Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide …

Continuous-time event data are common in applications such as individual behavior data, financial transactions, and medical health records. Modeling such data can be very challenging, in particular for applications with many different types of events, since it requires a model to predict the event types as well as the time of occurrence. Recurrent neural networks that parameterize time-varying intensity functions are the current state-of-the-art for predictive modeling with such data. These models typically assume that all event sequences come from the same data distribution. However, in many applications event sequences are generated by different sources, or users, and their characteristics can be very different. In this paper, we extend the broad class of neural marked point process models to mixtures of latent embeddings, where each mixture component models the characteristic traits of a given user. Our approach relies on augmenting these models with a latent variable that encodes user characteristics, represented by a mixture model over user behavior that is trained via amortized variational inference. We evaluate our methods on four large real-world datasets and demonstrate systematic improvements from our approach over existing work for a variety of predictive metrics such as log-likelihood, next event ranking, and source-of-sequence identification.

Since the introduction of Generative Adversarial Networks (GANs) [Goodfellow et al., 2014] there has been a regular stream of both technical advances (e.g., Arjovsky et al. [2017]) and creative uses of these generative models (e.g., [Karras et al., 2019, Zhu et al., 2017, Jin et al., 2017]). In this work we propose an approach for using the power of GANs to automatically generate videos to accompany audio recordings by aligning to spectral properties of the recording. This allows musicians to explore new forms of multi-modal creative expression, where musical performance can induce an AIgenerated musical video that is guided by said performance, as well as a medium for creating a visual narrative to follow a storyline (similar to what was proposed by Frosst and Kereliuk [2019]). When trained properly, these latent spaces are learned in a structured manner, where nearby points generate similar images. For our work we make use of the BigGAN family of models [Brock et al., 2019], which are class-conditional generative models.

The roadmap aims to not only provide learners with an overall concept of … Paths include data science, machine learning, deep learning, and data …

In first collaboration with INNOTECH, OSARO supplies machine learning software to create smart general-purpose piece picking station.

"Hell is when other people are fake" -- Jean-Paul Sartre writing in 2020. Have you ever read Jean-Paul Sartre's famous play No Exit? The main character, Garcin, cries out "Hell is--other people!" after realizing hell is not torture racks and fire, but interpersonal strife manufactured by Lucifer to push sufferers to the brink of psychological collapse. Physical torture would be infinitely easier to bear, Garcin declares, than the vicissitudes of continuous socialization. If you had roommates in quarantine, you might relate.

Video consumption was skyrocketing even before the lockdowns. And over the past few months Netflix, Amazon, HBO Now and most recently Disney Plus have seen their subscriber base explode. For the operators running the networks that deliver all that content, it has been a test of business agility to reconfigure networks fast to keep up with the changing situation on the ground, ensuring their subscribers could continue making Zoom calls and binge a boxset. In Italy, the first country in Europe to enforce a lockdown, peak throughput on the mobile network increased by up to 90% compared to the weeks before lockdown. In Spain, the mobile network experienced a 35% increase in throughput, while over fixed networks it increased 50%.

When I first watched Ironman, I was thrilled with its advancement in technologies. But what hooked me to the movie was J.A.R.V.I.S. (Just A Rather Very Intelligent System). Well, what is not to like about it. The AI system was so intelligent that it painted the ironman suit with a single command. The best thing is that now we have the best AI Android apps that are making this dream come true. AI apps are there to assist the users to achieve their daily targets and accomplish their goals. Well, some of them can have completely different functionalities that will be described further.

Artificial intelligence, defined broadly, is the field of training machines to … Beneath that umbrella is machine learning, in which machines …

New Delhi, Nov 3 (PTI) The railways is set to go big with artificial intelligence and data analytics to improve its operational efficiency and has tied up …