Scientific machine learning allows us to tackle data-driven modeling in … novel deep learning and traditional physics-based modeling to address …

… the state and telecommunications industry has the potential to also create job opportunities in artificial intelligence, machine learning and …

Avi Veidman is the CEO at Nucleai, the provider of an AI-powered oncology platform that utilizes machine learning to offer insights into cancer …

Project title: Deep Learning-Aided Diagnosis of Autoimmune Blistering Diseases. Ryan Clairmont, 17, Canyon Crest Academy

Netflix launched in 1997 as a mail-based DVD rental business. Alongside the growing US DVD market in the late 1990s and early 2000s, Netflix's business grew and the company went public in 2002. Netflix posted its first profit a year later. By 2007, Netflix introduced its streaming service, and by 2013, the company began producing original content. Today, Netflix is one of the world's largest entertainment services with over 200 million paid memberships spanning 190 countries, according to the company's 2020 Annual Report.

The "machine–learning" patrol model analyzed more than 6 million officer responses to nearly 2 million calls for service, the Greater Austin Crime …

… Haven and Texas State University Criminal Justice and Criminology. That project developed a "machine-learning patrol model" and community survey.

Global Machine Learning in Healthcare Market Research Report with Opportunities and Strategies to Boost Growth- COVID-19 Impact and Recovery is …

Controlling the manner in which a character moves in a real-time animation system is a challenging task with useful applications. Existing style transfer systems require access to a reference content motion clip, however, in real-time systems the future motion content is unknown and liable to change with user input. In this work we present a style modelling system that uses an animation synthesis network to model motion content based on local motion phases. An additional style modulation network uses feature-wise transformations to modulate style in real-time. To evaluate our method, we create and release a new style modelling dataset, 100STYLE, containing over 4 million frames of stylised locomotion data in 100 different styles that present a number of challenges for existing systems. To model these styles, we extend the local phase calculation with a contact-free formulation. In comparison to other methods for real-time style modelling, we show our system is more robust and efficient in its style representation while improving motion quality.

News feed recommendation is an important web service. In recent years, pre-trained language models (PLMs) have been intensively applied to improve the recommendation quality. However, the utilization of these deep models is limited in many aspects, such as lack of explainability and being incompatible with the existing inverted index systems. Above all, the PLMs based recommenders are inefficient, as the encoding of user-side information will take huge computation costs. Although the computation can be accelerated with efficient transformers or distilled PLMs, it is still not enough to make timely recommendations for the active users, who are associated with super long news browsing histories. In this work, we tackle the efficient news recommendation problem from a distinctive perspective. Instead of relying on the entire input (i.e., the collection of news articles a user ever browsed), we argue that the user's interest can be fully captured merely with those representative keywords. Motivated by this, we propose GateFormer, where the input data is gated before feeding into transformers. The gating module is made personalized, lightweight and end-to-end learnable, such that it may perform accurate and efficient filtering of informative user input. GateFormer achieves highly impressive performances in experiments, where it notably outperforms the existing acceleration approaches in both accuracy and efficiency. We also surprisingly find that even with over 10-fold compression of the original input, GateFormer is still able to maintain on-par performances with the SOTA methods.