SRI Blog

 -By Hugh Leather - University of Edinburgh Edinburgh,                                                   Chris Cummins -  Facebook AI Research Paper Link   image Courtesy:Embecosm Abstract Writing optimising compilers is difficult. The range of programs that may be presented to the compiler is huge and the systems on which they run are complex, heterogeneous, non-deterministic, and constantly changing. The space of possible optimisations is also vast, making it very hard for compiler writers to design heuristics that take all of these considerations into account. As a result, many compiler optimisations are out of date or poorly tuned. A retrospective of machine learning in compiler optimisation from its earliest inception, through some of the works that set themselves apart, to today’s deep learning, finishing with our visi...

 -By Hayata Yamasaki,  Sathyawageeswar Subramanian,  Sho Sonoda,  Masato Koashi Paper Link image Courtesy: Fossguru.com Abstract Kernel methods augmented with random features give scalable algorithms for learning from big data. But it has been computationally hard to sample random features according to a probability distribution that is optimized for the data, so as to minimize the required number of features for achieving the learning to the desired accuracy. Here, we develop a quantum algorithm for sampling from this optimized distribution over features, in runtime O(D) that is linear in the dimension D of the input data. Our algorithm achieves an exponential speedup in D compared to any known classical algorithm for this sampling task. In contrast to existing quantum machine learning algorithms, our algorithm circumvents sparsity and low-rank assumptions and thus has wide applicability. We also show that the sampled features can be combined with regression by...

 He guo, Xiameng Qin, Jiaming Liu, Junyu Han, Jingtuo Liu, Errui Ding Department of Computer Vision Technology(VIS), Baidu Inc Paper link Abstract Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts. Most of the existing works employ classical detection and recognition paradigm. Paper proposes an Entity-aware Attention Text Extraction Network called EATEN , which is an end-to-end trainable system to extract the entities without any post-processing. In the proposed framework, each entity is parsed by its corresponding entity-aware decoder, respectively. Moreover, we innovatively introduce a state transition mechanism which further improves the robustness of entity extraction. In consideration of the absence of public benchmarks, we construct a dataset of almost 0.6 million images in three realworld scenarios (train ticket, passport and business card), which is publicly available at https://github.com/b...

- 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...