Text to speech (TTS) has attracted a lot of attention recently due to advancements in deep learning. Neural network-based TTS models (such as Tacotron 2, DeepVoice 3 and Transformer TTS) have outperformed conventional concatenative and statistical parametric approaches in terms of speech quality. Neural network-based…
Artificial intelligence has evolved to become a revolutionary technology. It is rapidly changing the economy, both by creating new opportunities (it’s the backbone of the gig economy) and by bringing venerable institutions, like transportation, into the 21st century. Yet deep at its core something is…
Having experience in deep learning doesn’t hurt when it comes to the often mysterious, time- and cost-consuming process of hunting down an appropriate neural architecture. But truth be told, no one really knows what works the best on a new dataset and task. Relying on…
Drone racing has transformed from a niche activity sparked by enthusiastic hobbyists to an internationally televised sport. In parallel, computer vision and machine learning are making rapid progress, along with advances in agile trajectory planning, control, and state estimation for quadcopters. These advances enable increased…
Last summer we announced Microsoft Research Open Data—an Azure-based repository-as-a-service for sharing datasets—to encourage the reproducibility of research and make research data assets readily available in the cloud. Among other things, the project started a conversation between the community and Microsoft’s legal team about dataset…
Memory is an important part of human intelligence and the human experience. It grounds us in the current moment, helping us understand where we are and, consequently, what we should do next. Consider the simple example of reading a book. The ultimate goal is to…
Imagine moving to a new city. You want to get from your new home to your new job. Unfamiliar with the area, you ask your co-workers for the best route, and as far as you can tell ... they’re right! You get to work and…
One of the core directions of Project Malmo is to develop AI capable of rich interactions. Whether that means learning new skills to apply to challenging problems, understanding complex environments, or knowing when to enlist the help of humans, reinforcement learning (RL) is a core…
In many real-life scenarios, obtaining information is costly, and getting fully observed data is almost impossible. For example, in the recruiting world, obtaining relevant information (in other words, a feature value) for a company could mean performing time-consuming interviews. The same applies to many other…
While reinforcement learning (RL) has seen significant successes over the past few years, modern deep RL methods are often criticized for how sensitive they are with respect to their hyper-parameters. One such hyper-parameter is the discount factor, which controls how future rewards are weighted compared…
Deep Neural Networks (DNNs) have facilitated tremendous progress across a range of applications, including image classification, translation, language modeling, and video captioning. DNN training is extremely time-consuming, needing efficient multi-accelerator parallelization. In “PipeDream: Generalized Pipeline Parallelism for DNN Training,” published at the 27th ACM Symposium…
When people create, it’s not very often they achieve what they’re looking for on the first try. Creating—whether it be a painting, a paper, or a machine learning model—is a process that has a starting point from which new elements and ideas are added and…
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