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research

∙ 12/18/2020

When Machine Learning Meets Quantum Computers: A Case Study

Along with the development of AI democratization, the machine learning a...

Weiwen Jiang, et al. ∙

research

∙ 08/28/2019

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...

John K. Tsotsos, et al. ∙

research

∙ 09/08/2019

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...

Yaoli Mao, et al. ∙

research

∙ 02/19/2020

Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos

Image-based sports analytics enable automatic retrieval of key events in...

Subhajit Chaudhury, et al. ∙

research

∙ 05/20/2020

Map Generation from Large Scale Incomplete and Inaccurate Data Labels

Accurately and globally mapping human infrastructure is an important and...

Rui Zhang, et al. ∙

research

∙ 09/16/2021

Efficient Scaling of Dynamic Graph Neural Networks

We present distributed algorithms for training dynamic Graph Neural Netw...

Venkatesan T. Chakaravarthy, et al. ∙

research

∙ 12/17/2018

Deep Networks Incorporating Spiking Neural Dynamics

Neural networks have become the key technology of Artificial Intelligenc...

Stanisław Woźniak, et al. ∙

research

∙ 02/26/2020

The Emerging Landscape of Explainable AI Planning and Decision Making

In this paper, we provide a comprehensive outline of the different threa...

Tathagata Chakraborti, et al. ∙

research

∙ 03/04/2021

Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture

Graph Convolutional Networks (GCNs) are increasingly adopted in large-sc...

Seung Won Min, et al. ∙

research

∙ 05/17/2021

Vision Transformers are Robust Learners

Transformers, composed of multiple self-attention layers, hold strong pr...

Sayak Paul, et al. ∙

research

∙ 10/20/2021

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

As a technical sub-field of artificial intelligence (AI), explainable AI...

Q Vera Liao, et al. ∙

research

∙ 01/11/2020

Embedding Compression with Isotropic Iterative Quantization

Continuous representation of words is a standard component in deep learn...

Siyu Liao, et al. ∙

research

∙ 04/03/2020

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

Air traffic control is an example of a highly challenging operational pr...

Supriyo Ghosh, et al. ∙

research

∙ 12/20/2019

Chart Auto-Encoders for Manifold Structured Data

Auto-encoding and generative models have made tremendous successes in im...

Stefan Schonsheck, et al. ∙

research

∙ 12/12/2020

PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data

An automated machine learning framework for geospatial data named PAIRS ...

Wang Zhou, et al. ∙

research

∙ 08/03/2021

The Devil is in the GAN: Defending Deep Generative Models Against Backdoor Attacks

Deep Generative Models (DGMs) allow users to synthesize data from comple...

Ambrish Rawat, et al. ∙

research

∙ 11/14/2018

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

Networks are fundamental building blocks for representing data, and comp...

Rise Ooi, et al. ∙

research

∙ 07/21/2021

HistoCartography: A Toolkit for Graph Analytics in Digital Pathology

Advances in entity-graph based analysis of histopathology images have br...

Guillaume Jaume, et al. ∙

research

∙ 10/12/2021

Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks

The lottery ticket hypothesis (LTH) states that learning on a properly p...

Shuai Zhang, et al. ∙

research

∙ 01/12/2021

Expanding Explainability: Towards Social Transparency in AI systems

As AI-powered systems increasingly mediate consequential decision-making...

Upol Ehsan, et al. ∙

research

∙ 08/27/2021

ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models

Automatic construction of relevant Knowledge Bases (KBs) from text, and ...

Pierre L. Dognin, et al. ∙

research

∙ 04/19/2019

Continual Learning with Self-Organizing Maps

Despite remarkable successes achieved by modern neural networks in a wid...

Pouya Bashivan, et al. ∙

research

∙ 06/23/2020

A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm

Out of the recent advances in systems and control (S&C)-based analysis o...

Orlando Romero, et al. ∙

research

∙ 01/04/2020

Temporal Tensor Transformation Network for Multivariate Time Series Prediction

Multivariate time series prediction has applications in a wide variety o...

Yuya Jeremy Ong, et al. ∙

research

∙ 02/18/2020

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

Despite the great achievements of the modern deep neural networks (DNNs)...

