SRI Blog

- By Anatasiia Kornilova , Mikhail Salnikov, Olga Novitskaya, Maria Begicheva, Egor Sevriugov, Kirill Shcherbakov  Paper Link Github Code Abstract  In the last decade, a huge step was done in the field of mobile microscopes development as well as in the field of mobile microscopy application to real-life disease diagnostics and a lot of other important areas (air/water quality pollution, education, agriculture). In the current study, we applied image processing techniques from Deep Learning (in-focus/out-of-focus classification, image deblurring and denoising, multi-focus image fusion) to the data obtained from the mobile microscope. Overview of significant works for every the task is presented, the most suitable approaches were highlighted. With the development of optical microscopy technologies, the cost of simple microscopes has become low enough for their mass usage. A considerable role in that class plays mobile microscopy – the field where smartphone camera and computati...

-By Han Han, Vincent Lostanlen  New York University Paper Link ABSTRACT  Disentangling and recovering physical attributes, such as shape and material, from a few waveform examples is a challenging inverse problem in audio signal processing, with numerous applications in musical acoustics as well as structural engineering. We propose to address this problem via a combination of time–frequency analysis and supervised machine learning. We start by synthesizing a dataset of sounds using the functional transformation method. Then, we represent each percussive sound in terms of its time-invariant scattering transform coefficients and formulate the parametric estimation of the resonator as multidimensional regression with a deep convolutional neural network. We interpolate scattering coefficients over the surface of the drum as a surrogate for potentially missing data, and study the response of the neural network to interpolated samples. Lastly, we resynthesize drum sounds ...

-By Joana Lorenz , Maria Inês Silva, David Aparício, João Tiago Ascensão, Pedro Bizarro Feedzai Paper link                                     Git hub link Abstract Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking) harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Image courtesy: BitCoin Magazine  ...

-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 c...

-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...

-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 ...

-By Jonathan Frankle† MIT CSAIL  David J. Schwab CUNY ITS and Ari S. Morcos  of Facebook AI Research Paper link Many important aspects of neural network learning take place within the very earliest iterations or epochs of training.  For example,  Sparse  Trainable sub-networks emerge  Gradient descent moves into a small subspace  Network undergoes a critical period  Researchers examine the changes that deep neural networks undergo during this early phase of training. Over the past decade, methods for successfully training big, deep neural networks have revolutionized machine learning. Yet surprisingly, the underlying reasons for the success of these approaches remain poorly understood, despite remarkable empirical performance. A large body of work has focused on understanding what happens during the later stages of training, while the initial phase has been less explored.  Research is built ...