MACHINE LEARNING
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Torch 7: Open Source System
This system provides a powerful environment for state-of-the-art machine learning algorithms. It is easy to use and provides a very efficient implementation, thanks to the fast scripting language (Lua) and underlying C implementations.
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eigen Video Understanding
Aiming to achieve near-human video understanding, in this project we analyze several gigabytes of spatiotemporal data to perform action recognition, multi-person tracking, object permanence and video reasoning. eigen has built a scalable video-understanding platform for long-form video reasoning that scales to new environments and camera angles without any re-training.
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Machine Learning Development Environment
Data analytics requires a wide range of tools for a wide range of tasks: data collection, cleaning and labeling; model training and testing; presenting results, etc. Our system is designed for both performance and ease of use.
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Digital Pathology
In problems with a large number of labels, most multi-label and multi-class techniques incur a significant computational burden at test time. This is because, for each test instance, they need to systematically evaluate every label to decide whether it is relevant for the instance or not.
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Deep Learning
We have been developing neural network learning algorithms for more than a decade, and several of the most successful algorithms in use today have been created at NEC Labs. These include original algorithms for image/video interpretation and text analysis.
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Semantic Analysis & Reasoning
We develop several types of algorithms for high-level semantic analysis, which are used for tasks such as scene interpretation, document retrieval and question answering. For text interpretation, a syntactic analysis extracts relevant elements followed by concept interpretation. To combine different data types, a data-specific module first generates metadata representations that are integrated into a deep learning network.
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Text Understanding And Factual Generation
Our research spans many areas of text understanding and text generation, with a particular emphasis on factuality checking and factually guided text generation. NEC’s products help humans safely draw conclusions from large quantities of text that they don’t have the time to read. As a leader in the FEVER fact extraction and verification competitions, we have developed systems that achieved higher evidence precision and higher robustness to adversarial attack, and pioneered the ability to pursue missing evidence through multiple retrieval steps.
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Physics Informed Machine Learning
Since 2019, the parameters of large deep learning models have increased by over 300 times every 18 months. However, the future ML progress cannot continue simply based on using more data or creating larger models, because the growing gap between the model demand and resource supply is not sustainable.
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