Erik M Lindgren
Erik M Lindgren
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Accelerating large-scale inference with anisotropic vector quantization
R Guo, P Sun, E Lindgren, Q Geng, D Simcha, F Chern, S Kumar
International Conference on Machine Learning, 3887-3896, 2020
Composing Normalizing Flows for Inverse Problems
J Jay Whang, E Lindgren, A Dimakis
International Conference on Machine Learning (ICML), 2021
Leveraging sparsity for efficient submodular data summarization
E Lindgren, S Wu, AG Dimakis
Advances in Neural Information Processing Systems 29, 2016
Experimental design for cost-aware learning of causal graphs
E Lindgren, M Kocaoglu, AG Dimakis, S Vishwanath
Advances in Neural Information Processing Systems 31, 2018
A rule-based design specification language for synthetic biology
E Oberortner, S Bhatia, E Lindgren, D Densmore
ACM Journal on Emerging Technologies in Computing Systems (JETC) 11 (3), 1-19, 2014
Efficient training of retrieval models using negative cache
E Lindgren, S Reddi, R Guo, S Kumar
Advances in Neural Information Processing Systems 34, 4134-4146, 2021
On robust learning of ising models
EM Lindgren, V Shah, Y Shen, AG Dimakis, A Klivans
NeurIPS Workshop on Relational Representation Learning, 2019
Exact map inference by avoiding fractional vertices
EM Lindgren, AG Dimakis, A Klivans
International Conference on Machine Learning, 2120-2129, 2017
Combinatorial Optimization for Graphical Structures in Machine Learning
EM Lindgren
The University of Texas at Austin, 2019
Rescaled JL Embedding
S Wu, E Lindgren
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