Deep Learning, NLP, NMT, AI, ML
First Tensor: tensor([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]], [[12, 13, 14], [15, 16, 17], [18, 19, 20], [21, 22, 23]]]) Second Tensor: tensor([[[ 4, 5, 6], [ 7, 8, 9], [10, 11, 12], [13, 14, 15]], [[16, 17, 18], [19, 20, 21], [22, 23, 24], [25, 26, 27]]]) Tensor1 minus Tensor2: tensor([[[-4, -4, -4], [-4, -4, -4], [-4, -4, -4], [-4, -4, -4]], [[-4, -4, -4], [-4, -4, -4], [-4, […]
Read moreWelcome to Research Focus, a new series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Meet the 2022 recipients of the Microsoft Research Global PhD Fellowship Microsoft is thrilled to announce the 2022 Microsoft Research Global PhD Fellows from around the world. The program aims to empower the next generation of computing-related research talent. Microsoft recognizes the value of diversity in computing and aims to increase the pipeline […]
Read moreBy Microsoft Mixed Reality & AI Labs in Cambridge and Zurich Computer vision is one of the
Read moreWelcome to Research Focus, a new series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Clifford neural layers for PDE modeling Johannes Brandstetter, Rianne van den Berg, Max Welling, and Jayesh K. Gupta Partial differential equations (PDEs) are widely used to describe simulation of physical processes as scalar and vector fields interacting and coevolving over time. Recent research has focused on using neural surrogates to accelerate such […]
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Introduction In this article, we will show you how to create random tensors in PyTorch with torch.randint() and torch.randint_like() functions. These functions are very useful to initialize
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Introduction In this article, we will see how we can perform element-wise multiplication of tensors in PyTorch by using torch.mul() or torch.multiply() function. We will see various examples to understand better how these functions work. Element Wise Tensor Multiplication with torch.mul() & torch.multiply() torch.mul() function in PyTorch is used to do element-wise multiplication of tensors. It should be noted here that torch.multiply() is just an alias for torch.mul() function and they do the same work. Using either of torch.mul() […]
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Introduction In this tutorial, we will explain how to multiply tensors in PyTorch with torch.matmul() function. We will see its syntax and see various examples to understand its functionality in a better way. Syntax of torch.matmul() function The syntax of torch matmul function is as follows – torch.matmul(tensor1, tensor2, out) tensor1 – The first tensor for multiplication tensor2 – The second tensor for multiplication out – Output tensor, result of multiplication of tensor1 with tensor2 Functionality
Read moreFigure 1: Performance assessment methods change across the development lifecycle for complex AI systems in ways that differ from general purpose AI. The emphasis shifts from rapid technical innovation that requires easy-to-calculate aggregate performance
Read moreAs AI becomes more deeply integrated into every aspect of our lives, it is essential that AI systems perform appropriately for their intended use. We know AI models can never be perfect, so how do we decide when AI performance is ‘good enough’ for use in a real life application? Is level of accuracy a sufficient gauge? What else matters? These are questions Microsoft Research tackles every day as part of our mission to follow a responsible, human-centered approach to […]
Read moreOnline trust has come a long way since the time of centralized databases, where information was concentrated in one location and the security and validation of that information relied on a core set of people and systems. While convenient, this model of centralized management and oversight had
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