Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.

We prove near-tight concentration of measure for polynomial functions of the Ising model under high temperature.

Low-rank matrix approximation (LRMA) methods have achieved excellent accuracy among today's collaborative filtering (CF) methods. In existing LRMA methods, the rank of user/item feature matrices is typically fixed, i.e., the same rank is adopted to describe all users/items. However, our studies show that submatrices with different ranks could coexist in the same user-item rating matrix, so that approximations with fixed ranks cannot perfectly describe the internal structures of the rating matrix, therefore leading to inferior recommendation accuracy. In this paper, a mixture-rank matrix approximation (MRMA) method is proposed, in which user-item ratings can be characterized by a mixture of LRMA models with different ranks. Meanwhile, a learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to MRMA. Experimental studies on MovieLens and Netflix datasets demonstrate that MRMA can outperform six state-of-the-art LRMA-based CF methods in terms of recommendation accuracy.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. The WNBA and the Indiana Fever announced that the allegations of "hateful comments" directed toward Angel Reese on May 17 were "not substantiated." Reese and her Chicago Sky faced the Fever and Caitlin Clark, and at one point, the two had to be separated after a flagrant foul by Clark against Reese. The association announced the next day that it would launch an investigation into the alleged comments.

Texas Rep. Jasmine Crockett attacked President Donald Trump's West Point address on MSNBC and called it proof of his unfitness as commander in chief. Rep. Jasmine Crockett, D-Texas, appears to be leaning in on her rising political stardom this week, briefly sharing what appeared to be a fan-made song that referred to the Democratic firebrand as the "leader of the future." "Jasmine Crockett, she rises with the dawn. Fighting for justice, her light will never be gone," the song went. Infectious with passion, she'll never bow down.

A UFO researcher has an ominous message for humanity as governments around the world begin releasing more information about alleged contact with extraterrestrials. Dr Julia Mossbridge is a cognitive neuroscientist and a researcher of unidentified aerial phenomena (UAP) - the new term for UFOs and alien sightings. After scientists in Colombia recovered a mysterious, sphere-shaped object that many now believe is a piece of UFO technology, Mossbridge said the world is moving into an era which may soon have to deal with the knowledge that aliens exist. 'We are entering a time when we are starting to recognize as humans we don't have the control that we thought we had over everything,' Dr Mossbridge told Fox News. However, Mossbridge, who studies how humans think and also attended the May 1 congressional hearing on UAPs, said the impending disclosure of alien life could throw the worldview of many people into chaos.

Neural Information Processing Systems http://nips.cc/

While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we take a step towards general scene-level image editing by developing an automatic interaction-free object removal model. Our model learns to find and remove objects from general scene images using image-level labels and unpaired data in a generative adversarial network (GAN) framework. We achieve this with two key contributions: a two-stage editor architecture consisting of a mask generator and image in-painter that co-operate to remove objects, and a novel GAN based prior for the mask generator that allows us to flexibly incorporate knowledge about object shapes. We experimentally show on two datasets that our method effectively removes a wide variety of objects using weak supervision only.

The accurate exposure is the key of capturing high-quality photos in computational photography, especially for mobile phones that are limited by sizes of camera modules. Inspired by luminosity masks usually applied by professional photographers, in this paper, we develop a novel algorithm for learning local exposures with deep reinforcement adversarial learning. To be specific, we segment an image into sub-images that can reflect variations of dynamic range exposures according to raw low-level features. Based on these sub-images, a local exposure for each sub-image is automatically learned by virtue of policy network sequentially while the reward of learning is globally designed for striking a balance of overall exposures. The aesthetic evaluation function is approximated by discriminator in generative adversarial networks. The reinforcement learning and the adversarial learning are trained collaboratively by asynchronous deterministic policy gradient and generative loss approximation. To further simply the algorithmic architecture, we also prove the feasibility of leveraging the discriminator as the value function. Further more, we employ each local exposure to retouch the raw input image respectively, thus delivering multiple retouched images under different exposures which are fused with exposure blending. The extensive experiments verify that our algorithms are superior to state-of-the-art methods in terms of quantitative accuracy and visual illustration.

Neural Information Processing Systems http://nips.cc/