DENOISING, Machine Learning Leave a comment

Adversarial score matching and improved sampling for image generation

Denoising score matching with Annealed Langevin Sampling (DSM-ALS) is a recent approach to generative modeling. Despite the convincing visual quality of samples, this method appears to perform worse than Generative Adversarial Networks (GANs) under the Fr’echet Inception Distance, a popular metric for generative models… We show that this apparent gap vanishes when denoising the final Langevin samples using the score network. In addition, we propose two improvements to DSM-ALS: 1) Consistent Annealed Sampling as a more stable alternative to Annealed […]

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Artificial intelligence Leave a comment

DeepSpeed: Extreme-scale model training for everyone

In February, we announced DeepSpeed, an open-source deep learning training optimization library, and ZeRO (Zero Redundancy Optimizer), a novel memory optimization technology in the library, which vastly advances large model training by improving scale, speed, cost, and usability. DeepSpeed has enabled researchers to create Turing Natural Language Generation (Turing-NLG), the largest language model with 17 billion parameters and state-of-the-art accuracy at the time of its release. In May, we released ZeRO-2—supporting model training of 200 billion parameters up to 10x […]

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Context-aware Neural MT, NLP

Issue #98 – Unified and Multi-encoders for Context-aware Neural MT

10 Sep20 Issue #98 – Unified and Multi-encoders for Context-aware Neural MT Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction Context-aware Neural MT uses context information to perform document-level translation or domain adaptation. The context of surrounding sentences allows the model to capture discourse phenomena. The context of similar sentences can also be useful to dynamically adapt the translation to a domain. In this post, we take a look at two papers which compare uni-encoder and multi-encoder […]

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Graphics and multimedia Leave a comment

Expressive Pixels: A new visual communication platform to support creativity, accessibility, and innovation

The need to express oneself is innate for every person in the world, and its roots run through art, technology, communication, and the acts of learning and building things from the ground up. It’s no coincidence, then, that a new platform being released by Microsoft Research, called Expressive Pixels, stems from this belief. Expressive Pixels introduces an authoring app combined with open-source firmware, peripherals, documentation, and APIs that allow users and makers to create animations and then display them on […]

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Artificial intelligence Leave a comment

Platform for Situated Intelligence: An open-source framework for multimodal, integrative AI

Over the years at Microsoft Research, we’ve studied how to build AI systems that perceive, understand, and act in a human-filled world in real time. Our motivation has been to create computing systems that can support interactive experiences akin to what we expect when we talk to or collaborate with people. This research line has involved the development of several physically situated interactive applications, including embodied conversational agents that serve as personal assistants, robots that give directions in our building, […]

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Artificial intelligence Leave a comment

Domain-specific language model pretraining for biomedical natural language processing

COVID-19 highlights a perennial problem facing scientists around the globe: how do we stay up to date with the cutting edge of scientific knowledge? In just a few months since the pandemic emerged, tens of thousands of research papers have been published concerning COVID-19 and the SARS-CoV-2 virus. This explosive growth sparks the creation of the COVID-19 Open Research Dataset (CORD-19) to facilitate research and discovery. However, a pandemic is just one salient example of a prevailing challenge to this […]

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Artificial intelligence Leave a comment

Microsoft HoloLens 2: Improved Research Mode to facilitate computer vision research

Since its launch in November 2019, Microsoft HoloLens 2 has helped enterprises in manufacturing, construction, healthcare, and retail onboard employees more quickly, complete tasks faster, and greatly reduce errors and waste. It sets the high-water mark for intelligent edge devices by leveraging a multitude of sensors and a dedicated ASIC (Application-Specific Integrated Circuit) to allow multiple real-time computer vision workloads to run continuously. In Research Mode, HoloLens 2 is also a potent computer vision research device. (Note: Research Mode is […]

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annotations, NLP

Issue #96 – Using Annotations for Machine Translating Named Entities

27 Aug20 Issue #96 – Using Annotations for Machine Translating Named Entities Author: Dr. Carla Parra Escartín, Global Program Manager @ Iconic Introduction Getting the translation of named entities right is not a trivial task and Machine Translation (MT) has traditionally struggled with it. If a named entity is wrongly translated, the human eye will quickly spot it, and more often than not, those mistranslations will make people burst into laughter as machines can, seemingly, be very creative. To a […]

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Artificial intelligence Leave a comment

MineRL sample-efficient reinforcement learning challenge—back for a second year—benefits organizers, as well as larger research community

To unearth a diamond in the block-based open world of Minecraft requires the acquisition of materials and the construction of tools before any diamond mining can even begin. Players need to gather wood, which they’ll use to make a wood pickaxe for mining stone underground. They’ll use the stone to fashion a stone pickaxe and, with the tool upgrade, mine iron ore. They’ll build a furnace for smelting the iron and use that to make the iron pickaxe they need […]

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bilingual word embeddings, NLP

Issue #94 – Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation

13 Aug20 Issue #94 – Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation Author: Dr. Chao-Hong Liu, Machine Translation Scientist @ Iconic Introduction Curating corpora of quality sentence pairs is a fundamental task to building Machine Translation (MT) systems. This resource can be availed from Translation Memory (TM) systems where the human translations are recorded. However, in most cases we don’t have TM databases but comparable corpora, e.g. news articles of the same story in different languages. […]

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