In a recently published paper in this year’s INTERSPEECH, we were able to achieve additional improvement on the efficiency of Asynchronous Decentralized Parallel Stochastic Gradient Descent, reducing the training time from 11.5 hours to 5.2 hours using 64 NVIDIA V100 GPUs.
IBM scientists presented three papers at INTERSPEECH 2019 that address the shortcomings of End-to-end automatic approaches for speech recognition - an emerging paradigm in the field of neural network-based speech recognition that offers multiple benefits.
IBM Research's papers at INTERSPEECH 2019 showcase our focus on improving the underlying speech technologies that enable companies provide their customers with a uniformly good experience across different channels and extract actionable insights from these interactions.
Can Artificial Intelligence (AI) capture the narrative of a community on a controversial topic to provide an unbiased outcome? Recently, the citizens of Lugano, a city of more than 60,000 citizens on the Swiss-Italian border, provided the answer. What is Project Debater? In February 2019, IBM unveiled Project Debater to the world. It’s the first ever AI technology […]
IBM researchers published the first major release of the Adversarial Robustness 360 Toolbox (ART). Initially released in April 2018, ART is an open-source library for adversarial machine learning that provides researchers and developers with state-of-the-art tools to defend and verify AI models against adversarial attacks. ART addresses growing concerns about people’s trust in AI, specifically the security of AI in mission-critical applications.
It is no surprise that following the massive success of deep learning technology in solving complicated tasks, there is a growing demand for automated deep learning. Even though deep learning is a highly effective technology, there is a tremendous amount of human effort that goes into designing a deep learning algorithm.
Convex optimization problems, which involve the minimization of a convex function over a convex set, can be approximated in theory to any fixed precision in polynomial time. However, practical algorithms are known only for special cases. An important question is whether it is possible to develop algorithms for a broader subset of convex optimization problems that are efficient in both theory and practice.
IBM researchers have partnered with scientists from MIT, Northeastern University, Boston University and University of Minnesota to publish two papers on novel attacks and defenses for graph neural networks and on a new robust training algorithm called hierarchical random switching at IJCAI 2019.
We report new research results relevant to AI planning in our paper, "Depth-First Memory-Limited AND/OR Search and Unsolvability in Cyclic Search Spaces," presented at the International Joint Conference on Artificial Intelligence, IJCAI-19.
At IJCAI'19, IBM researchers present new results on causal knowledge extraction from large amounts of text for applications in enterprise risk management.