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.

Bernd Malle, undefined, , undefined...

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.

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Abstract. The application of dynamic time warping (DTW) to the correction of retention time shift in chromatographic fingerprints of Radix Puerariae thomsonii (

PubMedのタイトルとアブストラクトに出現する略語とその展開形を検索します。