It's time to level up.

Most online daters prefer to contact people with the same level of education as them, but a new study suggests that as people grow older they become less picky about it. Researchers based at Queensland's University of Technology, Australia, analyzed the online dating behaviors of 41,000 Australians aged between 18 and 80. The researchers said that humans usually look for similar characteristics and traits in a partner in areas such as age, attractiveness and culture, however, the internet has changed this process. Researchers based at Queensland's University of Technology, Australia, analyzed the online dating behaviors of 41,000 Australians aged between 18 and 80 The researchers analyzed the behaviors of people using the online dating website'RSVP' during a four month period in 2016. Mr Stephen Whyte, a behavioural economist and co-author of the study, said: 'Selecting a mate can be one of the largest psychological and economic decisions a person can make and has long been the subject of social science research across a range of disciplines.

What Can I Do Now? Guiding Users in a World of Automated Decisions Abstract More and more processes governing our lives use in some part an automatic decision step, where - based on a feature vector derived from an applicant - an algorithm has the decision power over the final outcome. Here we present a simple idea which gives some of the power back to the applicant by providing her with alternatives which would make the decision algorithm decide differently. It is based on a formalization reminiscent of methods used for evasion attacks, and consists in enumerating the subspaces where the classifiers decides the desired output. This has been implemented for the specific case of decision forests (ensemble methods based on decision trees), mapping the problem to an iterative version of enumerating k-cliques. We live in a world where more and more of decision affecting our lives are taken by automatic systems.

Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian non-parametrics. While these models free from choosing the number of components a-priori, computationally attractive variational inference often reintroduces the need to do so, via a truncation on the variational distribution. In this paper we present a truncation-free hybrid inference for DPMM, combining the advantages of sampling-based MCMC and variational methods. The proposed hybridization enables more efficient variational updates, while increasing model complexity only if needed. We evaluate the properties of the hybrid updates and their empirical performance in single- as well as mixed-membership models. Our method is easy to implement and performs favorably compared to existing schemas.

The problem of information fusion from multiple data-sets acquired by multimodal sensors has drawn significant research attention over the years. In this paper, we focus on a particular problem setting consisting of a physical phenomenon or a system of interest observed by multiple sensors. We assume that all sensors measure some aspects of the system of interest with additional sensor-specific and irrelevant components. Our goal is to recover the variables relevant to the observed system and to filter out the nuisance effects of the sensor-specific variables. We propose an approach based on manifold learning, which is particularly suitable for problems with multiple modalities, since it aims to capture the intrinsic structure of the data and relies on minimal prior model knowledge. Specifically, we propose a nonlinear filtering scheme, which extracts the hidden sources of variability captured by two or more sensors, that are independent of the sensor-specific components. In addition to presenting a theoretical analysis, we demonstrate our technique on real measured data for the purpose of sleep stage assessment based on multiple, multimodal sensor measurements. We show that without prior knowledge on the different modalities and on the measured system, our method gives rise to a data-driven representation that is well correlated with the underlying sleep process and is robust to noise and sensor-specific effects. Preprint submitted to Elsevier January 16, 2017 1. Introduction Often, when measuring a phenomenon of interest that arises from a complex dynamical system, a single data acquisition method is not capable of capturing its entire complexity and characteristics, and it is usually prone to noise and interferences. Recently, due to technological advances, the use of multiple types of measurement instruments and sensors have become more and more popular; nowadays, such equipment is smaller, less expensive, and can be mounted on everyday products and devices more easily. In contrast to a single sensor, multimodal sensors may capture complementary aspects and features of the measured phenomenon, and may enable us to extract a more reliable and detailed description of the measured phenomenon. The vast progress in the acquisition of multimodal data calls for the development of analysis and processing tools, which appropriately combine data from the different sensors and handle well the inherent challenges that arise.

We study large-scale kernel methods for acoustic modeling in speech recognition and compare their performance to deep neural networks (DNNs). We perform experiments on four speech recognition datasets, including the TIMIT and Broadcast News benchmark tasks, and compare these two types of models on frame-level performance metrics (accuracy, cross-entropy), as well as on recognition metrics (word/character error rate). In order to scale kernel methods to these large datasets, we use the random Fourier feature method of Rahimi and Recht (2007). We propose two novel techniques for improving the performance of kernel acoustic models. First, in order to reduce the number of random features required by kernel models, we propose a simple but effective method for feature selection. The method is able to explore a large number of non-linear features while maintaining a compact model more efficiently than existing approaches. Second, we present a number of frame-level metrics which correlate very strongly with recognition performance when computed on the heldout set; we take advantage of these correlations by monitoring these metrics during training in order to decide when to stop learning. This technique can noticeably improve the recognition performance of both DNN and kernel models, while narrowing the gap between them. Additionally, we show that the linear bottleneck method of Sainath et al. (2013) improves the performance of our kernel models significantly, in addition to speeding up training and making the models more compact. Together, these three methods dramatically improve the performance of kernel acoustic models, making their performance comparable to DNNs on the tasks we explored.

An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report an investigation into a new approach using approximate Bayesian computation (ABC) to condition model parameters to data and prior knowledge. As the case study we examine menu interaction, where we have click time data only to infer a cognitive model that implements a search behaviour with parameters such as fixation duration and recall probability. Our results demonstrate that ABC (i) improves estimates of model parameter values, (ii) enables meaningful comparisons between model variants, and (iii) supports fitting models to individual users. ABC provides ample opportunities for theoretical HCI research by allowing principled inference of model parameter values and their uncertainty.

We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the known problem of over-regularisation that has been shown to arise in regular VAEs also manifests itself in our model and leads to cluster degeneracy. We show that a heuristic called minimum information constraint that has been shown to mitigate this effect in VAEs can also be applied to improve unsupervised clustering performance with our model. Furthermore we analyse the effect of this heuristic and provide an intuition of the various processes with the help of visualizations. Finally, we demonstrate the performance of our model on synthetic data, MNIST and SVHN, showing that the obtained clusters are distinct, interpretable and result in achieving competitive performance on unsupervised clustering to the state-of-the-art results.

In this paper, we propose an efficient transfer leaning methods for training a personalized language model using a recurrent neural network with long short-term memory architecture. With our proposed fast transfer learning schemes, a general language model is updated to a personalized language model with a small amount of user data and a limited computing resource. These methods are especially useful for a mobile device environment while the data is prevented from transferring out of the device for privacy purposes. Through experiments on dialogue data in a drama, it is verified that our transfer learning methods have successfully generated the personalized language model, whose output is more similar to the personal language style in both qualitative and quantitative aspects.

HTC may have taken a bolder approach in the smartphone world with its new U Ultra and U Play, but it's decided to play it safe with its roadmap for the rest of the year. After today's launch event in Taipei, I caught up with President of Smartphone and Connected Devices Business, Chialin Chang, who confirmed that HTC will only be releasing six to seven smartphones this year. While that's a drastic cut from last year's eleven to twelve models, he claims this has so far allowed the company to focus on its smartphones' core features, in a bid to put up a better fight against other brands. In the case of the two newest phones, Chang sees machine learning as their main selling point. The exec described the so-called Sense Companion virtual assistant as a combination of Google's Awareness API, device information and third-party data.