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Workshop in advances and future research in secure federated machine learning for the application in the health inforamatics domain

ERCIM News, the quarterly magazine of the European Research Consortium for Informatics and Mathematics

The Security Forum of the Hagenberger Kreis 2017 at the FH Hagenberg is again a great success. More than 200 participants inform themselves about the latest trends, threats and dangers of IT security during the two days. SEC4YOU is represented at the Security Forum 2017 with an

Important text examines most significant algorithms for optimizing large systems and clarifying relations between optimization procedures. Much data appear as charts and graphs and will be highly valuable to readers in selecting a method and estimating computer time and cost in problem-solving. Initial chapter on linear and nonlinear programming presents all necessary background for subjects covered in rest of book. Second chapter illustrates how large-scale mathematical programs arise from real-world problems. Appendixes. List of Symbols.

11/02/22 - Data augmentation is a critical contributing factor to the success of deep learning but heavily relies on prior domain knowledge w...

Elon Musk explains that we need a (revenue-neural) carbon tax in order to avoid a climate crisis. To see the full speech klick here: https://www.youtube.com/watch?v=iavquu6PP9g

This paper presents a comparison study between native code and different browser-based implementations: JavaScript, ASM.js as well as WebAssembly on a representative mix of algorithms, showing that current efforts in runtime optimization push the boundaries well towards (and even beyond) native binary performance. AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important. On top of many AI applications already using mobile hardware, and even browsers for computationally demanding AI applications, we are already witnessing the emergence of client-side (federated) machine learning algorithms, driven by the interests of large corporations and startups alike. Apart from mathematical and algorithmic concerns, this trend especially demands new levels of computational efficiency from client environments. Consequently, this paper deals with the question of state-of-the-art performance by presenting a comparison study between native code and different browser-based implementations: JavaScript, ASM.js as well as WebAssembly on a representative mix of algorithms. Our results show that current efforts in runtime optimization push the boundaries well towards (and even beyond) native binary performance. We analyze the results obtained and speculate on the reasons behind some surprises, rounding the paper off by outlining future possibilities as well as some of our own research efforts.

This paper presents a comparison study between native code and different browser-based implementations: JavaScript, ASM.js as well as WebAssembly on a representative mix of algorithms, showing that current efforts in runtime optimization push the boundaries well towards (and even beyond) native binary performance. AI applications pose increasing demands on performance, so it is not surprising that the era of client-side distributed software is becoming important. On top of many AI applications already using mobile hardware, and even browsers for computationally demanding AI applications, we are already witnessing the emergence of client-side (federated) machine learning algorithms, driven by the interests of large corporations and startups alike. Apart from mathematical and algorithmic concerns, this trend especially demands new levels of computational efficiency from client environments. Consequently, this paper deals with the question of state-of-the-art performance by presenting a comparison study between native code and different browser-based implementations: JavaScript, ASM.js as well as WebAssembly on a representative mix of algorithms. Our results show that current efforts in runtime optimization push the boundaries well towards (and even beyond) native binary performance. We analyze the results obtained and speculate on the reasons behind some surprises, rounding the paper off by outlining future possibilities as well as some of our own research efforts.

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AMiner利用数据挖掘和社会网络分析与挖掘技术，提供研究者语义信息抽取、面向话题的专家搜索、权威机构搜索、话题发现和趋势分析、基于话题的社会影响力分析、研究者社会网络关系识别等众多功能。

Bernd Malle, undefined, , undefined...

Founder @Lemonphi - 507 lần trích dẫn - graphs - graph embedding - distributed learning & recommenders

The bAbI Project is organized towards the goal of automatic text understanding and reasoning.

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder...

We present an integral equation which generalizes a variety of known rendering algorithms. In the course of discussing a monte carlo solution we also present a new form of variance reduction, called Hierarchical sampling and give a number of ...

Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other.

Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.