Pu Zhao, et al. ∙

research

∙ 01/18/2020

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

Today, the prominence of data science within organizations has given ris...

Amy X. Zhang, et al. ∙

research

∙ 01/07/2020

A Unified Conversational Assistant Framework for Business Process Automation

Business process automation is a booming multi-billion-dollar industry t...

Yara Rizk, et al. ∙

research

∙ 08/07/2021

Jointly Attacking Graph Neural Network and its Explanations

Graph Neural Networks (GNNs) have boosted the performance for many graph...

Wenqi Fan, et al. ∙

research

∙ 01/29/2019

Visualizing and Understanding Generative Adversarial Networks (Extended Abstract)

Generative Adversarial Networks (GANs) have achieved impressive results ...

David Bau, et al. ∙

research

∙ 07/02/2020

Verifiably Safe Exploration for End-to-End Reinforcement Learning

Deploying deep reinforcement learning in safety-critical settings requir...

Nathan Hunt, et al. ∙

research

∙ 05/15/2020

Semantic Photo Manipulation with a Generative Image Prior

Despite the recent success of GANs in synthesizing images conditioned on...

David Bau, et al. ∙

research

∙ 07/02/2020

Designing Environments Conducive to Interpretable Robot Behavior

Designing robots capable of generating interpretable behavior is a prere...

Anagha Kulkarni, et al. ∙

research

∙ 07/27/2020

Chest X-ray Report Generation through Fine-Grained Label Learning

Obtaining automated preliminary read reports for common exams such as ch...

Tanveer Syeda-Mahmood, et al. ∙

research

∙ 01/18/2021

Discrete Graph Structure Learning for Forecasting Multiple Time Series

Time series forecasting is an extensively studied subject in statistics,...

Chao Shang, et al. ∙

research

∙ 02/26/2019

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Graph representation learning resurges as a trending research subject ow...

Aldo Pareja, et al. ∙

research

∙ 01/08/2020

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

This paper builds upon recent work in the declarative design of dialogue...

Tathagata Chakraborti, et al. ∙

research

∙ 05/29/2020

Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study

Pulmonary Embolism (PE) is a life-threatening disorder associated with h...

Luyao Shi, et al. ∙

research

∙ 10/26/2021

Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks

Deep Neural Networks (DNNs) are known to be vulnerable to adversarial at...

Yonggan Fu, et al. ∙

research

∙ 11/10/2019

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

This paper explores the task of interactive image retrieval using natura...

Fuwen Tan, et al. ∙

research

∙ 06/09/2020

Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement

Disentangled representation learning has recently attracted a significan...

Xiaojie Guo, et al. ∙

research

∙ 07/24/2020

Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning

In real world applications like healthcare, it is usually difficult to b...

Yiqin Yu, et al. ∙

research

∙ 07/28/2021

The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations

Explainability of AI systems is critical for users to take informed acti...

Upol Ehsan, et al. ∙

research

∙ 03/18/2019

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...

Sarath Sreedharan, et al. ∙

research

∙ 06/16/2020

A Study of Compositional Generalization in Neural Models

Compositional and relational learning is a hallmark of human intelligenc...

Tim Klinger, et al. ∙

research

∙ 10/15/2021

LPRules: Rule Induction in Knowledge Graphs Using Linear Programming

Knowledge graph (KG) completion is a well-studied problem in AI. Rule-ba...

Sanjeeb Dash, et al. ∙

research

∙ 03/26/2022

Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension

Question answering (QA) is a fundamental means to facilitate assessment ...

Ying Xu, et al. ∙

research

∙ 06/01/2022

Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness

Document understanding is a key business process in the data-driven econ...

Christoph Auer, et al. ∙

research

∙ 08/15/2018

Building medical image classifiers with very limited data using segmentation networks

Deep learning has shown promising results in medical image analysis, how...

Ken C. L. Wong, et al. ∙

research

∙ 09/21/2018

Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration

Autonomous cyber-physical agents and systems play an increasingly large ...

Ritesh Noothigattu, et al. ∙

research

∙ 11/21/2018

Recognizing Disguised Faces in the Wild

Research in face recognition has seen tremendous growth over the past co...

Maneet Singh, et al. ∙

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