Meta-Experience Replay (MER) integrates meta-learning and experience replay to achieve state-of-the-art performance on continual learning benchmarks.
IBM researchers present recent work on advancing, trusting, and scaling learning at the annual International Conference on Learning Representations (ICLR).
IBM Research and New York University are using AI to analyze retina imaging data and help to assess the presence of glaucoma.
An interactive career goal recommender framework that uses dialogue to incorporate user feedback and interactively improve the recommendations.
A forecasting method that is applicable to arbitrary sequences and comes with a regret bound competing against a class of methods, which includes Kalman filters.
The Algebraic Gradient-based Solver (AGS), a novel solver for approximate marginal MAP inference, shows how ideas from planning can be used for inference.
At the Thirty-Second Conference on Neural Information Processing Systems in Montreal, IBM Research AI will share new ideas and results across our portfolio of research aimed at progressing AI towards real-world challenges. Throughout the week, we will present dozens of papers and demos showcasing our work, as listed below. In addition, we will highlight three […]
In a recent paper “Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks,” we describe a general end-to-end Graph-to-Sequence attention-based neural encoder-decoder architecture that encodes an input graph and decodes the target sequence. Graph encoder and attention-based decoder are two important building blocks in the development and widespread acceptance of machine learning solutions. Two of […]