Hardware Architecture for Deep Learning 深度学习硬件架构 Advanced Topics in Computer Science 计算机科学前沿专题 Advances in Computer Vision 计算机视觉进展 Digital and Computational Photography 数字与计算摄影 Quantitative Methods...
Hardware Architecture for Deep Learning 深度学习硬件架构 Advanced Topics in Computer Science 计算机科学前沿专题 Advances in Computer Vision 计算机视觉进展 Digital and Computational Photography 数字与计算摄影 Quantitative Methods for Natural Language Processing 自然语言处理定量方法 Modeling with Machine Learning: ...
Nature, 502(7470):172–172, 2013. 文章来源申明:本篇文章来自:EFFICIENT METHODS AND HARDWARE FOR DEEP LEARNING-augmented 可以在 公众号『运筹OR帷幄』后台 回复关键词:“韩松论文”获取由我平台编辑精心整理的韩松论文资料,如果觉得有用, 请勿吝啬你的留言和赞哦!~...
他的研究方向主要包括高效深度学习 (Efficient Deep Learning) 及自动机器学习 (AutoML)。他开发的硬件感知 (hardware-aware) 自动机器学习框架 (ProxylessNAS, Once-for-All) 可以自动为目标硬件平台和效率约束设计专用的神经网络结构,从而大幅提升神经网络在目标硬件上的性能与效率,多次获得低功耗计算机视觉竞赛奖项 (...
Song Han. My research area is the intersection of computer architecture and machine learning, especially software and hardware co-design for deep learning and its applications.Before coming to MIT, I received my B.Eng. in Electronic Engineering from Tsinghua University. During my undergrad, I was...
TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning(NeurIPS'20) Once for All: Train One Network and Specialize it for Efficient Deployment(ICLR'20) ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware(ICLR'19) ...
他的研究方向主要包括高效深度学习 (Efficient Deep Learning) 及自动机器学习 (AutoML)。他开发的硬件感知 (hardware-aware) 自动机器学习框架 (ProxylessNAS, Once-for-All) 可以自动为目标硬件平台和效率约束设计专用的神经网络结构,从而大幅提升神经网络在目标硬件上的性能与效率,多次获得低功耗计算机视觉竞赛奖项 (...
Finding high-performing small deep learning architectures: Neural Architecture Search and Meta Learning. “There are some exciting techniques being explored within the Deep Learning community,” says Thompson, “for example the ‘lottery ticket hypothesis’ where researchers are try...
They designed EfficientViT with a hardware-friendly architecture, so it could be easier to run on different types of devices, such as virtual reality headsets or the edge computers on autonomous vehicles. Their model could also be applied to other computer vision tasks, like image classification...
It involves using software to simulate networks of very basic neurons on normal computer architecture (see “10 Breakthrough Technologies: Deep Learning,” May/June 2013). But that approach, which produced Google’s cat-spotting software, relies on vast clusters of computers to run the simulated ...