Jiaxiang Wu

Orcid: 0000-0001-9132-5625

Affiliations:
  • Tencent AI Lab, Shenzhen, China
  • Chinese Academy of Sciences, Institute of Automation, Beijing, China (PhD 2017)


According to our database1, Jiaxiang Wu authored at least 52 papers between 2013 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Online presence:

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Bibliography

2024
Multi-Scale Attention Flow for Probabilistic Time Series Forecasting.
IEEE Trans. Knowl. Data Eng., May, 2024

Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting.
CoRR, 2024

2023
RPTQ: Reorder-based Post-training Quantization for Large Language Models.
CoRR, 2023

Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance.
CoRR, 2023

Anime Character Identification and Tag Prediction by Multimodality Modeling: Dataset and Model.
Proceedings of the International Joint Conference on Neural Networks, 2023

Towards Stable Test-time Adaptation in Dynamic Wild World.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Privacy-Preserving Face Recognition Using Random Frequency Components.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery - a Focus on Affinity Prediction Problems with Noise Annotations.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Deep Network Quantization via Error Compensation.
IEEE Trans. Neural Networks Learn. Syst., 2022

Boost Test-Time Performance with Closed-Loop Inference.
CoRR, 2022

DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery - A Focus on Affinity Prediction Problems with Noise Annotations.
CoRR, 2022

Efficient Test-Time Model Adaptation without Forgetting.
Proceedings of the International Conference on Machine Learning, 2022

Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Self-Supervised Pre-training for Protein Embeddings Using Tertiary Structures.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

GNN-Retro: Retrosynthetic Planning with Graph Neural Networks.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Disturbance-immune weight sharing for neural architecture search.
Neural Networks, 2021

EPTool: A New Enhancing PSSM Tool for Protein Secondary Structure Prediction.
J. Comput. Biol., 2021

Comprehensive Study on Enhancing Low-Quality Position-Specific Scoring Matrix with Deep Learning for Accurate Protein Structure Property Prediction: Using Bagging Multiple Sequence Alignment Learning.
J. Comput. Biol., 2021

Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation.
Int. J. Comput. Vis., 2021

Energy-Based Learning for Cooperative Games, with Applications to Feature/Data/Model Valuations.
CoRR, 2021

EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models.
CoRR, 2021

tFold-TR: Combining Deep Learning Enhanced Hybrid Potential Energy for Template-Based Modelling Structure Refinement.
CoRR, 2021

Federated Face Recognition.
CoRR, 2021

AdaXpert: Adapting Neural Architecture for Growing Data.
Proceedings of the 38th International Conference on Machine Learning, 2021

PSSM-Distil: Protein Secondary Structure Prediction (PSSP) on Low-Quality PSSM by Knowledge Distillation with Contrastive Learning.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Bayesian Automatic Model Compression.
IEEE J. Sel. Top. Signal Process., 2020

Bagging MSA Learning: Enhancing Low-Quality PSSM with Deep Learning for Accurate Protein Structure Property Prediction.
Proceedings of the Research in Computational Molecular Biology, 2020

Revisiting Parameter Sharing for Automatic Neural Channel Number Search.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

WeightAln: Weighted Homologous Alignment for Protein Structure Property Prediction.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020

Protein Ensemble Learning with Atrous Spatial Pyramid Networks for Secondary Structure Prediction.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020

M-NAS: Meta Neural Architecture Search.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Few Shot Network Compression via Cross Distillation.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Double Quantization for Communication-Efficient Distributed Optimization.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Collaborative Channel Pruning for Deep Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

An Efficient Approach to Informative Feature Extraction from Multimodal Data.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks.
IEEE Trans. Neural Networks Learn. Syst., 2018

Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization.
Proceedings of the 35th International Conference on Machine Learning, 2018

Core-set based product quantization for large-scale multimedia search.
Proceedings of the 10th International Conference on Internet Multimedia Computing and Service, 2018

2017
Pseudo Label based Unsupervised Deep Discriminative Hashing for Image Retrieval.
Proceedings of the 2017 ACM on Multimedia Conference, 2017

Fast K-means for Large Scale Clustering.
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

Bayesian Co-Boosting for Multi-modal Gesture Recognition.
Proceedings of the Gesture Recognition, 2017

2016
Quantized Convolutional Neural Networks for Mobile Devices.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

Shoot to Know What: An Application of Deep Networks on Mobile Devices.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Learning Deep Features For MSR-bing Information Retrieval Challenge.
Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, MM '15, Brisbane, Australia, October 26, 2015

Hashing for Distributed Data.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Online sketching hashing.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014
Bayesian co-boosting for multi-modal gesture recognition.
J. Mach. Learn. Res., 2014

Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction.
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

Supervised Hashing with Soft Constraints.
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014

2013
Fusing multi-modal features for gesture recognition.
Proceedings of the 2013 International Conference on Multimodal Interaction, 2013


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