SRI Blog

-By Riya R. Ganiga, Shaguftha Zuveria Kottur, Tallapalli Surabhi, A. Parkavi, Sini Anna Alex Ramaiah Institute of Technology, Bangalore, India Paper Link Abstract Compilers are used to translate between languages or representations. In neural networks, the input is usually in the form of a compute graph with tensor computations associated with nodes which needs to be translated into executable. The compiler plays an important role in this conversion, performing optimizations and lowering. It can be used as a bridge to target multiple hardware architectures from multiple front ends and hence is a major component for scalability of neural network frameworks. Also, the optimizations and conversions done by a compiler lead to reduction in time taken to train a particular network and its implementation. courtesy:  https://www.cs.utexas.edu/~roshan Code readability, ease of construction, time and space complexity and size of the code are some of the important features of a program code. The

-By Leonardo Baldassini, Jose Antonio Rodr´ıguez Serrano  BBVA Data & Analytics Paper Link Abstract Designing the client2vec an internal library to rapidly build baselines for banking applications. Client2vec uses marginalized stacked de-noising autoencoders on current account transactions data to create vector embeddings which represent the behaviors of our clients. These representations can then be used in, and optimized against, a variety of tasks such as client segmentation, profiling and targeting. Most data analytics and commercial campaigns in retail banking revolve around the concept of behavioral similarity, for instance: studies and campaigns on client retention; product recommendations; web applications where clients can compare their expenses with those of similar people in order to better manage their own finances; data integrity tools. The analytic work behind each of these products normally requires the construction of a set of customer attributes and a model, both t

-By N. Tomashenko1 , B. M. L. Srivastava , X. Wang , E. Vincent , A. Nautsch , J. Yamagishi, Evans , J. Patino , J.-F. Bonastre1 , P.-G. Noé1 , M. Todisco University of Edinburgh, UK Paper Link Abstract The VoicePrivacy initiative aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges. In this paper, we formulate the voice anonymization task selected for the VoicePrivacy 2020 Challenge and describe the datasets used for system development and evaluation. We also present the attack models and the associated objective and subjective evaluation metrics. We introduce two anonymization baselines and report objective evaluation results. Recent years have seen mounting calls for the preservation of privacy when treating or storing personal data. This is not least the result of the European gene