In this article we provide a formulation of empirical bayes described by Atchade (2011) to tune the hyperparameters of priors used in bayesian set up of collaborative filter. We implement the same in MovieLens small dataset. We see that it can be used to get a good initial choice for the parameters. It can also be used to guess an initial choice for hyper-parameters in grid search procedure even for the datasets where MCMC oscillates around the true value or takes long time to converge.

Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating, and adapt to changes or drifts in the underlying data generating distribution. In this paper, we approach these problems from a Bayesian perspective covering general conjugate exponential models. Our proposal makes use of non-conjugate hierarchical priors to explicitly model temporal changes of the model parameters. We also derive a novel variational inference scheme which overcomes the use of non-conjugate priors while maintaining the computational efficiency of variational methods over conjugate models. The approach is validated on three real data sets over three latent variable models.

We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure the cross-modality similarity. However, previous methods often establish the shared embedding space based on linear mapping functions which might not be sophisticated enough to reveal more complicated inter-modal correspondences. Additionally, current studies assume that the rankings are of equal importance, and thus all rankings are used simultaneously, or a small number of rankings are selected randomly to train the embedding space at each iteration. Such strategies, however, always suffer from outliers as well as reduced generalization capability due to their lack of insightful understanding of procedure of human cognition. In this paper, we involve the self-paced learning theory with diversity into the cross-modal learning to rank and learn an optimal multi-modal embedding space based on non-linear mapping functions. This strategy enhances the model's robustness to outliers and achieves better generalization via training the model gradually from easy rankings by diverse queries to more complex ones. An efficient alternative algorithm is exploited to solve the proposed challenging problem with fast convergence in practice. Extensive experimental results on several benchmark datasets indicate that the proposed method achieves significant improvements over the state-of-the-arts in this literature.

We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths. Our model family augments graphical structure in latent variables with neural network observation models. For inference, we extend variational autoencoders to use graphical model approximating distributions with recognition networks that output conjugate potentials. All components of these models are learned simultaneously with a single objective, giving a scalable algorithm that leverages stochastic variational inference, natural gradients, graphical model message passing, and the reparameterization trick. We illustrate this framework with several example models and an application to mouse behavioral phenotyping.

This paper presents a statistical method for use in music transcription that can estimate score times of note onsets and offsets from polyphonic MIDI performance signals. Because performed note durations can deviate largely from score-indicated values, previous methods had the problem of not being able to accurately estimate offset score times (or note values) and thus could only output incomplete musical scores. Based on observations that the pitch context and onset score times are influential on the configuration of note values, we construct a context-tree model that provides prior distributions of note values using these features and combine it with a performance model in the framework of Markov random fields. Evaluation results show that our method reduces the average error rate by around 40 percent compared to existing/simple methods. We also confirmed that, in our model, the score model plays a more important role than the performance model, and it automatically captures the voice structure by unsupervised learning.

Be it climate change or rogue artificial intelligence, Elon Musk often turns to Twitter to share his concerns regarding the future of life on Earth. And this time, the tech boss has offered a grave perspective on the fate of humanity. Responding to a recent article which argues the world may soon hit'peak person' as fertility rates drop, Musk warned the global population is'accelerating towards collapse, but few seem to notice or care.' Be it climate change or rogue artificial intelligence, Elon Musk often turns to Twitter to share his concerns regarding the future of life on Earth. When asked at the Code Conference in California if the answer to the question of whether we are in a simulated computer game was'yes', Elon Musk said'probably.' Musk believes that computer game technology, particularly virtual reality, is already approaching a point that it is indistinguishable from reality.

Citrine Informatics, the chemicals and materials artificial intelligence (AI) platform, today announced it has won the World Materials Forum Start-up Challenge Award. The Start-up Challenge recognizes innovative materials solutions from all over the world and Citrine was chosen as the winner from a group of 12 semi-finalists. Citrine combines AI with the world's largest materials database to help bring high performance products to market faster for the Fortune 1000. The AI is specifically optimized to take advantage of known relationships in chemistry and physics. "We are excited for this recognition from one of the most important organizations in the field of materials science," said Greg Mulholland, CEO of Citrine.

Chatbots, deep learning, concierge apps, big data -- these words may seem meaningless today, but they offer an incredible opportunity available for event professionals who are willing to embrace innovation. Artificial intelligence (AI) will soon revolutionize the events industry and play a pivotal role in growing your event. From customer support to event management and marketing, AI may change how we experience live events -- and give yours a competitive advantage. Join Event Manager Blog's Julius Solaris as he answers your most burning questions about AI. What is useful and what is not?

With AI or deep learning able to integrate with content marketing efforts, current data suggests that although 57.1% of US marketers remain unlikely to use AI or deep learning in their 2017 content marketing, a significant number felt differently. BrightEdge and Survey Monkey polled 1,019 marketers worldwide and found that a third (31.4%) of respondents said they would use AI to help flesh out their content marketing strategy this year. And an additional 8.7% said they were very likely to do so. Meanwhile, 2.8% said they're already using AI to develop their content efforts, eMarketer reports. Additionally, more marketers are likely investing in AI because they're confident there is a demand for it.