User response prediction, which aims to predict the probability that a user will provide a predefined positive response in a given context such as clicking on an ad or purchasing an item, is crucial to many industrial applications such as online advertising, recommender systems, and search ranking. However, due to the high dimensionality and super sparsity of the data collected in these tasks, handcrafting cross features is inevitably time expensive. Prior studies in predicting user response leveraged the feature interactions by enhancing feature vectors with products of features to model second-order or high-order cross features, either explicitly or implicitly. Nevertheless, these existing methods can be hindered by not learning sufficient cross features due to model architecture limitations or modeling all high-order feature interactions with equal weights. This work aims to fill this gap by proposing a novel architecture Deep Cross Attentional Product Network (DCAP), which keeps cross network's benefits in modeling high-order feature interactions explicitly at the vector-wise level. Beyond that, it can differentiate the importance of different cross features in each network layer inspired by the multi-head attention mechanism and Product Neural Network (PNN), allowing practitioners to perform a more in-depth analysis of user behaviors. Additionally, our proposed model can be easily implemented and train in parallel. We conduct comprehensive experiments on three real-world datasets. The results have robustly demonstrated that our proposed model DCAP achieves superior prediction performance compared with the state-of-the-art models.

Understanding pedestrian crossing behavior is an essential goal in intelligent vehicle development, leading to an improvement in their security and traffic flow. In this paper, we developed a method called IntFormer. It is based on transformer architecture and a novel convolutional video classification model called RubiksNet. Following the evaluation procedure in a recent benchmark, we show that our model reaches state-of-the-art results with good performance ($\approx 40$ seq. per second) and size ($8\times $smaller than the best performing model), making it suitable for real-time usage. We also explore each of the input features, finding that ego-vehicle speed is the most important variable, possibly due to the similarity in crossing cases in PIE dataset.

Performance of recommender systems (RS) relies heavily on the Many large scale e-commerce platforms (such as Etsy, Overstock, amount of training data available. This poses a chicken-and-egg etc) and online content platforms (such as Spotify, Overstock, Disney, problem for early-stage products, whose amount of data, in turn, Netflix, etc) have such a large inventory of items that showcasing relies on the performance of their RS. On the other hand, zero-shot all of them in front of their users is simply not practical. In learning promises some degree of generalization from an old dataset particular, in the online content category of businesses, it is often to an entirely new dataset. In this paper, we explore the possibility seen that users of their service do not have a crisp intent in mind of zero-shot learning in RS. We develop an algorithm, dubbed ZEro-unlike in the retail shopping experience where the users often have Shot Recommenders (ZESRec), that is trained on an old dataset a crisp intent of purchasing something. The need for personalized and generalize to a new one where there are neither overlapping recommendations therefore arises from the fact that not only it is users nor overlapping items, a setting that contrasts typical crossdomain impractical to show all the items in the catalogue but often times RS that has either overlapping users or items. Different users of such services need help discovering the next best thing from categorical item indices, i.e., item ID, in previous methods, -- be it the new and exciting movie or be it a new music album or ZESRec uses items' natural-language descriptions (or description even a piece of merchandise that they may want to consider for embeddings) as their continuous indices, and therefore naturally future buying if not immediately.

Learn to write programs using the foundational AI algorithms powering everything from NASA's Mars Rover to DeepMind's AlphaGo Zero. Learn to write AI programs using the algorithms powering everything from NASA's Mars Rover to DeepMind's AlphaGo Zero.

GPT-3 from OpenAI has captured public attention unlike any other AI model in the 21st century. The sheer flexibility of the model in performing a series of generalized tasks with near-human efficiency and accuracy is what makes it so exciting. It has created a paradigm shift in the world of Natural Language Processing(NLP), where till now the models were trained based on the ungeneralized approach to excel at one or two tasks. GPT-3 is the first step towards democratizing access to technology. It enables audiences from all walks of life to solve complex technical problems from the comfort of a user-friendly interface, which allows you to design training prompts for specific AI problems using natural language.

Stock futures cut some losses last week on Thursday and Friday as markets rallied, but today looks like more of the same with selling pressure out of the early session. Inflation worries amid a massive corporate earnings quarter saw the S&P 500 fall as much as 4% last week, so if one thing is for sure, it is that volatility appears to be making a comeback. This week, we will get more information on how the Fed is feeling about inflation with the Fed minutes to be released Wednesday amid some massive consumer earnings cues from multinationals such as Walmart WMT, Home Depot HD, and Macy's M on Tuesday. For investors looking to find the best opportunities, the deep learning algorithms at Q.ai have crunched the data to give you a set of Top Buys. Our Artificial Intelligence ("AI") systems assessed each firm on parameters of Technicals, Growth, Low Volatility Momentum, and Quality Value to find the best Top Buys.

Miniaturization has opened the possibility for a wide range of diagnostic tools, such as point-of-care detection of diseases, to be performed quickly and with very small samples. For example, unknown particles can be analyzed by passing them through nanopores and recording tiny changes in the electrical current. However, the intensity of these signals can be very low, and is often buried under random noise. New techniques for extracting the useful information are clearly needed. Now, scientists from Osaka University have used deep learning to "denoise" nanopore data.

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First things first, how are images even stored in a computer? In the above image, look how the brighter section of the channel plotted is the corresponding colour. These 3 channels or matrices come together to give us different colours, each element of the matrix denotes a pixel value ranging from 0 to 255. Gray-scale Images have one channel in them, in further discussion, when I refer to image, it is a gray-scale image. Input Size: For every small increase in the size of the input matrix, the number of parameters that have to be trained in the network increases greatly.

Artificial Intelligence, Machine Learning and, Deep Learning are the buzzwords of this century. Their wide range of applications has changed the facets of technology in every field, ranging from Healthcare, Manufacturing, Business, Education, Banking, Information Technology, and whatnot! Although these words are familiar and used widely, they are often used interchangeably. But there is a vast difference between all of these. In this article, we will explore these buzzwords and learn the difference between them.