Microsoft

∙ Redmond , United States ∙ 1 follower

Microsoft Corporation is a technology company. The Company develops, licenses, and supports a range of software products, services and devices. The Company's segments include Productivity and Business Processes, Intelligent Cloud and More Personal Computing. The Company's products include operating systems; cross-device productivity applications; server applications; business solution applications; desktop and server management tools; software development tools; video games, and training and certification of computer system integrators and developers. It also designs, manufactures, and sells devices, including personal computers (PCs), tablets, gaming and entertainment consoles, phones, other intelligent devices, and related accessories, that integrate with its cloud-based offerings. It offers an array of services, including cloud-based solutions that provide customers with software, services, platforms, and content, and it provides solution support and consulting services.

Featured Researchers

Yang Liu
320 publications
Wei Wang
235 publications
Wei Liu
216 publications
Lei Zhang
200 publications
Yu Zhang
184 publications
Wei Zhang
176 publications
Jun Wang
173 publications
Rui Wang
144 publications
Wei Chen
141 publications
Jianfeng Gao
133 publications
Wei Li
128 publications

Deep Learning in the Era of Edge Computing: Challenges and Opportunities

The era of edge computing has arrived. Although the Internet is the back...
10/17/2020 ∙ by Mi Zhang, et al. ∙ 71 ∙ share

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Natural Language QA Approaches using Reasoning with External Knowledge

Question answering (QA) in natural language (NL) has been an important a...
03/06/2020 ∙ by Chitta Baral, et al. ∙ 65 ∙ share

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Microscopic fine-grained instance classification through deep attention

Fine-grained classification of microscopic image data with limited sampl...
10/06/2020 ∙ by Mengran Fan, et al. ∙ 63 ∙ share

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FLO: Fast and Lightweight Hyperparameter Optimization for AutoML

Integrating ML models in software is of growing interest. Building accur...
11/12/2019 ∙ by Chi Wang, et al. ∙ 58 ∙ share

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Accelerating Physics-Informed Neural Network Training with Prior Dictionaries

Physics-Informed Neural Networks (PINNs) can be regarded as general-purp...
04/17/2020 ∙ by Wei Peng, et al. ∙ 49 ∙ share

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Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning

Current state-of-the-art object detectors can have significant performan...
11/17/2019 ∙ by Fuxun Yu, et al. ∙ 47 ∙ share

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On the Anomalous Generalization of GANs

Generative models, especially Generative Adversarial Networks (GANs), ha...
09/27/2019 ∙ by Jinchen Xuan, et al. ∙ 46 ∙ share

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360-Indoor: Towards Learning Real-World Objects in 360° Indoor Equirectangular Images

While there are several widely used object detection datasets, current c...
10/03/2019 ∙ by Shih-Han Chou, et al. ∙ 46 ∙ share

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Relationship-Aware Spatial Perception Fusion for Realistic Scene Layout Generation

The significant progress on Generative Adversarial Networks (GANs) have ...
09/02/2019 ∙ by Hongdong Zheng, et al. ∙ 44 ∙ share

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SQuINTing at VQA Models: Interrogating VQA Models with Sub-Questions

Existing VQA datasets contain questions with varying levels of complexit...
01/20/2020 ∙ by Ramprasaath R. Selvaraju, et al. ∙ 44 ∙ share

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GPT-GNN: Generative Pre-Training of Graph Neural Networks

Graph neural networks (GNNs) have been demonstrated to be powerful in mo...
06/27/2020 ∙ by Ziniu Hu, et al. ∙ 43 ∙ share

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D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge

Mobile edge learning is an emerging technique that enables distributed e...
01/28/2020 ∙ by Xiaoran Cai, et al. ∙ 42 ∙ share

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MixPath: A Unified Approach for One-shot Neural Architecture Search

The expressiveness of search space is a key concern in neural architectu...
01/16/2020 ∙ by Xiangxiang Chu, et al. ∙ 39 ∙ share

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Subspace approximation with outliers

The subspace approximation problem with outliers, for given n points in ...
06/30/2020 ∙ by Amit Deshpande, et al. ∙ 39 ∙ share

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Security and Machine Learning in the Real World

Machine learning (ML) models deployed in many safety- and business-criti...
07/13/2020 ∙ by Ivan Evtimov, et al. ∙ 38 ∙ share

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Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal

Minmax optimization, especially in its general nonconvex-nonconcave form...
02/02/2019 ∙ by Chi Jin, et al. ∙ 34 ∙ share

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Appending Adversarial Frames for Universal Video Attack

There have been many efforts in attacking image classification models wi...
12/10/2019 ∙ by Zhikai Chen, et al. ∙ 34 ∙ share

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DSRGAN: Explicitly Learning Disentangled Representation of Underlying Structure and Rendering for Image Generation without Tuple Supervision

We focus on explicitly learning disentangled representation for natural ...
09/30/2019 ∙ by Guang-Yuan Hao, et al. ∙ 33 ∙ share

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Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting

High-quality image inpainting requires filling missing regions in a dama...
04/16/2019 ∙ by Yanhong Zeng, et al. ∙ 32 ∙ share

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Learning Spatial Awareness to Improve Crowd Counting

The aim of crowd counting is to estimate the number of people in images ...
09/16/2019 ∙ by Zhi-Qi Cheng, et al. ∙ 32 ∙ share

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Instance-wise Depth and Motion Learning from Monocular Videos

We present an end-to-end joint training framework that explicitly models...
12/19/2019 ∙ by Seokju Lee, et al. ∙ 30 ∙ share

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Non-local Recurrent Neural Memory for Supervised Sequence Modeling

Typical methods for supervised sequence modeling are built upon the recu...
08/26/2019 ∙ by Canmiao Fu, et al. ∙ 29 ∙ share

