Humans have an inherent ability to learn novel concepts from only a few samples and generalize these concepts to different situations. Even though today's machine learning models excel with a plethora of training data on standard recognition tasks, a considerable gap exists between machine-level pattern recognition and human-level concept learning. To narrow this gap, the Bongard Problems (BPs) were introduced as an inspirational challenge for visual cognition in intelligent systems. Despite new advances in representation learning and learning to learn, BPs remain a daunting challenge for modern AI. Inspired by the original one hundred BPs, we propose a new benchmark Bongard-LOGO for human-level concept learning and reasoning. We develop a program-guided generation technique to produce a large set of human-interpretable visual cognition problems in action-oriented LOGO language. Our benchmark captures three core properties of human cognition: 1) context-dependent perception, in which the same object may have disparate interpretations given different contexts; 2) analogy-making perception, in which some meaningful concepts are traded off for other meaningful concepts; and 3) perception with a few samples but infinite vocabulary. In experiments, we show that the state-of-the-art deep learning methods perform substantially worse than human subjects, implying that they fail to capture core human cognition properties. Finally, we discuss research directions towards a general architecture for visual reasoning to tackle this benchmark.

Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.

The high accurate identification of inertia tensor of combined spacecraft, which is composed of a servicing spacecraft and a target, is necessary to perform attitude control. Due to the uncertainty of the operating environments of combined spacecraft, the measurement noise of the angular rate may be very complex and will seriously influence the identification accuracy. This paper proposes a least square based weighted ensemble deep learning method to realize a highly accurate identification for the inertia tensor of combined spacecraft in complex operating environments. In this method, a single deep neural network regression model is firstly constructed as an individual model for the ensemble deep learning, and then is trained by enough training data and a designed training strategy. After obtaining a certain number of accurate and diverse single models, all the outputs of single models are combined by several linear functions.

Convolutional Neural Networks (CNNs), a Deep Learning algorithm, take an input image, process it, and classify it into various aspects in the image. As a class of artificial neural networks (ANNs) that lead to various computer vision tasks, CNN is attracting interest across diverse domains, including radiology. It can effectively capture the Spatial and Temporal dependencies in an image with the help of the application of relevant filters. It also performs a better fitting to the image dataset owing to the decrease in the number of parameters involved and the reusability of weights. In Convolutional Neural Networks, convolution is the first layer to excerpt features from an input image.

"Never overlook a Kaggle competition when it doesn't award prizes/ranking, it may have even more interesting stuff (like xDeepFM) for you."- The session "Deep Learning For Tabular Data" was presented at the DLDC 2020, also known as the Deep Learning DevCon 2020 by Luca Massaron, who is Senior Data Scientist and Kaggle Master. Deep Learning DevCon 2020 is the conference of the year that is hosted by the Association of Data Scientists in partnership with Analytics India Magazine. Scheduled for 29th and 30th October, the DLDC conference brought together the leading experts as well as the best minds of deep learning and machine learning industry from around the globe. In this session, Massaron started discussing a brief on what deep learning and deep neural networks are and why it is relevant.

Cyber threats continue to escalate in both sophistication and volume. Traditional approaches to threat detection, however, are no longer sufficient to ensure protection. Correspondingly, machine learning (ML) has proven highly effective at identifying and warding off cyber attacks. Machine learning's power is the result of three factors: data, compute power and algorithms. Due to its very nature, the cyber field produces substantial amounts of data.

Text Summarization is highly useful in today's digital world. I will now walk you through some important methods to implement Text Summarization. This is the traditional method, in which the process is to identify significant phrases/sentences of the text corpus and include them in the summary. The summary obtained from this method will contain the key-sentences of the original text corpus. It can be done through many methods, I will show you using gensim and spacy.

Real-time object detection project Click here to download the source code to this post ... To build our deep learning-based real-time object detector with OpenCV we'll need to (1) ... The course will teach you how to make your own classifier from only one positive image. The project is about the real time streaming, and detect objects in video games as well.

JCMR recently Announced Deep Learning Chipsets study with 200 market data Tables and Figures spread through Pages and easy to understand detailed TOC on "Global Deep Learning Chipsets Market. Global Deep Learning Chipsets Market allows you to get different methods for maximizing your profit. The research study provides estimates for Deep Learning Chipsets Forecast till 2028*. Some of the Leading key Company's Covered for this Research are Google, BrainChip, Intel, AMD, NVIDIA, Xilinx, IBM, ARM, Graphcore, Qualcomm, Amazon, Facebook, Cerebras Systems, Mobileye, Movidius, CEVA, Nervana Systems, Wave Computing Our report will be revised to address COVID-19 effects on the Global Deep Learning Chipsets Market. Global Deep Learning Chipsets Market for a Leading company is an intelligent process of gathering and analyzing the numerical data related to services and products. This Research Give idea to aims at your targeted customer's understanding, needs and wants.