The ConditionaL Neural Networks (CLNN) and the Masked ConditionaL Neural Networks (MCLNN) exploit the nature of multi-dimensional temporal signals. The CLNN captures the conditional temporal influence between the frames in a window and the mask in the MCLNN enforces a systematic sparseness that follows a filterbank-like pattern over the network links. The mask induces the network to learn about time-frequency representations in bands, allowing the network to sustain frequency shifts. Additionally, the mask in the MCLNN automates the exploration of a range of feature combinations, usually done through an exhaustive manual search. We have evaluated the MCLNN performance using the Ballroom and Homburg datasets of music genres. MCLNN has achieved accuracies that are competitive to state-of-the-art handcrafted attempts in addition to models based on Convolutional Neural Networks.

We propose a hybrid algorithm for top-$n$ recommendation task that allows to incorporate both user and item side information within the standard collaborative filtering approach. The algorithm extends PureSVD -- one of the state-of-the-art latent factor models -- by exploiting a generalized formulation of the singular value decomposition. This allows to inherit key advantages of the classical algorithm such as highly efficient Lanczos-based optimization procedure, minimal parameter tuning during a model selection phase and a quick folding-in computation to generate recommendations instantly even in a highly dynamic online environment. Within the generalized formulation itself we provide an efficient scheme for side information fusion which avoids undesirable computational overhead and addresses the scalability question. Evaluation of the model is performed in both standard and cold-start scenarios using the datasets with different sparsity levels. We demonstrate in which cases our approach outperforms conventional methods and also provide some intuition on when it may give no significant improvement.

Artificial Intelligence, as we know today, is procuring a positive outcome in our society. The AI's ability to perform tasks with minimising errors and maximising efficiency has been helping in jobs that are better suited for computers. Many production companies have witnessed increased production and effective cost reduction with the help of Artificial Intelligence. Artificial intelligence is also being considered to replace humans in dangerous situations like working in radioactive elements and limited oxygen areas. Additionally, organisations are benefiting with greater work accuracy with the use of Artificial Intelligence, thereby providing a certain degree of perfection and trust; and helping humans live more satisfactorily.

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Recently, Google's CEO, Sundar Pichai, likened the impact of artificial intelligence to the discovery of fire. In the same way our distant ancestors' world was transformed by an invention that literally turned night into day, the foundations of how our society operates is about to undergo a similar shift. But will artificial intelligence really live up to the hype? That all depends on you and what you decide to do with it. Fire was meaningless until creative cavemen figured out exactly what to do with it.

To be honest, some applications of artificial intelligence can be mind-boggling and in fact, the technology is luring businesses into taking unimaginable ventures. For the longest time, Alibaba has maintained the position of being China's leading e-commerce firm.

I am looking for a series of videos that I can use to learn Pytorch. I found out that there are some presentations, but any recommended videos to go through Deep Learning tutorials with Pytorch would be appreciated.

Hoy traemos a este espacio el nuevo número de iJET International Journal of Emerging Technologies in Learning el último de 2017 Vol 12, No 12 (2017) Table of Contents Papers Application of Digital Music Technology in Music Pedagogy Peiwei Zhang, Xin Sui Music Solfeggio Learning Platform Construction and Application Qiao Zhou, Baihui Yan The Effects of the CALL Model on College English Reading Teaching Dan Zhang, Xiaoying Wang The Construction of Intelligent English Teaching Model Based on Artificial Intelligence Design and Implementation of English Reading Examination System Based on WEB Platform Lan Guo, Zhiyu Zhao, Lu Bai, Jing Lv, Xin Zhao On Spoken English Phoneme Evaluation Method Based on Sphinx-4 Computer System Computer Multimedia Assisted English Vocabulary Teaching Courseware Multi-Interactive Teaching Model of College English in Computer Information Technology Environment Design Flow of English Learning System Based on Item Response Theory Yuemei Liu, Xuetao Zhao Application of Kinect Technology in Blind Aerobics Learning Short Papers Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis Haiyun Li, Xuebo Zhang, Junhui Wang Evaluation of Sports Visualization Based on Wearable Devices Application of Data Mining in Library-Based Personalized Learning A Personalized Recommender System Based on Library Database Music Learning Based on Computer Software Baihui Yan, Qiao Zhou International Journal of Emerging Technologies in Learning.

There has been a discussion recently about using RL to land a SpaceX booster. Coincidentally I've been working on exactly this in OpenAI. It was as much fun as it was frustrating at times. It's trained with a PPO implementation from Unity that I've changed to work with OpenAI (GitHub). The official OpenAI implementation is convoluted and impossible to work with in my opinion. This particular agent took 200'000 tries over the course of 12 hours and 20 million frames (with a frame skip value of 5, so 100 million total frames).

It's a widely known (if little discussed) fact that the adult film industry often serves as a pioneer for video technology. It was the industry's early backing of VHS that helped to bury Betamax and its embrace of Blu-ray that helped shovel dirt over rival HD-DVD. So we should all take notice of a series of reports in Motherboard about the emergence of a new type of adult video technology--only this format doesn't have the imprimatur of established adult filmmakers or tech companies behind it. Instead, it's the brainchild of coders working with machine learning tools to swap the faces of adult actresses with those of established Hollywood celebrities. These so-called "deep fakes" are growing in popularity, Motherboard notes.