Yang You

Orcid: 0000-0003-2816-4384

Affiliations:

National University of Singapore
UC Berkeley, USA (PhD 2020)

According to our database¹, Yang You authored at least 110 papers between 2013 and 2024.

Collaborative distances:

Dijkstra number² of three.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

Self-filling evidential clustering for partial multi-view data.

[BibT_eX]

[DOI]

Chaoyu Gong

Yang You

Expert Syst. Appl., March, 2024

Sparse Reconstructive Evidential Clustering for Multi-View Data.

[BibT_eX]

[DOI]

Chaoyu Gong

Yang You

IEEE CAA J. Autom. Sinica, February, 2024

DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers.

[BibT_eX]

[DOI]

CoRR, 2024

HeteGen: Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices.

[BibT_eX]

[DOI]

CoRR, 2024

Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization.

[BibT_eX]

[DOI]

CoRR, 2024

Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning.

[BibT_eX]

[DOI]

CoRR, 2024

RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents.

[BibT_eX]

[DOI]

CoRR, 2024

OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models.

[BibT_eX]

[DOI]

CoRR, 2024

AutoChunk: Automated Activation Chunk for Memory-Efficient Long Sequence Inference.

[BibT_eX]

[DOI]

CoRR, 2024

FastFold: Optimizing AlphaFold Training and Inference on GPU Clusters.

[BibT_eX]

[DOI]

Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, 2024

2023

Adaptive evidential <i>K</i>-NN classification: Integrating neighborhood search and feature weighting.

[BibT_eX]

[DOI]

Inf. Sci., November, 2023

A Sparse Reconstructive Evidential K-Nearest Neighbor Classifier for High-Dimensional Data.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., June, 2023

Multitask Learning for Visual Question Answering.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., March, 2023

Parallel Training of Pre-Trained Models via Chunk-Based Dynamic Memory Management.

[BibT_eX]

[DOI]

IEEE Trans. Parallel Distributed Syst., 2023

Efficient Dataset Distillation via Minimax Diffusion.

[BibT_eX]

[DOI]

Jianyang Gu

Saeed Vahidian

Vyacheslav Kungurtsev

CoRR, 2023

DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching.

[BibT_eX]

[DOI]

CoRR, 2023

LoBaSS: Gauging Learnability in Supervised Fine-tuning Data.

[BibT_eX]

[DOI]

CoRR, 2023

Let's reward step by step: Step-Level reward model as the Navigators for Reasoning.

[BibT_eX]

[DOI]

CoRR, 2023

Can pre-trained models assist in dataset distillation?

[BibT_eX]

[DOI]

CoRR, 2023

Color Prompting for Data-Free Continual Unsupervised Domain Adaptive Person Re-Identification.

[BibT_eX]

[DOI]

CoRR, 2023

Dataset Quantization.

[BibT_eX]

[DOI]

CoRR, 2023

Learning Referring Video Object Segmentation from Weak Annotation.

[BibT_eX]

[DOI]

CoRR, 2023

Summarizing Stream Data for Memory-Restricted Online Continual Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline.

[BibT_eX]

[DOI]

CoRR, 2023

Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models.

[BibT_eX]

[DOI]

CoRR, 2023

InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning.

[BibT_eX]

[DOI]

CoRR, 2023

DiM: Distilling Dataset into Generative Model.

[BibT_eX]

[DOI]

CoRR, 2023

DREAM: Efficient Dataset Distillation by Representative Matching.

[BibT_eX]

[DOI]

CoRR, 2023

Colossal-Auto: Unified Automation of Parallelization and Activation Checkpoint for Large-scale Models.

[BibT_eX]

[DOI]

CoRR, 2023

ATP: Adaptive Tensor Parallelism for Foundation Models.

[BibT_eX]

[DOI]

CoRR, 2023

Hanayo: Harnessing Wave-like Pipeline Parallelism for Enhanced Large Model Training Efficiency.

[BibT_eX]

[DOI]

Proceedings of the International Conference for High Performance Computing, 2023

To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

An Efficient 2D Method for Training Super-Large Deep Learning Models.

[BibT_eX]

[DOI]

Qifan Xu

Yang You

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2023

Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training.

[BibT_eX]

[DOI]

Proceedings of the 52nd International Conference on Parallel Processing, 2023

Adaptive Computation with Elastic Input Sequence.

