Given that a static learning rate usually doesn't work well for static optimizers (like SGD), and there are many adaptive optimizers (like Adam and its variants), what is generally the best way to tune the learning rate for a neural net? I've come across many methods ranging from testing a (one-cycle) scheduler, reduce LR on plateau, cosine decay (with restarts), (noisy) linear cosine decay, weight decay and super convergence (?) etc Seems like it's mostly arbitrary (or chosen based on theoretical performance) and tested over and Iver again.

Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms rely on context as attribute value representation, which makes them unfeasible for real-world domains like social networks are inherently relational. We propose Relational Boosted Bandits(RB2), acontextual bandits algorithm for relational domains based on (relational) boosted trees. RB2 enables us to learn interpretable and explainable models due to the more descriptive nature of the relational representation. We empirically demonstrate the effectiveness and interpretability of RB2 on tasks such as link prediction, relational classification, and recommendations.

This article investigates multilingual evidence retrieval and claim verification as a step to combat global disinformation, a first effort of this kind, to the best of our knowledge. A 400 example mixed language English-Romanian dataset is created for cross-lingual transfer learning evaluation. We make code, datasets, and trained models available upon publication.

Such a query can be answered currently at a high human effort cost, by inspecting e.g., a JSON list of Assemblée elected officials (available from NosDeputes.fr) and manually connecting the names with those found in a national registry of companies. This considerable effort may still miss connections that could be found if one added information about politicians' and business people's spouses, information sometimes available in public knowledge bases such as DBPedia, or journalists' notes. No single query language can be used on such heterogeneous data; instead, we study methods to query the corpus by specifying some keywords and asking for all the connections that exist, in one or across several data sources, between these keywords. This problem has been studied under the name of keyword search over structured data, in particular for relational databases [49, 27], XML documents [24, 33], RDF graphs [30, 16]. However, most of these works assumed one single source of data, in which connections among nodes are clearly identified. When authors considered several data sources [31], they still assumed that one query answer comes from a single data source. In contrast, the ConnectionLens system [10] answers keyword search queries over arbitrary combinations of datasets and heterogeneous data models, independently produced by actors unaware of each other's existence.

This paper proposed several transformer-based approaches for Reliable Intelligence Identification on Vietnamese social network sites at VLSP 2020 evaluation campaign. We exploit both of monolingual and multilingual pre-trained models. Besides, we utilize the ensemble method to improve the robustness of different approaches. Our team achieved a score of 0.9378 at ROC-AUC metric in the private test set which is competitive to other participants.

Deepfake technology gained notoriety after some celebrities were made to appear to say things they never said. The latest deepfake controversy hit the music business, with far-reaching implications.

Classifying text data from a Data Source which consists of Movie Reviews. The processing of Text Data is mandatory before we start applying Machine Learning Techniques to them. We classified whether the Movie is having a positive or a negative rating by assigning them 1; if the rating is greater than 7 and 0 if the rating is less than 4. There are some unlabeled data that I did not include in my Analysis. The text_train is a list of length 25000, while I have printed the Reviews which consists of positive ratings(1).

Earlier this fall, A.I. ethicist Timnit Gebru submitted a paper for consideration at an academic conference about predictive language models: on their environmental cost, and how they could learn racist and sexist language and also spread misinformation. Since she was working for Google, the company first wanted to review the paper--which Gebru wrote with several of her colleagues--and sign off on it. She was then told by senior managers that the paper didn't meet Google's publication bar, and that she should retract it or remove the names of Google employees. Gebru wanted more clarity on why they wanted it retracted and said that if Google couldn't provide that information, she would resign. This kicked off a few days of wrangling and several intense emails--until a manager emailed Gebru's boss, saying they had accepted her resignation.

UsTwo Games continues to prove that it's not a one-hit-wonder. The studio, best known for the award-winning Monument Valley, has pulled off an equally-creative sequel, a dreamy adventure in mobile VR, and a timed exclusive for Apple Arcade about fixing objects and fraying relationships. UsTwo's latest project, Alba: A Wildlife Adventure, hits the same creative heights. It also shows that the company is comfortable making 3D games with a mostly traditional camera and control scheme. If you're looking for something chill and non-violent to play over the festive period -- a family-friendly break from Cyberpunk 2077, perhaps -- it's worth checking out via Apple Arcade, Steam or GOG.com.

Rejoice! Apple Music is finally available on Google Assistant-enabled smart speakers and displays like the Nest Audio and Nest Hub Max. Previously, only YouTube Music, Spotify, Pandora, and Deezer were able to be played natively on Google speakers. The addition of Apple Music to Google speakers is a welcome change for veteran iOS users who have previously relied on Bluetooth as a workaround to play from Apple's catalog of 70 million songs on Google Home speakers. Without further ado, here's how to start rocking out to Apple Music directly from Google speakers. Here's a look at how to link your Apple Music account in the Google Home app.