IBM researchers and geoscientists from Eni, a leading global energy company, are building an augmented intelligence platform based on AI called cognitive discovery to support Eni's decision-making during the initial stages of hydrocarbon exploration, which naturally occur in crude oil.
Data augmentation is one of the leading methods to tackle the problem of few-shot learning, but current synthesis approaches only address the scenario of a single label per image, when in reality real life images may contain multiple objects. The IBM team came up with a novel technique for synthesizing samples with multiple labels.
Our team of IBM researchers published research in Radiology around a new AI model that can predict the development of malignant breast cancer in patients within the year, at rates comparable to human radiologists.
Understanding of the macroscopic behavior of deep learning neural networks.
Classical computing has served society incredibly well. It gave us the Internet and cashless commerce. It sent humans to the moon, put robots on Mars and smartphones in our pockets. But many of the world’s biggest mysteries and potentially greatest opportunities remain beyond the grasp of classical computers forever. To continue the pace of progress, […]
We’re introducing a number of significant enhancements to the IBM Q Experience quantum cloud services and software platform.
At CVPR 2019, IBM researchers introduce techniques to interpret visually descriptive language to generate compositional scene representations from textual descriptions.
At CVPR 2019, IBM researchers introduce an improved method to bridge the semantic gap between visual scenes and language to produce diverse, creative and human-like captions.
Deep neural networks have demonstrated good results for few-shot learning. However, very few works have investigated the problem of few-shot object detection. A team of IBM researchers developed a novel approach for Distance Metric Learning (DML).
New techniques make fine tuning an AI model more efficient when doing transfer learning
IBM researchers, in collaboration with NYU and MIT, propose a novel alternative to backprop at ICML 2019 that offers competitive performance.
For the second year in a row, researchers at the IBM-Illinois Center for Cognitive Computing Systems Research(C3SR) won a competition challenging experts worldwide to design low-power embedded systems for Internet-of-Things (IoT) applications. The 2019 Design Automation Conference (DAC) System Design Contest’s objective: create algorithms that can accurately detect and locate objects from images taken by […]