[BibT_eX]

[DOI]

Fuzhao Xue

Valerii Likhosherstov

Proceedings of the International Conference on Machine Learning, 2023

A Study on Transformer Configuration and Training Objective.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

One Student Knows All Experts Know: From Sparse to Dense.

[BibT_eX]

[DOI]

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention.

[BibT_eX]

[DOI]

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

CAME: Confidence-guided Adaptive Memory Efficient Optimization.

[BibT_eX]

[DOI]

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

Sequence Parallelism: Long Sequence Training from System Perspective.

[BibT_eX]

[DOI]

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

CowClip: Reducing CTR Prediction Model Training Time from 12 Hours to 10 Minutes on 1 GPU.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Weakly Supervised Learning for Textbook Question Answering.

[BibT_eX]

[DOI]

IEEE Trans. Image Process., 2022

Distributed evidential clustering toward time series with big data issue.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2022

Elixir: Train a Large Language Model on a Small GPU Cluster.

[BibT_eX]

[DOI]

CoRR, 2022

EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models.

[BibT_eX]

[DOI]

CoRR, 2022

Prompt Vision Transformer for Domain Generalization.

[BibT_eX]

[DOI]

CoRR, 2022

A Frequency-aware Software Cache for Large Recommendation System Embeddings.

[BibT_eX]

[DOI]

CoRR, 2022

FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders.

[BibT_eX]

[DOI]

CoRR, 2022

Deeper vs Wider: A Revisit of Transformer Configuration.

[BibT_eX]

[DOI]

CoRR, 2022

Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels.

[BibT_eX]

[DOI]

CoRR, 2022

CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU.

[BibT_eX]

[DOI]

CoRR, 2022

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours.

[BibT_eX]

[DOI]

CoRR, 2022

Sky Computing: Accelerating Geo-distributed Computing in Federated Learning.

[BibT_eX]

[DOI]

Jie Zhu

Shenggui Li

Yang You

CoRR, 2022

Crafting Better Contrastive Views for Siamese Representation Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Random Sharpness-Aware Minimization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Handling heavy-tailed input of transformer inference on GPUs.

[BibT_eX]

[DOI]

Proceedings of the ICS '22: 2022 International Conference on Supercomputing, Virtual Event, June 28, 2022

Tesseract: Parallelize the Tensor Parallelism Efficiently.

[BibT_eX]

[DOI]

Proceedings of the 51st International Conference on Parallel Processing, 2022

Concurrent Adversarial Learning for Large-Batch Training.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Joint Evidential $K$-Nearest Neighbor Classification.

[BibT_eX]

[DOI]

Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

Self-reconstructive evidential clustering for high-dimensional data.

[BibT_eX]

[DOI]

Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

CAFE: Learning to Condense Dataset by Aligning Features.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

An Efficient Training Approach for Very Large Scale Face Recognition.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Crafting Better Contrastive Views for Siamese Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Towards Efficient and Scalable Sharpness-Aware Minimization.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Distributed EK-NN Classification.

[BibT_eX]

[DOI]

Proceedings of the Belief Functions: Theory and Applications, 2022

Go Wider Instead of Deeper.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Evidential instance selection for <i>K</i>-nearest neighbor classification of big data.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2021

Large-Scale Deep Learning Optimizations: A Comprehensive Survey.

[BibT_eX]

[DOI]

CoRR, 2021

Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training.

[BibT_eX]

[DOI]

CoRR, 2021

Sparse-MLP: A Fully-MLP Architecture with Conditional Computation.

[BibT_eX]

[DOI]

CoRR, 2021

PatrickStar: Parallel Training of Pre-trained Models via a Chunk-based Memory Management.

[BibT_eX]

[DOI]

CoRR, 2021

2.5-dimensional distributed model training.

[BibT_eX]

[DOI]

CoRR, 2021

Maximizing Parallelism in Distributed Training for Huge Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2021

Sequence Parallelism: Making 4D Parallelism Possible.

[BibT_eX]

[DOI]

CoRR, 2021

An Efficient Training Approach for Very Large Scale Face Recognition.

[BibT_eX]

[DOI]

CoRR, 2021

An Efficient 2D Method for Training Super-Large Deep Learning Models.

[BibT_eX]

[DOI]

CoRR, 2021

Communication-avoiding kernel ridge regression on parallel and distributed systems.

[BibT_eX]

[DOI]

CCF Trans. High Perform. Comput., 2021

Auto-Precision Scaling for Distributed Deep Learning.

