Michael Maire

Michael Maire

University of Chicago

H-index: 36

North America-United States

About Michael Maire

Michael Maire, With an exceptional h-index of 36 and a recent h-index of 30 (since 2020), a distinguished researcher at University of Chicago, specializes in the field of Computer Vision, Deep Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Drop-Connect as a Fault-Tolerance Approach for RRAM-based Deep Neural Network Accelerators

Residual Connections Harm Self-Supervised Abstract Feature Learning

Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation

Deciphering'What'and'Where'Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations

SySMOL: A Hardware-software Co-design Framework for Ultra-Low and Fine-Grained Mixed-Precision Neural Networks

Hyperfields: Towards zero-shot generation of nerfs from text

Run-Time Prevention of Software Integration Failures of Machine Learning APIs

CacheGen: Fast Context Loading for Language Model Applications

Michael Maire Information

University

Position

___

Citations(all)

58691

Citations(since 2020)

45559

Cited By

26037

hIndex(all)

36

hIndex(since 2020)

30

i10Index(all)

50

i10Index(since 2020)

44

Email

University Profile Page

Google Scholar

Michael Maire Skills & Research Interests

Computer Vision

Deep Learning

Top articles of Michael Maire

Drop-Connect as a Fault-Tolerance Approach for RRAM-based Deep Neural Network Accelerators

arXiv preprint arXiv:2404.15498

2024/4/23

Residual Connections Harm Self-Supervised Abstract Feature Learning

arXiv preprint arXiv:2404.10947

2024/4/16

Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation

Advances in Neural Information Processing Systems

2024/2/13

Deciphering'What'and'Where'Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations

arXiv preprint arXiv:2312.06716

2023/12/11

Xiao Zhang
Xiao Zhang

H-Index: 4

Michael Maire
Michael Maire

H-Index: 23

SySMOL: A Hardware-software Co-design Framework for Ultra-Low and Fine-Grained Mixed-Precision Neural Networks

arXiv preprint arXiv:2311.14114

2023/11/23

Hyperfields: Towards zero-shot generation of nerfs from text

arXiv preprint arXiv:2310.17075

2023/10/26

Run-Time Prevention of Software Integration Failures of Machine Learning APIs

Proceedings of the ACM on Programming Languages

2023/10/16

CacheGen: Fast Context Loading for Language Model Applications

arXiv preprint arXiv:2310.07240

2023/10/11

Automatic and Efficient Customization of Neural Networks for ML Applications

arXiv preprint arXiv:2310.04685

2023/10/7

Structural Adversarial Objectives for Self-Supervised Representation Learning

arXiv preprint arXiv:2310.00357

2023/9/30

Xiao Zhang
Xiao Zhang

H-Index: 4

Michael Maire
Michael Maire

H-Index: 23

Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation

arXiv preprint arXiv:2309.15726

2023/9/27

Xin Yuan
Xin Yuan

H-Index: 11

Michael Maire
Michael Maire

H-Index: 23

Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits

arXiv preprint arXiv:2305.19889

2023/5/31

Not all bits have equal value: Heterogeneous precisions via trainable noise

Advances in Neural Information Processing Systems

2022/12/6

On convexity and linear mode connectivity in neural networks

2022/11/23

Accelerated Training via Principled Methods for Incrementally Growing Neural Networks

2022/9/29

Xin Yuan
Xin Yuan

H-Index: 11

Michael Maire
Michael Maire

H-Index: 23

Automated testing of software that uses machine learning apis

2022/5/21

Boosting contrastive self-supervised learning with false negative cancellation

2022

Tri Huynh
Tri Huynh

H-Index: 4

Michael Maire
Michael Maire

H-Index: 23

Online meta-learning via learning with layer-distributed memory

Advances in Neural Information Processing Systems

2021/12/6

A replication of are machine learning cloud APIs used correctly

2021/5/25

Are machine learning cloud apis used correctly?

2021/5/22

See List of Professors in Michael Maire University(University of Chicago)