Publications

Articles about IBM Research scientists' and engineers' technical publications

In-Memory Computing Using Photonic Memory Devices

IBM researchers discover that in-memory computing on an integrated photonic chip has the ability to further transform the computing landscape.

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Certifying Attack Resistance of Convolutional Neural Networks

Researchers from MIT and IBM propose an efficient and effective method for certifying attack resistance of convolutional neural networks to given input data.

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Efficient Adversarial Robustness Evaluation of AI Models with Limited Access

IBM researchers present AutoZOOM, an efficient and practical tool for evaluating adversarial robustness of AI models with limited access.

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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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Building Ethically Aligned AI

IBM Research has studied and assessed two possible ways to solve AI's "value alignment" problem and build ethically aligned AI systems.

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NeuNetS: Automating Neural Network Model Synthesis for Broader Adoption of AI

On December 14, 2018, IBM released NeuNetS, a fundamentally new capability that addresses the skills gap for the development of latest AI models for a wide range of business domains. NeuNetS uses AI to automatically synthesize deep neural network models faster and easier than ever before, scaling up the adoption of AI by companies and […]

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AI Year in Review: Highlights of Papers and Predictions from IBM Research AI

For more than seventy years, IBM Research has been inventing, exploring, and imagining the future. We have been pioneering the field of artificial intelligence (AI) since its inception. We were there when the field was launched at the famous 1956 Dartmouth workshop. Just three years later, an IBMer and early computer pioneer, Arthur Samuel, coined […]

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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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Word Mover’s Embedding: Universal Text Embedding from Word2Vec

Text representation plays an important role in many natural language processing (NLP) tasks such as document classification and clustering, sense disambiguation, machine translation, and document matching. Since there are no explicit features in text, developing effective text representations is an important goal in AI and NLP research. A fundamental challenge in this respect is learning […]

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Scientists Prove a Quantum Computing Advantage over Classical

Gaining a Quantum Advantage Scientists Sergey Bravyi of IBM Research, David Gosset of the University of Waterloo’s Institute for Quantum Computing, and Robert König of the Institute for Advanced Study and Zentrum Mathematik, Technische Universität München, have published in Science as “Quantum advantage with shallow circuits.” Quantum computing is getting a significant amount of attention […]

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Steering Material Scientists to Better Memory Devices

IBM researchers propose guidelines for novel analog memory devices to enable fast, energy-efficient and accurate AI hardware accelerators.

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Graphene Puts Nanomaterials In Their Place

Nanomaterials offer unique optical and electrical properties and bottom-up integration within industrial semiconductor manufacturing processes. However, they also present one of the most challenging research problems. In essence, semiconductor manufacturing today lacks methods for depositing nanomaterials at predefined chip locations without chemical contamination. We think that graphene, one of the thinnest, strongest, most flexible and most […]

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