Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions. Due to the fundamental difference between these auctions, demand-side platforms (DSPs) have had to update ...

We introduce a novel technique for distribution learning based on a notion of sample compression. Any class of distributions that allows such a compression scheme can be learned with few samples. Moreover, if a class of distributions has such a ...

Deep neural networks have performed well on various benchmark vision tasks with large-scale labeled training data; however, such training data is expensive and time-consuming to obtain. Due to domain shift or dataset bias, directly transferring models ...

Current image emotion recognition works mainly classified the images into one dominant emotion category, or regressed the images with average dimension values by assuming that the emotions perceived among different viewers highly accord with each other. ...

We propose a novel method that combines the simulation results of a model-based prediction tool with the knowledge contained in measurement data. This mixture of the a priori information and the posteriori knowledge aims at enhancing the prediction ...

In this paper we propose to use local variation of spatiotemporal texture vectors for motion detection. The local variation is defined as the largest eigenvalue component of spatiotemporal (sp) texture vectors in certain time window at each location in a ...