Greg Heinrich

According to our database, Greg Heinrich authored at least 9 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation.

Mike Ranzinger, Jon Barker, Greg Heinrich, Pavlo Molchanov, Bryan Catanzaro, Andrew Tao

CoRR, 2024

MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models.

Gongfan Fang, Hongxu Yin, Saurav Muralidharan, Greg Heinrich, Jeff Pool, Jan Kautz, Pavlo Molchanov, Xinchao Wang

CoRR, 2024

A deeper look at depth pruning of LLMs.

Shoaib Ahmed Siddiqui, Xin Dong, Greg Heinrich, Thomas M. Breuel, Jan Kautz, David Krueger, Pavlo Molchanov

CoRR, 2024

Flextron: Many-in-One Flexible Large Language Model.

Ruisi Cai, Saurav Muralidharan, Greg Heinrich, Hongxu Yin, Zhangyang Wang, Jan Kautz, Pavlo Molchanov

Proceedings of the Forty-first International Conference on Machine Learning, 2024

FasterViT: Fast Vision Transformers with Hierarchical Attention.

Ali Hatamizadeh, Greg Heinrich, Hongxu Yin, Andrew Tao, José M. Álvarez, Jan Kautz, Pavlo Molchanov

Proceedings of the Twelfth International Conference on Learning Representations, 2024

AM-RADIO: Agglomerative Vision Foundation Model Reduce All Domains Into One.

Mike Ranzinger, Greg Heinrich, Jan Kautz, Pavlo Molchanov

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

AM-RADIO: Agglomerative Model - Reduce All Domains Into One.

Mike Ranzinger, Greg Heinrich, Jan Kautz, Pavlo Molchanov

CoRR, 2023

Global Context Vision Transformers.

Ali Hatamizadeh, Hongxu Yin, Greg Heinrich, Jan Kautz, Pavlo Molchanov

Proceedings of the International Conference on Machine Learning, 2023

2019

Metaoptimization on a Distributed System for Deep Reinforcement Learning.

Greg Heinrich, Iuri Frosio

Proceedings of the 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments, 2019