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Overview Microsoft Research’s Project InnerEye team in Cambridge (UK) worked with University Hospitals Birmingham NHS Foundation Trust to develop deep learning models to analyze anonymized chest X-Rays and chest computed tomography (CT) scans to assist clinicians in determining disease severity, aid decision…

Two people side by side, Matt Brodsky on the left and Jaime Teevan on the right, in black and white smile and look forward. Teevan is holding a cell phone.
Microsoft Research Podcast

In this episode of the podcast, Chief Scientist Jaime Teevan and Senior User Research Manager Matt Brodsky examine how the level of IT support available during the shift, including the ability to provide hardware and software, made the difference between laying…

In the news | Quanta Magazine

Computer proof assistants have been an intriguing subplot in mathematics for years — promising to automate core aspects of the way mathematicians work, but in practice having little effect on the field. But a new result, completed in early June,…

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2021年7月27日，中国北京——微软公司今天宣布，微软杰出首席科学家、原微软亚洲研究院常务副院长周礼栋博士升任微软亚洲研究院院长，全面负责微软亚洲研究院在北京和上海的研究工作，以及与中国及亚太地区学术界和产业界的合作。洪小文博士不再兼任微软亚洲研究院院长一职，作为微软全球资深副总裁和微软亚太研发集团主席，洪小文博士将继续负责推动微软在亚太地区的整体研发战略布局和创新生态系统的构建。 微软全球资深...

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With physical products, it’s easy to see the impact from material sourcing, manufacturing and usage all the way to what happens at the end of a product’s life. When thinking about digital design, however, things get trickier. The resources used are far-removed and abstract concepts that…

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编者按：计算机科学的发展无疑推动了人工智能技术的进步，在这场21世纪的信息革命中，研究和发展人工智能理论及应用是当前科技背景下的必然趋势。近日，图灵奖得主 John Hopcroft 教授与微软亚洲研究院院长洪小文博士在研究院展开了一场炉边谈话。在此期间，John Hopcroft 讲述了自己对人工智能领域发展的独到见解，以及对人才培养的建议，两位专家还分享了对未来研究和技术趋势的各自观点。让我们...

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编者按：微软亚洲研究院“铸星计划”旨在发掘和助力新一代青年学者，使其成为科研创新能力突出、走在世界科技前沿的学术带头人。无论是与领域内顶尖研究员合作的机会，还是最新、最丰富的数据集和强大的支持资源，亦或是产业界独有的实际应用场景，都吸引着青年才俊们来到 MSRA 探索领域内前沿新知。 年轻、开拓、探索，是铸星计划的关键词；合作、创新、成就，是每个学术新星发光闪耀的必经之途。来自东北大学的王莹老师，...

While the NNGP and NTK limits essentially only considers the neural network initialization, the feature learning limit incorporates the entire training trajectory. A Neural network is represented by a stack of vertical shapes: an inverted trapezoid, a square, and a triangle. On the left side of the shape, A blue arrow moves upward and represents the first forward pass. The NNGP limit can be thought of as the limit of this first forward pass. On the right side of the shape, a green arrow moves downward and represents the first backward pass. The NTK limit can be thought of as the limit for this first backward pass. In contrast, the feature learning limit takes into account the many cycles of forward and backward passes that take place during the entire training process. These cycles are represented by many repetitions of blue upward arrow and green downward arrows to the right of the neural network. An orange box encloses all of these cycles. On top of the box is the annotation “SGD Training Progress” with an arrow to the right. An arrow comes out from the bottom of the box pointing to a textbox that says “Feature Learning Limit, This Work.”
Microsoft Research Blog

| Edward Hu and Greg Yang

In the pursuit of learning about fundamentals of the natural world, scientists have had success with coming at discoveries from both a bottom-up and top-down approach. Neuroscience is a great example of the former. Spanish anatomist Santiago Ramón y Cajal…

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In the news | Microsoft Customers Stories

Pearson helps people around the globe learn foreign languages so that they can advance in academics and careers. When primary school students learn English, they often need more practice and feedback from teachers and parents than they can get. Pearson…