ibm

∙ Armonk , United States ∙ 0 followers

International Business Machines Corporation is an American multinational information technology company headquartered in Armonk, New York, with operations in over 170 countries.

Featured Researchers

Wei Liu
197 publications
Rui Zhang
149 publications
Wei Zhang
140 publications
Bo Zhang
96 publications
Xiaodan Liang
84 publications
Jie Chen
84 publications
Yu Cheng
74 publications
Pin-Yu Chen
68 publications
Yang Zhang
66 publications
Liang Zhao
63 publications
Chang Xu
59 publications

A Possible Reason for why Data-Driven Beats Theory-Driven Computer Vision

Why do some continue to wonder about the success and dominance of deep l...
08/28/2019 ∙ by John K. Tsotsos, et al. ∙ 82 ∙ share

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How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?

In recent years there has been an increasing trend in which data scienti...
09/08/2019 ∙ by Yaoli Mao, et al. ∙ 71 ∙ share

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Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos

Image-based sports analytics enable automatic retrieval of key events in...
02/19/2020 ∙ by Subhajit Chaudhury, et al. ∙ 57 ∙ share

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Map Generation from Large Scale Incomplete and Inaccurate Data Labels

Accurately and globally mapping human infrastructure is an important and...
05/20/2020 ∙ by Rui Zhang, et al. ∙ 57 ∙ share

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Deep Networks Incorporating Spiking Neural Dynamics

Neural networks have become the key technology of Artificial Intelligenc...
12/17/2018 ∙ by Stanisław Woźniak, et al. ∙ 48 ∙ share

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The Emerging Landscape of Explainable AI Planning and Decision Making

In this paper, we provide a comprehensive outline of the different threa...
02/26/2020 ∙ by Tathagata Chakraborti, et al. ∙ 47 ∙ share

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Embedding Compression with Isotropic Iterative Quantization

Continuous representation of words is a standard component in deep learn...
01/11/2020 ∙ by Siyu Liao, et al. ∙ 36 ∙ share

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A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control

Air traffic control is an example of a highly challenging operational pr...
04/03/2020 ∙ by Supriyo Ghosh, et al. ∙ 32 ∙ share

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Chart Auto-Encoders for Manifold Structured Data

Auto-encoding and generative models have made tremendous successes in im...
12/20/2019 ∙ by Stefan Schonsheck, et al. ∙ 31 ∙ share

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Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary Learning

Networks are fundamental building blocks for representing data, and comp...
11/14/2018 ∙ by Rise Ooi, et al. ∙ 28 ∙ share

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Continual Learning with Self-Organizing Maps

Despite remarkable successes achieved by modern neural networks in a wid...
04/19/2019 ∙ by Pouya Bashivan, et al. ∙ 26 ∙ share

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Temporal Tensor Transformation Network for Multivariate Time Series Prediction

Multivariate time series prediction has applications in a wide variety o...
01/04/2020 ∙ by Yuya Jeremy Ong, et al. ∙ 25 ∙ share

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Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent

Despite the great achievements of the modern deep neural networks (DNNs)...
02/18/2020 ∙ by Pu Zhao, et al. ∙ 25 ∙ share

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A Unified Conversational Assistant Framework for Business Process Automation

Business process automation is a booming multi-billion-dollar industry t...
01/07/2020 ∙ by Yara Rizk, et al. ∙ 23 ∙ share

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How do Data Science Workers Collaborate? Roles, Workflows, and Tools

Today, the prominence of data science within organizations has given ris...
01/18/2020 ∙ by Amy X. Zhang, et al. ∙ 23 ∙ share

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Visualizing and Understanding Generative Adversarial Networks (Extended Abstract)

Generative Adversarial Networks (GANs) have achieved impressive results ...
01/29/2019 ∙ by David Bau, et al. ∙ 22 ∙ share

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Semantic Photo Manipulation with a Generative Image Prior

Despite the recent success of GANs in synthesizing images conditioned on...
05/15/2020 ∙ by David Bau, et al. ∙ 21 ∙ share

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EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Graph representation learning resurges as a trending research subject ow...
02/26/2019 ∙ by Aldo Pareja, et al. ∙ 20 ∙ share

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D3BA: A Tool for Optimizing Business Processes Using Non-Deterministic Planning

This paper builds upon recent work in the declarative design of dialogue...
01/08/2020 ∙ by Tathagata Chakraborti, et al. ∙ 20 ∙ share

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Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries

This paper explores the task of interactive image retrieval using natura...
11/10/2019 ∙ by Fuwen Tan, et al. ∙ 19 ∙ share

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Planning with Explanatory Actions: A Joint Approach to Plan Explicability and Explanations in Human-Aware Planning

In this work, we formulate the process of generating explanations as mod...
03/18/2019 ∙ by Sarath Sreedharan, et al. ∙ 18 ∙ share

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Building medical image classifiers with very limited data using segmentation networks

Deep learning has shown promising results in medical image analysis, how...
08/15/2018 ∙ by Ken C. L. Wong, et al. ∙ 16 ∙ share

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Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration

Autonomous cyber-physical agents and systems play an increasingly large ...
09/21/2018 ∙ by Ritesh Noothigattu, et al. ∙ 16 ∙ share

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Recognizing Disguised Faces in the Wild

Research in face recognition has seen tremendous growth over the past co...
11/21/2018 ∙ by Maneet Singh, et al. ∙ 16 ∙ share

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Toward A Neuro-inspired Creative Decoder

