Facebook

About: Facebook is a company organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Computer science & Artificial neural network. The organization has 7856 authors who have published 10906 publications receiving 570123 citations. The organization is also known as: facebook.com & FB.

Improved Road Connectivity by Joint Learning of Orientation and Segmentation

Abstract: Road network extraction from satellite images often produce fragmented road segments leading to road maps unfit for real applications. Pixel-wise classification fails to predict topologically correct and connected road masks due to the absence of connectivity supervision and difficulty in enforcing topological constraints. In this paper, we propose a connectivity task called Orientation Learning, motivated by the human behavior of annotating roads by tracing it at a specific orientation. We also develop a stacked multi-branch convolutional module to effectively utilize the mutual information between orientation learning and segmentation tasks. These contributions ensure that the model predicts topologically correct and connected road masks. We also propose Connectivity Refinement approach to further enhance the estimated road networks. The refinement model is pre-trained to connect and refine the corrupted ground-truth masks and later fine-tuned to enhance the predicted road masks. We demonstrate the advantages of our approach on two diverse road extraction datasets SpaceNet and DeepGlobe. Our approach improves over the state-of-the-art techniques by 9% and 7.5% in road topology metric on SpaceNet and DeepGlobe, respectively.

White-box vs Black-box: Bayes Optimal Strategies for Membership Inference.

Abstract: Membership inference determines, given a sample and trained parameters of a machine learning model, whether the sample was part of the training set. In this paper, we derive the optimal strategy for membership inference with a few assumptions on the distribution of the parameters. We show that optimal attacks only depend on the loss function, and thus black-box attacks are as good as white-box attacks. As the optimal strategy is not tractable, we provide approximations of it leading to several inference methods, and show that existing membership inference methods are coarser approximations of this optimal strategy. Our membership attacks outperform the state of the art in various settings, ranging from a simple logistic regression to more complex architectures and datasets, such as ResNet-101 and Imagenet.

Deforming autoencoders: Unsupervised disentangling of shape and appearance

Abstract: In this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner. As in the deformable template paradigm, shape is represented as a deformation between a canonical coordinate system (‘template’) and an observed image, while appearance is modeled in deformation-invariant, template coordinates. We introduce novel techniques that allow this approach to be deployed in the setting of autoencoders and show that this method can be used for unsupervised group-wise image alignment. We show experiments with expression morphing in humans, hands, and digits, face manipulation, such as shape and appearance interpolation, as well as unsupervised landmark localization. We also achieve a more powerful form of unsupervised disentangling in template coordinates, that successfully decomposes face images into shading and albedo, allowing us to further manipulate face images.

Inductive Matrix Completion Based on Graph Neural Networks

Abstract: We propose an inductive matrix completion model without using side information. By factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows (users) and columns (items), a majority of existing matrix completion methods are transductive, since the learned embeddings cannot generalize to unseen rows/columns or to new matrices. To make matrix completion inductive, content (side information), such as user's age or movie's genre, has to be used previously. However, high-quality content is not always available, and can be hard to extract. Under the extreme setting where not any side information is available other than the matrix to complete, can we still learn an inductive matrix completion model? In this paper, we investigate this seemingly impossible problem and propose an Inductive Graph-based Matrix Completion (IGMC) model without using any side information. It trains a graph neural network (GNN) based purely on local subgraphs around (user, item) pairs generated from the rating matrix and maps these subgraphs to their corresponding ratings. Our model achieves highly competitive performance with state-of-the-art transductive baselines. In addition, since our model is inductive, it can generalize to users/items unseen during the training (given that their ratings exist), and can even transfer to new tasks. Our transfer learning experiments show that a model trained out of the MovieLens dataset can be directly used to predict Douban movie ratings and works surprisingly well. Our work demonstrates that: 1) it is possible to train inductive matrix completion models without using any side information while achieving state-of-the-art performance; 2) local graph patterns around a (user, item) pair are effective predictors of the rating this user gives to the item; and 3) we can transfer models trained on existing recommendation tasks to new tasks without any retraining.

Ambiguous structured search queries on online social networks

Abstract: Ambiguous Structured Search Queries on Online Social Networks In one embodiment, a method includes accessing a social graph that includes a plurality of nodes and edges, receiving an unstructured text query comprising an ambiguous n-gram, identifying nodes and edges that correspond to the ambiguous n-gram, generating a first set of structured queries corresponding to the identified second nodes and edges, receiving from the first user a selection of a first structured query form the first set, and generating a second set of structured queries based on the selected first structured query. WO 2014/105640 PCT/US2013/076590 SOCIAL-NETWORKING SYSTEM ,160 DATA STORE- ---- .CLIENT SYSTEM 130 -150 ty ..NETWORK BROWSER THIRD PARTY SYSTEM .170