Although recommender systems have received great success, We categorize recommender systems as shallow it is well known for highly skewed datasets, models and deep models. The first class engineers and researchers need to adjust their incorporates shallow machine learning technologies methods to tackle the specific problem to yield good such as matrix factorization and learning to rank, results. Inability to deal with highly skewed dataset while the second class are deep learning models like usually generates hard computational problems for Wide and Deep [6]. Although a bit of out-of-dated, big data clusters and unsatisfactory results for shallow models are still widely used in small customers. In this paper, we propose a new companies and projects where agility, usability and algorithm solving the problem in the framework of matrix factorization. We model the data skewness efficiency far outweighs boost of performance which factors in the theoretic modeling of the approach is only economically visible for huge datasets. It is with easy to interpret and easy to implement well known since the invention of the first shallow formulas. We prove in experiments our method model, that data skewness and sparsity poses generates comparably favorite results with popular serious challenges for recommender system recommender system algorithms such as Learning performance. The setbacks are two folds: data to Rank, Alternating Least Squares and Deep Matrix skewness causes problems that need special Factorization.

In this work, we study a new image annotation task named Extractive Tags Summarization (ETS). The goal is to extract important tags from the context lying in an image and its corresponding tags. We adjust some state-of-the-art deep learning models to utilize both visual and textual information. Our proposed solution consists of different widely used blocks like convolutional and self-attention layers, together with a novel idea of combining auxiliary loss functions and the gating mechanism to glue and elevate these fundamental components and form a unified architecture. Besides, we introduce a loss function that aims to reduce the imbalance of the training data and a simple but effective data augmentation technique dedicated to alleviates the effect of outliers on the final results. Last but not least, we explore an unsupervised pre-training strategy to further boost the performance of the model by making use of the abundant amount of available unlabeled data. Our model shows the good results as 90% $F_\text{1}$ score on the public NUS-WIDE benchmark, and 50% $F_\text{1}$ score on a noisy large-scale real-world private dataset. Source code for reproducing the experiments is publicly available at: https://github.com/pixta-dev/labteam

The "You only look once v4"(YOLOv4) is one type of object detection methods in deep learning. YOLOv4-tiny is proposed based on YOLOv4 to simple the network structure and reduce parameters, which makes it be suitable for developing on the mobile and embedded devices. To improve the real-time of object detection, a fast object detection method is proposed based on YOLOv4-tiny. It firstly uses two ResBlock-D modules in ResNet-D network instead of two CSPBlock modules in Yolov4-tiny, which reduces the computation complexity. Secondly, it designs an auxiliary residual network block to extract more feature information of object to reduce detection error. In the design of auxiliary network, two consecutive 3x3 convolutions are used to obtain 5x5 receptive fields to extract global features, and channel attention and spatial attention are also used to extract more effective information. In the end, it merges the auxiliary network and backbone network to construct the whole network structure of improved YOLOv4-tiny. Simulation results show that the proposed method has faster object detection than YOLOv4-tiny and YOLOv3-tiny, and almost the same mean value of average precision as the YOLOv4-tiny. It is more suitable for real-time object detection.

Active learning effectively collects data instances for training deep learning models when the labeled dataset is limited and the annotation cost is high. Besides active learning, data augmentation is also an effective technique to enlarge the limited amount of labeled instances. However, the potential gain from virtual instances generated by data augmentation has not been considered in the acquisition process of active learning yet. Looking ahead the effect of data augmentation in the process of acquisition would select and generate the data instances that are informative for training the model. Hence, this paper proposes Look-Ahead Data Acquisition via augmentation, or LADA, to integrate data acquisition and data augmentation. LADA considers both 1) unlabeled data instance to be selected and 2) virtual data instance to be generated by data augmentation, in advance of the acquisition process. Moreover, to enhance the informativeness of the virtual data instances, LADA optimizes the data augmentation policy to maximize the predictive acquisition score, resulting in the proposal of InfoMixup and InfoSTN. As LADA is a generalizable framework, we experiment with the various combinations of acquisition and augmentation methods. The performance of LADA shows a significant improvement over the recent augmentation and acquisition baselines which were independently applied to the benchmark datasets.

Surface anomaly detection plays an important quality control role in many manufacturing industries to reduce scrap production. Machine-based visual inspections have been utilized in recent years to conduct this task instead of human experts. In particular, deep learning Convolutional Neural Networks (CNNs) have been at the forefront of these image processing-based solutions due to their predictive accuracy and efficiency. Training a CNN on a classification objective requires a sufficiently large amount of defective data, which is often not available. In this paper, we address that challenge by training the CNN on surface texture patches with a distance-based anomaly detection objective instead. A deep residual-based triplet network model is utilized, and defective training samples are synthesized exclusively from non-defective samples via random erasing techniques to directly learn a similarity metric between the same-class samples and out-of-class samples. Evaluation results demonstrate the approach's strength in detecting different types of anomalies, such as bent, broken, or cracked surfaces, for known surfaces that are part of the training data and unseen novel surfaces.

We propose a novel genetic-algorithm technique that generates black-box adversarial examples which successfully fool neural network based text classifiers. We perform a genetic search with multi-objective optimization guided by deep learning based inferences and Seq2Seq mutation to generate semantically similar but imperceptible adversaries. We compare our approach with DeepWordBug (DWB) on SST and IMDB sentiment datasets by attacking three trained models viz. char-LSTM, word-LSTM and elmo-LSTM. On an average, we achieve an attack success rate of 65.67% for SST and 36.45% for IMDB across the three models showing an improvement of 49.48% and 101% respectively. Furthermore, our qualitative study indicates that 94% of the time, the users were not able to distinguish between an original and adversarial sample.

This article was published as a part of the Data Science Blogathon. Today I am going to try my best in explaining in an intuitive way how Convolutional Recurrent Neural Networks (CRNN) work. When I first tried learning about how CRNN work, I found that the information was split between multiple sites and different levels of "depth" was also present, so I will try to explain them in a way that by the end of this article you will know how exactly do they work and why do they perform better in some categories than others. In this article, I will assume you already know a bit about how a simple Neural Network works. In case you need a little revision of the way it works or even if you don't know at all how they work, I recommend you watch the well-made videos explaining the way they work that I linked at the end of the article.

It is a branch of Machine Learning which uses a simulation of the human brain which is known as neural networks. These neural networks are made up of neurons that are similar to the fundamental unit of the human brain. The neurons make up a neural network model and this field of study altogether is named deep learning. The end result of a neural network is called a deep learning model. Mostly, in deep learning, unstructured data is used from which the deep learning model extracts features on its own by repeated training on the data.

Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems, which have proven extremely difficult to solve. The application of deep learning methods has recently exploded in popularity, leading to impressive successes in the context of medical imaging and also finding first use in the field of PAI. Deep learning methods possess unique advantages that can facilitate the clinical translation of PAI, such as extremely fast computation times and the fact that they can be adapted to any given problem.

You can also think of the activations from the few-shot examples as fast weights. Depending on how many examples there are, the number of fast weights can actually be comparable to the number of normal weights e.g. a transformer with 24 layers and dim 1024 has like 400 million parameters. If you have 1024 example tokens, then there are 2 * 24 * 1024 * 1024 48 million numbers from key/value activations. The activations are comparatively tiny for a model like GPT-3 though. I think for most cases with GPT-3, the few-shot examples aren't teaching it how to reason per se so much as contextualizing e.g.