Person re-identification (Re-ID) aims at matching images of the same person across disjoint camera views, which is a challenging problem in multimedia analysis, multimedia editing and content-based media retrieval communities. The major challenge lies in how to preserve similarity of the same person across video footages with large appearance variations, while discriminating different individuals. To address this problem, conventional methods usually consider the pairwise similarity between persons by only measuring the point to point (P2P) distance. In this paper, we propose to use deep learning technique to model a novel set to set (S2S) distance, in which the underline objective focuses on preserving the compactness of intra-class samples for each camera view, while maximizing the margin between the intra-class set and inter-class set. The S2S distance metric is consisted of three terms, namely the class-identity term, the relative distance term and the regularization term. The class-identity term keeps the intra-class samples within each camera view gathering together, the relative distance term maximizes the distance between the intra-class class set and inter-class set across different camera views, and the regularization term smoothness the parameters of deep convolutional neural network (CNN). As a result, the final learned deep model can effectively find out the matched target to the probe object among various candidates in the video gallery by learning discriminative and stable feature representations. Using the CUHK01, CUHK03, PRID2011 and Market1501 benchmark datasets, we extensively conducted comparative evaluations to demonstrate the advantages of our method over the state-of-the-art approaches.

There is a pressing need to build an architecture that could subsume these networks undera unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multistep neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. In this way, we build a unified framework that subsumes the binary or ternary networks as its special cases.More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to gated XNOR networks (or sparse binary networks) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore,the computational sparsity and the number of states in the discrete space can be flexibly modified to make it suitable for various hardware platforms.

In recent work, it was shown that combining multi-kernel based support vector machines (SVMs) can lead to near state-of-the-art performance on an action recognition dataset (HMDB-51 dataset). This was 0.4\% lower than frameworks that used hand-crafted features in addition to the deep convolutional feature extractors. In the present work, we show that combining distributed Gaussian Processes with multi-stream deep convolutional neural networks (CNN) alleviate the need to augment a neural network with hand-crafted features. In contrast to prior work, we treat each deep neural convolutional network as an expert wherein the individual predictions (and their respective uncertainties) are combined into a Product of Experts (PoE) framework.

Most of you guys out there must be thinking... What's exactly going on around me! What is AI? Am I too late to this party?! And, wasn't all this enough that people threw in a new term called deep learning? Great, another thing I have absolutely no idea about! I won't give you the clichéd line that it's never too late because that's not the point. It is actually because, a term that I loved as soon as I came across it- 'The AI Winter' - doesn't seem to ever be going to return again. It's not for the first time that the field of AI has gained popularity.

Anca Dragan UC Berkeley Ensuring that robots and humans work and play well together. Angela Schoellig University of Toronto Her algorithms are helping self-driving and self-flying vehicles get around more safely. Jianxiong Xiao AutoX His company AutoX aims to make self-driving cars more accessible. Greg Brockman OpenAI Trying to make sure that AI benefits humanity. Joshua Browder DoNotPay Using chatbots to help people avoid legal fees.

Today there are AI-powered apps that can tell you the breed of your dog or the species of a plant in seconds simply by taking a photo. When you upload an image to Facebook, your friends are identified immediately based on facial recognition technology. The ability for machines to do this specific type of analysis has, in some cases, surpassed humans, and the lifeblood of these advanced AI technologies is visual data. The entire concept of artificial intelligence is that machines can be built to perform the most human of tasks. In order to do that, they're modeled after human intelligence. For instance, the most cutting edge AI systems employ deep learning or deep neural networks that are modeled after the neural networks of the human brain.

Komputation is a neural network framework for the JVM written in the Kotlin programming language. See the TREC demo for more details.

Demand for artificial intelligence and machine learning specialists in the country are expected to see a 60 per cent rise by 2018 due to increasing adoption of automation, says KellyOCG India. According to Francis Padamadan, Country Director, KellyOCG India, a talent management solutions provider, although AI and machine adoption is on the rise in India, there is negligible talent with experience in technologies like deep learning and neutral networks.

In this post we will explain what is machine learning and deep learning at a high level with some real world examples. In future posts we will explore vertical use cases. The goal of this is not to turn you into a data scientist, but to give you a better understanding of what you can do with machine learning. Machine learning is becoming more accessible to developers, and Data scientists work with domain experts, architects, developers and data engineers, so it is important for everyone to have a better understanding of the possibilities. Every piece of information that your business generates has potential to add value.

Python is a very popular high-level language created by Guido van Rossum and first released in 1991. Python is named after the greatest comedy act of all time, Monty Python. Python can be used to create pretty much any type of application. Python has been popular for many years and it's popularity shows no signs of stopping anytime soon. Been an in demand language, knowing Python is definitely something beneficial for your career as a software developer. Python is a very widely used programming languages that can do almost anything. Having working knowledge of high level programming languages is something any software developer should have. Whether it is a script you need to run or a complete application, Python is something you can use in your daily life as a programmer. Here's a list of some of the best Python books, courses, videos and tutorials in 2017 to help you learn Python.