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An Anatomy of Graph Neural Networks Going Deep via the Lens of Mutual Information: Exponential Decay vs. Full Preservation

Graph Convolutional Network (GCN) has attracted intensive interests rece...
10/10/2019 ∙ by Nezihe Merve Gürel, et al. ∙ 28 ∙ share

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Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

Domain generalization is the problem of machine learning when the traini...
02/13/2020 ∙ by Vikas K. Garg, et al. ∙ 26 ∙ share

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The Non-IID Data Quagmire of Decentralized Machine Learning

Many large-scale machine learning (ML) applications need to train ML mod...
10/01/2019 ∙ by Kevin Hsieh, et al. ∙ 25 ∙ share

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NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

This paper reviews the NTIRE 2020 challenge on real image denoising with...
05/08/2020 ∙ by Abdelrahman Abdelhamed, et al. ∙ 25 ∙ share

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Graph Policy Network for Transferable Active Learning on Graphs

Graph neural networks (GNNs) have been attracting increasing popularity ...
06/24/2020 ∙ by Shengding Hu, et al. ∙ 25 ∙ share

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Densely Semantically Aligned Person Re-Identification

We propose a densely semantically aligned person re-identification (re-I...
12/21/2018 ∙ by Zhizheng Zhang, et al. ∙ 24 ∙ share

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Logic Rules Powered Knowledge Graph Embedding

Large scale knowledge graph embedding has attracted much attention from ...
03/09/2019 ∙ by Pengwei Wang, et al. ∙ 24 ∙ share

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Learning Residual Flow as Dynamic Motion from Stereo Videos

We present a method for decomposing the 3D scene flow observed from a mo...
09/16/2019 ∙ by Seokju Lee, et al. ∙ 24 ∙ share

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Affect-based Intrinsic Rewards for Learning General Representations

Positive affect has been linked to increased interest, curiosity and sat...
12/01/2019 ∙ by Dean Zadok, et al. ∙ 24 ∙ share

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Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos

In this paper, we propose a spatial-temporal relational reasoning networ...
12/16/2019 ∙ by Congcong Zhu, et al. ∙ 24 ∙ share

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Animating Face using Disentangled Audio Representations

All previous methods for audio-driven talking head generation assume the...
10/02/2019 ∙ by Gaurav Mittal, et al. ∙ 23 ∙ share

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RNNbow: Visualizing Learning via Backpropagation Gradients in Recurrent Neural Networks

We present RNNbow, an interactive tool for visualizing the gradient flow...
07/29/2019 ∙ by Dylan Cashman, et al. ∙ 22 ∙ share

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Disentangling Interpretable Generative Parameters of Random and Real-World Graphs

While a wide range of interpretable generative procedures for graphs exi...
10/12/2019 ∙ by Niklas Stoehr, et al. ∙ 22 ∙ share

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AirSim Drone Racing Lab

Autonomous drone racing is a challenging research problem at the interse...
03/12/2020 ∙ by Ratnesh Madaan, et al. ∙ 22 ∙ share

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MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation

Cross view feature fusion is the key to address the occlusion problem in...
03/30/2020 ∙ by Rongchang Xie, et al. ∙ 22 ∙ share

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Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks

Sampling methods (e.g., node-wise, layer-wise, or subgraph) has become a...
06/24/2020 ∙ by Weilin Cong, et al. ∙ 22 ∙ share

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Learning Calibratable Policies using Programmatic Style-Consistency

We study the important and challenging problem of controllable generatio...
10/02/2019 ∙ by Eric Zhan, et al. ∙ 21 ∙ share

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Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation

Convolutional networks are not aware of an object's geometric variations...
10/07/2019 ∙ by Hang Gao, et al. ∙ 21 ∙ share

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STANCY: Stance Classification Based on Consistency Cues

Controversial claims are abundant in online media and discussion forums....
10/14/2019 ∙ by Kashyap Popat, et al. ∙ 21 ∙ share

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Resource-Efficient Neural Networks for Embedded Systems

While machine learning is traditionally a resource intensive task, embed...
01/07/2020 ∙ by Wolfgang Roth, et al. ∙ 21 ∙ share

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Towards information-rich, logical text generation with knowledge-enhanced neural models

Text generation system has made massive promising progress contributed b...
03/02/2020 ∙ by Hao Wang, et al. ∙ 21 ∙ share

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Explainable Deep Classification Models for Domain Generalization

Conventionally, AI models are thought to trade off explainability for lo...
03/13/2020 ∙ by Andrea Zunino, et al. ∙ 21 ∙ share

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Stochastic Gradient Descent Escapes Saddle Points Efficiently

This paper considers the perturbed stochastic gradient descent algorithm...
02/13/2019 ∙ by Chi Jin, et al. ∙ 20 ∙ share

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Stochastic Online Learning with Probabilistic Graph Feedback

We consider a problem of stochastic online learning with general probabi...
03/04/2019 ∙ by Shuai Li, et al. ∙ 20 ∙ share

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Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set

Recently, deep learning based 3D face reconstruction methods have shown ...
03/20/2019 ∙ by Yu Deng, et al. ∙ 20 ∙ share

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DBRec: Dual-Bridging Recommendation via Discovering Latent Groups

In recommender systems, the user-item interaction data is usually sparse...
09/27/2019 ∙ by Jingwei Ma, et al. ∙ 20 ∙ share

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Conditional Self-Attention for Query-based Summarization

Self-attention mechanisms have achieved great success on a variety of NL...
02/18/2020 ∙ by Yujia Xie, et al. ∙ 20 ∙ share

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Tightening Bounds for Variational Inference by Revisiting Perturbation Theory

Variational inference has become one of the most widely used methods in ...
09/30/2019 ∙ by Robert Bamler, et al. ∙ 19 ∙ share

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