Publications

Found 70 results
Author Title [ Type(Asc)] Year
Filters: Author is M. W. Mahoney  [Clear All Filters]
Conference Paper
Kim, S., Gholami A., Yao Z., Mahoney M., & Keutzer K. (2021).  I-BERT: Integer-only BERT Quantization.
Yao, Z.., Gholami A.., Lei Q.., Keutzer K.., & Mahoney M. (2018).  Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. Proceedings of the 2018 NeurIPS Conference. 4954-4964.
Martin, C.. H., & Mahoney M. (2020).  Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks. Proceedings of 2020 SDM Conference.
Yao, Z., Dong Z., Zheng Z., Gholami A., Yu J., Tan E., et al. (2021).  HAWQV3: Dyadic Neural Network Quantization.
Dong, Z.., Yao Z.., Gholami A.., Mahoney M., & Keutzer K.. (2019).  HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision. Proceedings of ICCV 2019.
Kylasa, S.. B., Roosta-Khorasani F.., Mahoney M., & Grama A.. (2019).  GPU Accelerated Sub-Sampled Newton's Method. Proceedings of the 2019 SDM Conference. 702-710.
Yang, J., Mahoney M., Saunders M. A., & Sun Y. (2016).  Feature-distributed sparse regression: a screen-and-clean approach. Proceedings of the 2016 NIPS Conference.
Lopes, M.. E., Wang S.., & Mahoney M. (2018).  Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap. Proceedings of the 35th ICML Conference. 3223-3232.
Derezinski, M.., & Mahoney M. (2019).  Distributed estimation of the inverse Hessian by determinantal averaging. Proceedings of the 2019 NeurIPS Conference.
Jing, L., Liu B., Choi J., Janin A., Bernd J., Mahoney M., et al. (2016).  A discriminative and compact audio representation for event detection. Proceedings of the 2016 ACM Conference on Multimedia (MM '16). 57-61.
Devarakonda, A., Fountoulakis K., Demmel J., & Mahoney M. (2016).  Avoiding communication in primal and dual block coordinate descent methods.
Mahoney, M., Rao S., Wang D., & Zhang P. (2016).  Approximating the Solution to Mixed Packing and Covering LPs in parallel time.
Gleich, D., & Mahoney M. (2014).  Anti-Differentiating Approximation Algorithms: A Case Study with Min-Cuts, Spectral, and Flow.
Zhang, T.., Yao Z.., Gholami A.., Keutzer K.., Gonzalez J.., Biros G.., et al. (2019).  ANODEV2: A Coupled Neural ODE Evolution Framework. Proceedings of the 2019 NeurIPS Conference.
Utrera, F., Kravitz E., N. Erichson B., Khanna R., & Mahoney M. (2021).  Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. International Conference on Learning Representations.
Chen, J., Zheng L., Yao Z., Wang D., Stoica I., Mahoney M., et al. (2021).  ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training.
Gittens, A.., Rothauge K.., Wang S.., Mahoney M., Gerhardt L.., Prabhat, et al. (2018).  Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. Proceedings of the 24th Annual SIGKDD. 293-301.
Book Chapter
Mahoney, M., & Drineas P. (2016).  Structural properties underlying high-quality Randomized Numerical Linear Algebra algorithms. Handbook of Big Data. 137-154.
Gleich, D., & Mahoney M. (2016).  Mining Large graphs. Handbook of Big Data. 191-220.

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