Many parametric statistical models are not properly normalised and only specified up to an intractable partition function, which renders parameter estimation difficult. Examples of unnormalised models are Gibbs distributions, Markov random fields, and neural network models in unsupervised deep learning. In previous work, the estimation principle called noise-contrastive estimation (NCE) was introduced where unnormalised models are estimated by learning to distinguish between data and auxiliary noise. An open question is how to best choose the auxiliary noise distribution. We here propose a new method that addresses this issue. The proposed method shares with NCE the idea of formulating density estimation as a supervised learning problem but in contrast to NCE, the proposed method leverages the observed data when generating noise samples. The noise can thus be generated in a semi-automated manner. We first present the underlying theory of the new method, show that score matching emerges as a limiting case, validate the method on continuous and discrete valued synthetic data, and show that we can expect an improved performance compared to NCE when the data lie in a lower-dimensional manifold. Then we demonstrate its applicability in unsupervised deep learning by estimating a four-layer neural image model.

With the rapid development of robot and other intelligent and autonomous agents, how a human could be influenced by a robot's expressed mood when making decisions becomes a crucial question in human-robot interaction. In this pilot study, we investigate (1) in what way a robot can express a certain mood to influence a human's decision making behavioral model; (2) how and to what extent the human will be influenced in a game theoretic setting. More specifically, we create an NLP model to generate sentences that adhere to a specific affective expression profile. We use these sentences for a humanoid robot as it plays a Stackelberg security game against a human. We investigate the behavioral model of the human player.

We introduce a method for embedding words as probability densities in a low-dimensional space. Rather than assuming that a word embedding is fixed across the entire text collection, as in standard word embedding methods, in our Bayesian model we generate it from a word-specific prior density for each occurrence of a given word. Intuitively, for each word, the prior density encodes the distribution of its potential 'meanings'. These prior densities are conceptually similar to Gaussian embeddings. Interestingly, unlike the Gaussian embeddings, we can also obtain context-specific densities: they encode uncertainty about the sense of a word given its context and correspond to posterior distributions within our model. The context-dependent densities have many potential applications: for example, we show that they can be directly used in the lexical substitution task. We describe an effective estimation method based on the variational autoencoding framework. We also demonstrate that our embeddings achieve competitive results on standard benchmarks.

Diffusions and related random walk procedures are of central importance in many areas of machine learning, data analysis, and applied mathematics. Because they spread mass agnostically at each step in an iterative manner, they can sometimes spread mass "too aggressively," thereby failing to find the "right" clusters. We introduce a novel Capacity Releasing Diffusion (CRD) Process, which is both faster and stays more local than the classical spectral diffusion process. As an application, we use our CRD Process to develop an improved local algorithm for graph clustering. Our local graph clustering method can find local clusters in a model of clustering where one begins the CRD Process in a cluster whose vertices are connected better internally than externally by an $O(\log^2 n)$ factor, where $n$ is the number of nodes in the cluster. Thus, our CRD Process is the first local graph clustering algorithm that is not subject to the well-known quadratic Cheeger barrier. Our result requires a certain smoothness condition, which we expect to be an artifact of our analysis. Our empirical evaluation demonstrates improved results, in particular for realistic social graphs where there are moderately good---but not very good---clusters.

A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. As the studies of linguistics have proved that language is not linear word sequence but sequence of complex structure, translation at each step should be conditioned on the whole target-side context. To tackle the problem, we propose a new NMT model that decodes the sequence with the guidance of its structural prediction of the context of the target sequence. Our model generates translation based on the structural prediction of the target-side context so that the translation can be freed from the bind of sequential order. Experimental results demonstrate that our model is more competitive compared with the state-of-the-art methods, and the analysis reflects that our model is also robust to translating sentences of different lengths and it also reduces repetition with the instruction from the target-side context for decoding.

All the G7 nations have agreed at their summit in Canada on the importance of a "rules-based trading system", despite tensions with the US. The joint statement signed by US President Donald Trump and his counterparts comes amid a row over high US tariffs imposed this month on steel and aluminium imports. The EU and Canada have taken steps to retaliate. Mr Trump says tariffs are needed to reverse America's trade deficit. Soon after the joint statement was announced, the US president tweeted defiantly about not allowing "other countries to impose massive tariffs and trade barriers on its on farmers, workers and companies".

Global Artificial Intelligence in Retail Market is Expected to Grow From US$ 712.6 Million in 2016 to US$ 27,238.6 Inception of exponential technologies such as sensors, robotics, virtual reality, and artificial intelligence in the retail industry has enabled the retailers to enhance their interactions with consumers and transformed the way retail operations were performed. This change in the industry is prominently driven by the seismic shift in the shopping pattern of the consumers, and their preferences backed by demographic dividend across regions. The report focuses on an in-depth segmentation of this market based by retail format, technology, and application. The geographic segmentation of the report covers five major regions including; North Americas, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America (SA).

As every entrepreneur will know, securing that first round of seed capital is often the most crucial step in getting any business off the ground. And for their part, seed investors play a crucial role as tech influencers or tastemakers, often backing a trend years before it becomes mainstream. But what do these European tech tastemakers of today think is coming tomorrow? Artificial intelligence is the number one sector that seed investors in Europe are obsessing over, with a whopping 70% saying they're most excited about what's going on in the space. Mosaic Ventures, which has backed companies like period-tracking app Clue and crypto wallet Blockchain, this morning published the figures as part of an extensive study into the sentiment of 60 top European seed funders.

SCALE AI welcomes the Canada – France proposed creation of an international study group to promote a vision of artificial intelligence (AI) which will benefit society at large. This joint initiative was announced on June 7, ahead of the G7 meeting in Charlevoix, Quebec. The study group's mission will be to support and guide the development of ethical and inclusive AI technologies, aimed at shaping a better future for all. Innovation and adoption of AI technologies are accelerating very rapidly and have the potential to deliver massive social, societal and business value. The study group will provide foresight, expertise, and an international cooperation platform to help seize this major opportunity and address the challenges that go with it.

More than 2,500 women have stripped naked on a beach in the Republic of Ireland to break the world record for the largest skinny dip.