[BibT_eX]

[DOI]

Ruobing Han

James Demmel

Yang You

Proceedings of the High Performance Computing - 36th International Conference, 2021

Online evolutionary batch size orchestration for scheduling deep learning workloads in GPU clusters.

[BibT_eX]

[DOI]

Proceedings of the International Conference for High Performance Computing, 2021

Dynamic scaling for low-precision learning.

[BibT_eX]

[DOI]

Proceedings of the PPoPP '21: 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2021

Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops, 2021

Mask Aware Network for Masked Face Recognition in the Wild.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

2020

Fast LSTM by dynamic decomposition on cloud and distributed systems.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2020

How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers.

[BibT_eX]

[DOI]

CoRR, 2020

The Limit of the Batch Size.

[BibT_eX]

[DOI]

CoRR, 2020

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

Rethinking the Value of Asynchronous Solvers for Distributed Deep Learning.

[BibT_eX]

[DOI]

Arissa Wongpanich

Yang You

James Demmel

Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, 2020

2019

Fast Deep Neural Network Training on Distributed Systems and Cloud TPUs.

[BibT_eX]

[DOI]

IEEE Trans. Parallel Distributed Syst., 2019

Reducing BERT Pre-Training Time from 3 Days to 76 Minutes.

[BibT_eX]

[DOI]

CoRR, 2019

Large-batch training for LSTM and beyond.

[BibT_eX]

[DOI]

Proceedings of the International Conference for High Performance Computing, 2019

Fast LSTM Inference by Dynamic Decomposition on Cloud Systems.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

2018

Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems.

[BibT_eX]

[DOI]

Proceedings of the 32nd International Conference on Supercomputing, 2018

ImageNet Training in Minutes.

[BibT_eX]

[DOI]

Proceedings of the 47th International Conference on Parallel Processing, 2018

2017

Design and Implementation of a Communication-Optimal Classifier for Distributed Kernel Support Vector Machines.

[BibT_eX]

[DOI]

IEEE Trans. Parallel Distributed Syst., 2017

Parallel Multiclass Support Vector Machine for Remote Sensing Data Classification on Multicore and Many-Core Architectures.

[BibT_eX]

[DOI]

IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2017

Designing and implementing a heuristic cross-architecture combination for graph traversal.

[BibT_eX]

[DOI]

J. Parallel Distributed Comput., 2017

100-epoch ImageNet Training with AlexNet in 24 Minutes.

[BibT_eX]

[DOI]

CoRR, 2017

Scaling deep learning on GPU and knights landing clusters.

[BibT_eX]

[DOI]

Yang You

Aydin Buluç

James Demmel

Proceedings of the International Conference for High Performance Computing, 2017

Runtime Data Layout Scheduling for Machine Learning Dataset.

[BibT_eX]

[DOI]

Yang You

James Demmel

Proceedings of the 46th International Conference on Parallel Processing, 2017

2016

Asynchronous Parallel Greedy Coordinate Descent.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Scaling Support Vector Machines on modern HPC platforms.

[BibT_eX]

[DOI]

Yang You

Haohuan Fu

Shuaiwen Leon Song

Amanda Peters Randles

J. Parallel Distributed Comput., 2015

CA-SVM: Communication-Avoiding Support Vector Machines on Distributed Systems.

[BibT_eX]

[DOI]

Proceedings of the 2015 IEEE International Parallel and Distributed Processing Symposium, 2015

2014

Evaluating multi-core and many-core architectures through accelerating the three-dimensional Lax-Wendroff correction stencil.

[BibT_eX]

[DOI]

Int. J. High Perform. Comput. Appl., 2014

MIC-SVM: Designing a Highly Efficient Support Vector Machine for Advanced Modern Multi-core and Many-Core Architectures.

[BibT_eX]

[DOI]

Amanda Peters Randles

Guangwen Yang

Proceedings of the 2014 IEEE 28th International Parallel and Distributed Processing Symposium, 2014

Designing a Heuristic Cross-Architecture Combination for Breadth-First Search.

[BibT_eX]

[DOI]

Yang You

David A. Bader

Maryam Mehri Dehnavi

Proceedings of the 43rd International Conference on Parallel Processing, 2014

Scaling and analyzing the stencil performance on multi-core and many-core architectures.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Parallel and Distributed Systems, 2014

2013

Accelerating the 3D Elastic Wave Forward Modeling on GPU and MIC.

[BibT_eX]

[DOI]

Proceedings of the 2013 IEEE International Symposium on Parallel & Distributed Processing, 2013

Yang You

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