Creativity, a process that generates novel and valuable ideas, involves ...
02/06/2019 ∙ by Payel Das, et al. ∙ 16 ∙ share

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Improving Efficiency in Large-Scale Decentralized Distributed Training

Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchr...
02/04/2020 ∙ by Wei Zhang, et al. ∙ 16 ∙ share

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Link Prediction using Graph Neural Networks for Master Data Management

Learning graph representations of n-ary relational data has a number of ...
03/07/2020 ∙ by Balaji Ganesan, et al. ∙ 16 ∙ share

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CPDist: Deep Siamese Networks for Learning Distances Between Structured Preferences

Preference are central to decision making by both machines and humans. R...
09/21/2018 ∙ by Andrea Loreggia, et al. ∙ 14 ∙ share

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Learning to Remember, Forget and Ignore using Attention Control in Memory

Typical neural networks with external memory do not effectively separate...
09/28/2018 ∙ by T. S. Jayram, et al. ∙ 14 ∙ share

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Neural network gradient-based learning of black-box function interfaces

Deep neural networks work well at approximating complicated functions wh...
01/13/2019 ∙ by Alon Jacovi, et al. ∙ 14 ∙ share

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Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models

The prediction accuracy has been the long-lasting and sole standard for ...
08/05/2018 ∙ by Dong Su, et al. ∙ 12 ∙ share

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Using Multi-task and Transfer Learning to Solve Working Memory Tasks

We propose a new architecture called Memory-Augmented Encoder-Solver (MA...
09/28/2018 ∙ by T. S. Jayram, et al. ∙ 12 ∙ share

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sZoom: A Framework for Automatic Zoom into High Resolution Surveillance Videos

Current cameras are capable of recording high resolution video. While vi...
09/23/2019 ∙ by Mukesh Saini, et al. ∙ 12 ∙ share

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Model Agnostic Contrastive Explanations for Structured Data

Recently, a method [7] was proposed to generate contrastive explanations...
05/31/2019 ∙ by Amit Dhurandhar, et al. ∙ 11 ∙ share

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SimVAE: Simulator-Assisted Training forInterpretable Generative Models

This paper presents a simulator-assisted training method (SimVAE) for va...
11/19/2019 ∙ by Akash Srivastava, et al. ∙ 11 ∙ share

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CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator

Deep neural networks (DNNs) are vulnerable to adversarial attack despite...
12/16/2019 ∙ by Huy Phan, et al. ∙ 11 ∙ share

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TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection

This work provides a simple approach to discover tight object bounding b...
07/13/2018 ∙ by Yunchao Wei, et al. ∙ 10 ∙ share

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Safe Exploration in Markov Decision Processes with Time-Variant Safety using Spatio-Temporal Gaussian Process

In many real-world applications (e.g., planetary exploration, robot navi...
09/12/2018 ∙ by Akifumi Wachi, et al. ∙ 10 ∙ share

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Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks

We introduce a new scene graph generation method called image-level atte...
11/09/2018 ∙ by Guillaume Jaume, et al. ∙ 10 ∙ share

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A deep learning approach to solar-irradiance forecasting in sky-videos

Ahead-of-time forecasting of incident solar-irradiance on a panel is ind...
01/15/2019 ∙ by Talha A. Siddiqui, et al. ∙ 10 ∙ share

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Learning Embeddings into Entropic Wasserstein Spaces

Euclidean embeddings of data are fundamentally limited in their ability ...
05/08/2019 ∙ by Charlie Frogner, et al. ∙ 10 ∙ share

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AutoGAN: Neural Architecture Search for Generative Adversarial Networks

Neural architecture search (NAS) has witnessed prevailing success in ima...
08/11/2019 ∙ by Xinyu Gong, et al. ∙ 10 ∙ share

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Bio-Inspired Hashing for Unsupervised Similarity Search

The fruit fly Drosophila's olfactory circuit has inspired a new locality...
01/14/2020 ∙ by Chaitanya K. Ryali, et al. ∙ 10 ∙ share

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PoWER-BERT: Accelerating BERT inference for Classification Tasks

BERT has emerged as a popular model for natural language understanding. ...
01/24/2020 ∙ by Saurabh Goyal, et al. ∙ 10 ∙ share

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Joint Optimization of AI Fairness and Utility: A Human-Centered Approach

Today, AI is increasingly being used in many high-stakes decision-making...
02/05/2020 ∙ by Yunfeng Zhang, et al. ∙ 10 ∙ share

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Learning and Recognizing Archeological Features from LiDAR Data

We present a remote sensing pipeline that processes LiDAR (Light Detecti...
04/05/2020 ∙ by Conrad M Albrecht, et al. ∙ 10 ∙ share

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Mirrored Autoencoders with Simplex Interpolation for Unsupervised Anomaly Detection

Use of deep generative models for unsupervised anomaly detection has sho...
03/24/2020 ∙ by Y. Wu, et al. ∙ 9 ∙ share

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Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach

We study the problem of attacking a machine learning model in the hard-l...
07/12/2018 ∙ by Minhao Cheng, et al. ∙ 8 ∙ share

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Improving Transferability of Deep Neural Networks

Learning from small amounts of labeled data is a challenge in the area o...
07/30/2018 ∙ by Parijat Dube, et al. ∙ 8 ∙ share

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Improving Natural Language Inference Using External Knowledge in the Science Questions Domain

Natural Language Inference (NLI) is fundamental to many Natural Language...
09/15/2018 ∙ by Xiaoyan Wang, et al. ∙ 8 ∙ share

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