What is the minimal description that captures a space? Asking a mathematician’s basic question of a biological dataset reveals interesting answers about biology itself. This summarizes our underlying approach to subtyping hematological cancer. Disease subtyping is a central tenet of precision medicine, and is the challenging task of identifying and classifying patients with similar presentations […]
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.
At ACL 2019, IBM researchers released a paper detailing the model they trained to answer complex questions using Neural Program Induction, which allows an AI model can be taught to procedurally decompose a complex task into a program.
IBM Research AI and the University of Michigan are organizing a public competition to inspire and evaluate novel approaches that will lead to the next generation of AI-driven dialog systems.
A team of researchers from IBM Research AI and AI Horizons Network-partner the University of Michigan published the papers “A Large-Scale Corpus for Conversation Disentanglement” and “Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use” at ACL 2019. This work address two main challenges in building enterprise AI assistants.
At ACL 2019, IBM researchers will present a demonstration of HEIDL, a model that makes it easier and much faster for people to review the effectiveness of natural language labels generated by a deep learning model trained on human-labeled data.
IBM Research AI and IBM Watson worked together to develop a promising approach that achievies state-of-the-art performance on relation extraction. This work is being presented at ACL 2019.
The latest work on computational argumentation from the IBM Project Debater research team group is being presented at the ACL 2019 conference. Three papers will be presented at the main conference and one more paper will be presented in the co-located Argument Mining Workshop.