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Posts by Collection
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preprints
Spooky Action at a Distance: Normalization Layers Enable Side-Channel Spatial Communication
Published:
Normalization layers enable a surprising degree of spatial communication, and we suggest caution in employing them for applications requiring a limited receptive field.
Recommended citation: Samuel Pfrommer, George Ma, Yixiao Huang, Somayeh Sojoudi (2025). Spooky Action at a Distance: Normalization Layers Enable Side-Channel Spatial Communication. arXiv preprint arXiv:2507.04709. https://www.arxiv.org/abs/2507.04709
SpecAgent: A Speculative Retrieval and Forecasting Agent for Code Completion
Published:
SpecAgent proactively explores software repositories at indexing time to build speculative context that eliminates inference-time retrieval latency, improves LLM code-generation accuracy by up to 11% absolute, and introduces a new leakage-free benchmark construction method for realistic evaluation.
Recommended citation: George Ma, Anurag Koul, Qi Chen, Yawen Wu, Sachit Kuhar, Yu Yu, Aritra Sengupta, Varun Kumar, Murali Krishna Ramanathan (2025). SpecAgent: A Speculative Retrieval and Forecasting Agent for Code Completion. arXiv preprint arXiv:2510.17925. https://arxiv.org/abs/2510.17925
publications
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
Published in NeurIPS, 2023
We designed the Laplacian Canonization algorithm to address the sign and basis ambiguities of Laplacian eigenvectors.
Recommended citation: Jiangyan Ma, Yifei Wang, Yisen Wang (2023). Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding. In Thirty-seventh Conference on Neural Information Processing Systems. https://openreview.net/forum?id=1mAYtdoYw6
Baking Symmetry into GFlowNets
Published in NeurIPS-AI4Science, 2023
We proposed to incorporate state and action symmetries into GFlowNets.
Recommended citation: Jiangyan Ma, Emmanuel Bengio, Yoshua Bengio, Dinghuai Zhang (2023). Baking Symmetry into GFlowNets. In NeurIPS 2023 AI for Science: from Theory to Practice. https://openreview.net/forum?id=CZGHAeeBk3
A Canonicalization Perspective on Invariant and Equivariant Learning
Published in NeurIPS, 2024
We analysed the efficiency and expressiveness of invariant and equivariant networks from a canonicalization perspective.
Recommended citation: George Ma, Yifei Wang, Derek Lim, Stefanie Jegelka, Yisen Wang (2024). A Canonicalization Perspective on Invariant and Equivariant Learning. In Thirty-eighth Conference on Neural Information Processing Systems. https://openreview.net/forum?id=jjcY92FX4R¬eId=jjcY92FX4R
Revising and Falsifying Sparse Autoencoder Feature Explanations
Published in NeurIPS, 2025
We developed new methods to refine and falsify sparse autoencoder feature explanations, yielding higher-quality interpretability of large language models.
Recommended citation: George Ma, Samuel Pfrommer, Somayeh Sojoudi (2025). Revising and Falsifying Sparse Autoencoder Feature Explanations. In Thirty-ninth Conference on Neural Information Processing Systems. https://openreview.net/forum?id=OJAW2mHVND
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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