Kaidi Cao

According to our database1, Kaidi Cao authored at least 28 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2024
PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning.
CoRR, 2024

2023
GraphMETRO: Mitigating Complex Distribution Shifts in GNNs via Mixture of Aligned Experts.
CoRR, 2023

TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs.
CoRR, 2023

Communication-Free Distributed GNN Training with Vertex Cut.
CoRR, 2023

TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Large Graph Property Prediction via Graph Segment Training.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

AutoTransfer: AutoML with Knowledge Transfer - An Application to Graph Neural Networks.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
TuneUp: A Training Strategy for Improving Generalization of Graph Neural Networks.
CoRR, 2022

Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Learning Backward Compatible Embeddings.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Relational Multi-Task Learning: Modeling Relations between Data and Tasks.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Open-World Semi-Supervised Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization.
Proceedings of the 9th International Conference on Learning Representations, 2021

Concept Learners for Few-Shot Learning.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Coresets for Robust Training of Neural Networks against Noisy Labels.
CoRR, 2020

Concept Learners for Generalizable Few-Shot Learning.
CoRR, 2020

Coresets for Robust Training of Deep Neural Networks against Noisy Labels.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Few-Shot Video Classification via Temporal Alignment.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators.
Proceedings of the ASPLOS '20: Architectural Support for Programming Languages and Operating Systems, 2020

2019
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Disentangling Content and Style via Unsupervised Geometry Distillation.
Proceedings of the Deep Generative Models for Highly Structured Data, 2019

Delving Deep Into Hybrid Annotations for 3D Human Recovery in the Wild.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Learning Temporal Action Proposals With Fewer Labels.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

TransGaGa: Geometry-Aware Unsupervised Image-To-Image Translation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
CariGANs: unpaired photo-to-caricature translation.
ACM Trans. Graph., 2018

DNN Dataflow Choice Is Overrated.
CoRR, 2018

Pose-Robust Face Recognition via Deep Residual Equivariant Mapping.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Merge or Not? Learning to Group Faces via Imitation Learning.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018


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