Media Summary: Xiaomin Chang, University of Sydney This video briefly introduces our research work on multi-source learning for energy ... Shimin Di, HKUST MPNNs have demonstrated great success in graph representation learning. MPNNs generally rely on ... Mohannad Elhamod, Virginia Tech "Can a specimen image be expressed as a DNA-like sequence?". In this video, we present a ...

Kdd 2023 Taming The Domain - Detailed Analysis & Overview

Xiaomin Chang, University of Sydney This video briefly introduces our research work on multi-source learning for energy ... Shimin Di, HKUST MPNNs have demonstrated great success in graph representation learning. MPNNs generally rely on ... Mohannad Elhamod, Virginia Tech "Can a specimen image be expressed as a DNA-like sequence?". In this video, we present a ... Shibal Ibrahim, Massachusetts Institute of Technology Sparse Mixture-of-Experts (Sparse-MoE) framework efficiently scales up ... Liyao Jiang, University of Alberta Online ads are important in e-commerce sites, social media platforms, and search engines. Mohamed Ragab, Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR)), Emadeldeen ...

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KDD 2023 - Taming the Domain Shift in Multi-source Learning for Energy Disaggregation
KDD 2023 - A Framework for Learning Item-Features to Make a Domain
KDD 2023 - Domain-Specific Risk Minimization for Domain Generalization
KDD 2023 - A Message Passing Neural NetworK for Better Capturing Data-dependent Receptive Fields
KDD 2023 - Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks
KDD 2023 - Learning Cardinality Constrained Mixture of Experts with Trees and Local Search
KDD 2023 - A Look into Causal Effects under Entangled Treatment in Graphs
KDD 2023 - How to robustly detect failures with 3 types of telemetry data?
KDD 2023 - Quantitavely Measuring&Contrastively Exploring Heterogeneity for Domain Generalization
KDD 2023 - AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness
KDD 2023 - Graph Attention Mean Field for Very Large Scale Multi-Agent Reinforcement Learning
KDD 2023 - Source-Free Domain Adaptation with Temporal Imputation for Time Series Data
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KDD 2023 - Taming the Domain Shift in Multi-source Learning for Energy Disaggregation

KDD 2023 - Taming the Domain Shift in Multi-source Learning for Energy Disaggregation

Xiaomin Chang, University of Sydney This video briefly introduces our research work on multi-source learning for energy ...

KDD 2023 - A Framework for Learning Item-Features to Make a Domain

KDD 2023 - A Framework for Learning Item-Features to Make a Domain

Taeho Kim, Hanyang University.

KDD 2023 - Domain-Specific Risk Minimization for Domain Generalization

KDD 2023 - Domain-Specific Risk Minimization for Domain Generalization

Yi-fan Zhang, Institute of Automation.

KDD 2023 - A Message Passing Neural NetworK for Better Capturing Data-dependent Receptive Fields

KDD 2023 - A Message Passing Neural NetworK for Better Capturing Data-dependent Receptive Fields

Shimin Di, HKUST MPNNs have demonstrated great success in graph representation learning. MPNNs generally rely on ...

KDD 2023 - Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks

KDD 2023 - Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks

Mohannad Elhamod, Virginia Tech "Can a specimen image be expressed as a DNA-like sequence?". In this video, we present a ...

KDD 2023 - Learning Cardinality Constrained Mixture of Experts with Trees and Local Search

KDD 2023 - Learning Cardinality Constrained Mixture of Experts with Trees and Local Search

Shibal Ibrahim, Massachusetts Institute of Technology Sparse Mixture-of-Experts (Sparse-MoE) framework efficiently scales up ...

KDD 2023 - A Look into Causal Effects under Entangled Treatment in Graphs

KDD 2023 - A Look into Causal Effects under Entangled Treatment in Graphs

Jing Ma, University of Virginia.

KDD 2023 - How to robustly detect failures with 3 types of telemetry data?

KDD 2023 - How to robustly detect failures with 3 types of telemetry data?

Chenyu Zhao, Nankai University.

KDD 2023 - Quantitavely Measuring&Contrastively Exploring Heterogeneity for Domain Generalization

KDD 2023 - Quantitavely Measuring&Contrastively Exploring Heterogeneity for Domain Generalization

Yunze Tong, Zhejiang University

KDD 2023 - AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness

KDD 2023 - AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness

Liyao Jiang, University of Alberta Online ads are important in e-commerce sites, social media platforms, and search engines.

KDD 2023 - Graph Attention Mean Field for Very Large Scale Multi-Agent Reinforcement Learning

KDD 2023 - Graph Attention Mean Field for Very Large Scale Multi-Agent Reinforcement Learning

Qianyue Hao, Tsinghua University.

KDD 2023 - Source-Free Domain Adaptation with Temporal Imputation for Time Series Data

KDD 2023 - Source-Free Domain Adaptation with Temporal Imputation for Time Series Data

Mohamed Ragab, Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR)), Emadeldeen ...

KDD 2023 - CCTL: A Collaborative Transfer Learning Framework for Cross-domain Recommendation

KDD 2023 - CCTL: A Collaborative Transfer Learning Framework for Cross-domain Recommendation

Wei Zhang, Meituan.