SRI Blog

 Sho Yokoi, Ryo Takahashi ,Reina Akama, Jun Suzuki, Kentaro Inui Abstract A key principle in assessing textual similarity is measuring the degree of semantic overlap between two texts by considering the word alignment. Such alignment-based approaches are intuitive and interpretable; however, they are empirically inferior to the simple cosine similarity between general-purpose sentence vectors. To address this issue, we focus on and demonstrate the fact that the norm of word vectors is a good proxy for word importance, and their angle is a good proxy for word similarity. Alignment-based approaches do not distinguish them, whereas sentence-vector approaches automatically use the norm as the word importance. Accordingly, we propose a method that first decouples word vectors into their norm and direction, and then computes alignment-based similarity using earth mover’s distance (i.e., optimal transport cost), which we refer to as word rotator’s distance. Besides, we find how to “grow” the

 - By MALLIKARJUN B R and AYUSH TEWARI, MPI for Informatics, SIC, Germany and 9 others Paper Link Abstract Photorealistic editing of head portraits is a challenging task as humans are very sensitive to inconsistencies in faces. Paper present an approach for high-quality intuitive editing of the camera viewpoint and scene illumination (parameterised with an environment map) in a portrait image. This requires our method to capture and control the full reflectance field of the person in the image. Most editing approaches rely on supervised learning using training data captured with setups such as light and camera stages. Such datasets are expensive to acquire, not readily available and do not capture all the rich variations of in-the-wild portrait images. In addition, most supervised approaches only focus on relighting and do not allow camera viewpoint editing.  Paper present a method that learns from limited supervised training data. The training images only include people in a fixed neu

  Other learning paradigms are about minimization;  Reinforcement learning is about maximization . The statement quoted above has been attributed to Harry Klopf, though it might only be accurate in sentiment. The statement may sound vacuous, since minimization can be converted to maximization simply via negation of an objective. However, further reflection reveals a deeper observation. Many learning algorithms aim to mimic observed patterns, minimizing differences between model and data. Reinforcement learning is distinguished by its open-ended view. A reinforcement learning agent learns to improve its behavior over time, without a prescription for eventual dynamics or the limits of performance. If the objective takes non-negative values, minimization suggests a well-defined desired outcome while maximization conjures pursuit of the unknown Video Courtesy:bdtechtalks.com What happens when AI Plays Hide and Seek 500 Times Paper By - Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, 

  Ranwa Al Mallah , Godwin Badu-Marfo , Bilal Farooq image Courtesy: Comparitech Abstract Federated learning (FL) is a machine learning technique that aims at training an algorithm across decentralized entities holding their local data private. Wireless mobile networks allow users to communicate with other fixed or mobile users. The road traffic network represents an infrastructure-based configuration of a wireless mobile network where the Connected and Automated Vehicles (CAV) represent the communicating entities. Applying FL in a wireless mobile network setting gives rise to a new threat in the mobile environment that is very different from the traditional fixed networks. The threat is due to the intrinsic characteristics of the wireless medium and is caused by the characteristics of the vehicular networks such as high node-mobility and rapidly changing topology. Most cyber defense techniques depend on highly reliable and connected networks. This paper explores falsified information