With the advent of deep neural networks, some research focuses towards understanding their black-box behavior. In this paper, we propose a new type of self-interpretable models, that are, architectures designed to provide explanations along with their predictions. Our method proceeds in two stages and is trained end-to-end: first, our model builds a low-dimensional binary representation of any input where each feature denotes the presence or absence of concepts. Then, it computes a prediction only based on this binary representation through a simple linear model. This allows an easy interpretation of the model's output in terms of presence of particular concepts in the input. The originality of our approach lies in the fact that concepts are automatically discovered at training time, without the need for additional supervision. Concepts correspond to a set of patterns, built on local low-level features (e.g a part of an image, a word in a sentence), easily identifiable from the other concepts. We experimentally demonstrate the relevance of our approach using classification tasks on two types of data, text and image, by showing its predictive performance and interpretability.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. Whether you got the day off or you're stuck working like me, what better way to spend your day than by scoring a great deal? To help you find the best sales, I spent my morning scoping out the options to figure out which are actually worth your time. If you're more in the mood to browse a sitewide sale, Memorial Day is chock full of them, and we've got the inside scoop on the best ones: Drop took an amazing pair of headphones and made them even better. A good pair of headphones is a must if you're a music lover.

As Europe (together with Australia and Israel) are glued to their TV sets watching the 64th Eurovision song competition, we asked ourselves What makes a Eurovision song memorable? We are a group of artists, musicians and programmers that wanted explore human creativity and challenge it. We have created a Eurovision AI song that celebrates Eurovision – its melodrama, kitsch and camp, its humor and its gimmicks. The result is comprised entirely of material written and composed by Artificial Intelligence, titled "Blue Jeans & Bloody Tears". The project team fed hundreds of Eurovision songs – melodies and lyrics – into a neuron network.

We tackle the problem of object discovery, where objects are segmented for a given input image, and the system is trained without using any direct supervision whatsoever. A novel copy-pasting GAN framework is proposed, where the generator learns to discover an object in one image by compositing it into another image such that the discriminator cannot tell that the resulting image is fake. After carefully addressing subtle issues, such as preventing the generator from `cheating', this game results in the generator learning to select objects, as copy-pasting objects is most likely to fool the discriminator. The system is shown to work well on four very different datasets, including large object appearance variations in challenging cluttered backgrounds.

Value functions are crucial for model-free Reinforcement Learning (RL) to obtain a policy implicitly or guide the policy updates. Value estimation heavily depends on the stochasticity of environmental dynamics and the quality of reward signals. In this paper, we propose a two-step understanding of value estimation from the perspective of future prediction, through decomposing the value function into a reward-independent future dynamics part and a policy-independent trajectory return part. We then derive a practical deep RL algorithm from the above decomposition, consisting of a convolutional trajectory representation model, a conditional variational dynamics model to predict the expected representation of future trajectory and a convex trajectory return model that maps a trajectory representation to its return. Our algorithm is evaluated in MuJoCo continuous control tasks and shows superior results under both common settings and delayed reward settings.

When the Amazon Prime Day deals roll into town in July, it's a no-brainer that the Amazon Echo Dot will get a big discount like it does every year. The thing is, it'll be nowhere near as good as this one - and it ends at 9am tomorrow. This is a UK-only deal, but we've rounded up the latest top deals in the US over on our Memorial Day sales page. UK readers, be sure to check out the Bank Holiday sales this weekend too. We're expecting Prime Day to bring the Echo Dot down to £29.99 as per usual, or £24.99 at the very best if Google aggressively price cuts the Google Home Mini as the search giant can't help but wind Amazon up any chance it gets with its line of rival smart speakers.

I have seen several posts in this subreddit asking for resources for learning Machine Learning as part of their CS Career. I have listed some of my Python Machine Learning videos below and I hope that people with an interest in Python and learning MI would find it useful. Please feel free to give feedback as I'm always looking to improve my Computer Science skills.

Google AI has introduced a deep learning based approach that generates depth prediction from videos where both camera and subject are in motion. Humans are very good at making sense of the 3D world through 2D projections. Even in complex environments with objects in motion we can still form a fairly sound understanding of where everything is. Computer vision however does not do so well in this regard. Researchers in the field have long sought to develop a mechanism capable of achieving 3D world understanding by reconstructing geometry and depth ordering from 2D image data via computation.

Artificial Intelligence (AI) is the new electricity of our times. That's what Chris Duffey, creative technologist says about this incredible technology revolutionizing industries the world over. His new book Superhuman Innovation showcases how AI will supercharge the workforce, the world of work, and can be harnessed to deliver powerful change. It is a practical guide to how AI and Machine Learning are impacting not only how businesses, brands, and agencies innovate, but also what they innovate: products, services and content. In this world of product and pricing parity, the delivery of superior service experience has become the new marketing, and the new real competitive edge. Superhuman Innovation discusses how AI will serve the superstar innovators of tomorrow by enabling them to see deeper insights and set sail for higher goals.

Transcribing content from structural images, e.g., writing notes from music scores, is a challenging task as not only the content objects should be recognized, but the internal structure should also be preserved. Existing image recognition methods mainly work on images with simple content (e.g., text lines with characters), but are not capable to identify ones with more complex content (e.g., structured symbols), which often follow a fine-grained grammar. To this end, in this paper, we propose a hierarchical Spotlight Transcribing Network (STN) framework followed by a two-stage "where-to-what" solution. Specifically, we first decide "where-to-look" through a novel spotlight mechanism to focus on different areas of the original image following its structure. Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly. Moreover, we propose two implementations on the basis of STN, i.e., STNM and STNR, where the spotlight movement follows the Markov property and Recurrent modeling, respectively. We also design a reinforcement method to refine the framework by self-improving the spotlight mechanism. We conduct extensive experiments on many structural image datasets, where the results clearly demonstrate the effectiveness of STN framework.