conferences

Unifying Continual Learning and Meta-Learning with Meta-Experience Replay

Meta-Experience Replay (MER) integrates meta-learning and experience replay to achieve state-of-the-art performance on continual learning benchmarks.

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Will Adam Algorithms Work for Me?

A simple and effective approach to monitor the convergence of Adam algorithms, a generic class of adaptive gradient methods for non-convex optimization.

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IBM Research AI Advancing, Trusting, and Scaling Learning at ICLR

IBM researchers present recent work on advancing, trusting, and scaling learning at the annual International Conference on Learning Representations (ICLR).

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IBM Research AI at CHI 2019

At the ACM CHI Conference on Human Factors in Computing Systems, IBM researchers present recent work in human-computer interaction in the context of AI.

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AI Holds Promise for Glaucoma, a Leading Global Cause of Blindness

IBM Research and New York University are using AI to analyze retina imaging data and help to assess the presence of glaucoma.

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IBM Research at the Intersection of HCI and AI

IBM research on explainable AI, human-computer interaction (HCI), and automated ML featured at this year's conference on Intelligent User Interfaces.

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Harnessing Dialogue for Interactive Career Goal Recommendations

An interactive career goal recommender framework that uses dialogue to incorporate user feedback and interactively improve the recommendations.

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IBM Research AI at AAAI 2019

IBM Research AI will present dozens of technical papers and demos at the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI) in Honolulu, Hawaii.

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On-Line Learning of Linear Dynamical Systems with Kalman Filters

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.

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Algebraic Gradient-Based Solver (AGS): A Novel Solver for Approximate Marginal MAP Inference

The Algebraic Gradient-based Solver (AGS), a novel solver for approximate marginal MAP inference, shows how ideas from planning can be used for inference.

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IBM Research AI at 2018 Conference on Neural Information Processing Systems

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 […]

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Graph2Seq: A Generalized Seq2Seq Model for Graph Inputs

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 […]

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