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05/31/2023
Feature Learning in Image Hierarchies using Functional Maximal Correlation
This paper proposes the Hierarchical Functional Maximal Correlation Algo...
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05/26/2023
Attention Paper: How Generative AI Reshapes Digital Shadow Industry?
The rapid development of digital economy has led to the emergence of var...
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12/09/2022
The Cross Density Kernel Function: A Novel Framework to Quantify Statistical Dependence for Random Processes
This paper proposes a novel multivariate definition of statistical depen...
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11/29/2022
Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning
Deep metric learning aims to learn an embedding space, where semanticall...
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11/22/2022
One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
Cross-domain recommendation is an important method to improve recommende...
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11/19/2022
Can Gradient Descent Provably Learn Linear Dynamic Systems?
We study the learning ability of linear recurrent neural networks with g...
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10/13/2022
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Existing benchmark datasets for recommender systems (RS) either are crea...
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08/26/2022
Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model
Deep learning is usually data starved, and the unsupervised domain adapt...
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07/13/2022
Entry-Flipped Transformer for Inference and Prediction of Participant Behavior
Some group activities, such as team sports and choreographed dances, inv...
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06/13/2022
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
Learning big models and then transfer has become the de facto practice i...
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10/22/2021
CTP-Net For Cross-Domain Trajectory Prediction
Deep learning based trajectory prediction methods rely on large amount o...
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10/12/2021
Information Theoretic Structured Generative Modeling
Rényi's information provides a theoretical foundation for tractable and ...
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09/29/2021
On the Provable Generalization of Recurrent Neural Networks
Recurrent Neural Network (RNN) is a fundamental structure in deep learni...
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07/22/2021
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference
In this work, we propose a domain generalization (DG) approach to learn ...
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06/17/2021
Optical Mouse: 3D Mouse Pose From Single-View Video
We present a method to infer the 3D pose of mice, including the limbs an...
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02/19/2021
Regularized Recovery by Multi-order Partial Hypergraph Total Variation
Capturing complex high-order interactions among data is an important tas...
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02/17/2021
Performance Analysis for Correlated AoI and Energy Efficiency in Heterogeneous CR-IoT System
We consider a cognitive radio based Internet of Things (CR-IoT) system w...
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01/15/2021
A Risk-Sensitive Task Offloading Strategy for Edge Computing in Industrial Internet of Things
Edge computing has become one of the key enablers for ultra-reliable and...
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01/01/2021
Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
Recent advances in unsupervised domain adaptation (UDA) show that transf...
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01/01/2021
Energy-constrained Self-training for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a...
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12/05/2020
Two-Timescale Resource Allocation for Cooperative D2D Communication: A Matching Game Approach
In this paper, we consider a cooperative device-todevice (D2D) communica...
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09/07/2019
Bayesian Design of Sampling Set for Bandlimited Graph Signals
The design of sampling set (DoS) for bandlimited graph signals (GS) has ...
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07/18/2019
Locality-constrained Spatial Transformer Network for Video Crowd Counting
Compared with single image based crowd counting, video provides the spat...
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05/14/2019
Learning for Matching Game in Cooperative D2D Communication with Incomplete Information
This paper considers a cooperative device-to-device (D2D) communication ...
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03/01/2019
Progress Regression RNN for Online Spatial-Temporal Action Localization in Unconstrained Videos
Previous spatial-temporal action localization methods commonly follow th...
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11/02/2018
Cooperative Spectrum Sharing Between D2D Users and Edge-Users: A Matching Theory Perspective
The device-to-device (D2D) communication theoretically provides both the...
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11/01/2018
A Cascaded Channel-Power Allocation for D2D Underlaid Cellular Networks Using Matching Theory
We consider a device-to-device (D2D) underlaid cellular network, where e...
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10/31/2018
Matching Game Based Framework for Two-Timescale Cooperative D2D Communication
We consider a cooperative device-to-device (D2D) communication system, w...
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09/18/2018
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
LinkedIn Talent Solutions business contributes to around 65 annual reven...
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09/17/2018
Towards Deep and Representation Learning for Talent Search at LinkedIn
Talent search and recommendation systems at LinkedIn strive to match the...
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06/06/2018
Deploying Deep Ranking Models for Search Verticals
In this paper, we present an architecture executing a complex machine le...
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11/30/2017