We propose a novel learning-based approach for robust 3D shape matching. Our method builds upon deep functional maps and can be trained in a fully unsupervised manner. Previous deep functional map methods mainly focus on predicting optimised functional maps alone, and then rely on off-the-shelf post-processing to obtain accurate point-wise maps during inference. However, this two-stage procedure for obtaining point-wise maps often yields sub-optimal performance. In contrast, building upon recent insights about the relation between functional maps and point-wise maps, we propose a novel unsupervised loss to couple the functional maps and point-wise maps, and thereby directly obtain point-wise maps without any post-processing. Our approach obtains accurate correspondences not only for near-isometric shapes, but also for more challenging non-isometric shapes and partial shapes, as well as shapes with different discretisation or topological noise. Using a total of nine diverse datasets, we extensively evaluate the performance and demonstrate that our method substantially outperforms previous state-of-the-art methods, even compared to recent supervised methods. Our code is available at https://github.com/dongliangcao/Unsupervised-Learning-of-Robust-Spectral-Shape-Matching.

We present a deep learning-based method for propagating spatially-varying visual material attributes (e.g. texture maps or image stylizations) to larger samples of the same or similar materials. For training, we leverage images of the material taken under multiple illuminations and a dedicated data augmentation policy, making the transfer robust to novel illumination conditions and affine deformations. Our model relies on a supervised image-to-image translation framework and is agnostic to the transferred domain; we showcase a semantic segmentation, a normal map, and a stylization. Following an image analogies approach, the method only requires the training data to contain the same visual structures as the input guidance. Our approach works at interactive rates, making it suitable for material edit applications. We thoroughly evaluate our learning methodology in a controlled setup providing quantitative measures of performance. Last, we demonstrate that training the model on a single material is enough to generalize to materials of the same type without the need for massive datasets.

Artificial intelligence (AI) is being used to create new'deepfake' pop songs that sound like they're being performed by dead musicians, including Elvis Presley, Frank Sinatra, David Bowie and Michael Jackson. Jukebox, created by California-based company OpenAI, is a neural network that generates eerie approximates of pop songs in the style of multiple artists. The neural network generates music, including rudimentary singing complete with lyrics in English and a variety of instruments like guitar and piano. OpenAI has created a expansive library of new tracks, imitating a diverse selection of artists, including the Beatles, Nirvana, Katy Perry, Simon and Garfunkel, Stevie Wonder, Elton John and Ed Sheeran, as well as deceased heroes that almost appear to be brought back to life. Most of the samples have a bizarre, faraway quality to them, as if they're poorly produced demos from the 1950s that haven't seen the light of day until now.

In this paper, we propose an offline counterfactual policy estimation framework called Genie to optimize Sponsored Search Marketplace. Genie employs an open box simulation engine with click calibration model to compute the KPI impact of any modification to the system. From the experimental results on Bing traffic, we showed that Genie performs better than existing observational approaches that employs randomized experiments for traffic slices that have frequent policy updates. We also show that Genie can be used to tune completely new policies efficiently without creating risky randomized experiments due to cold start problem. As time of today, Genie hosts more than 10000 optimization jobs yearly which runs more than 30 Million processing node hours of big data jobs for Bing Ads. For the last 3 years, Genie has been proven to be the one of the major platforms to optimize Bing Ads Marketplace due to its reliability under frequent policy changes and its efficiency to minimize risks in real experiments.

Given the prevalence of data leaks, organizations appreciate the importance of implementing privacy policies to protect sensitive data. The growing field of Data Loss Prevention (DLP) offers tools to enforce such policies for both data stored within an organization and data being shared outside of an organization (e.g. through email). While the DLP community has given much attention to the problem of enforcing data privacy policies in a comprehensive manner, little has been done to support the development of such policies. We present a small user study demonstrating that developing such policies is also a very challenging problem. In our study, users were asked to evaluate various expressive file names for sensitivity; that it, they were asked to consider how broadly they were willing to share those filenames both inside and outside their place of employment. The study indicates that users interpret their employer’s privacy concerns in differing ways, resulting in complex, personalized privacy policies at the user end. These results suggest that it may be difficult for users to form a coherent organization-level privacy policy and that the results of a DLP-based enforcement of such policies (e.g. quarantined emails) may be confusing for many users in the organization.