Deep Learning, NLP, NMT, AI, ML
Welcome 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. In this article Microsoft Turing Universal Language Representation model, T-ULRv6, tops both XTREME and GLUE leaderboards with a single model Barun Patra, Saksham Singhal, To finish reading, please visit source site
Read more
Introduction In this tutorial, we will do an in-depth understanding of how to use torch.sum() function to sum the elements in PyTorch tensor. We will first understand its syntax and then cover its functionalities with various examples and illustrations to make it easy for beginners. What is torch.sum() function in PyTorch The torch sum() function is used to sum up the elements inside the tensor in PyTorch along a given dimension or axis. On the surface, this may look like […]
Read more
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]]]) Sum of Tensor1 and Tensor2: tensor([[[ 4, 6, 8], [10, 12, 14], [16, 18, 20], [22, 24, 26]], [[28, 30, 32], [34, […]
Read more
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 […]
Read more
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
Read more
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() […]
Read more
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 more