Slide: In defense of smart algorithms over hardware acceleration for large-scale deep learning systems B Chen, T Medini, J Farwell, C Tai, A Shrivastava Proceedings of Machine Learning and Systems 2, 291-306, 2020 | 115 | 2020 |
Extreme classification in log memory using count-min sketch: A case study of amazon search with 50m products TKR Medini, Q Huang, Y Wang, V Mohan, A Shrivastava Advances in Neural Information Processing Systems 32, 2019 | 76 | 2019 |
Fast processing and querying of 170tb of genomics data via a repeated and merged bloom filter (rambo) G Gupta, M Yan, B Coleman, B Kille, RAL Elworth, T Medini, T Treangen, ... Proceedings of the 2021 International Conference on Management of Data, 2226 …, 2021 | 14 | 2021 |
A tale of two efficient and informative negative sampling distributions S Daghaghi, T Medini, N Meisburger, B Chen, M Zhao, A Shrivastava International conference on machine learning, 2319-2329, 2021 | 11 | 2021 |
Bliss: A billion scale index using iterative re-partitioning G Gupta, T Medini, A Shrivastava, AJ Smola Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 9 | 2022 |
sameh gobriel, Charlie Tai, and Anshumali Shrivastava. Slide: In defense of smart algorithms over hardware acceleration for large-scale deep learning systems B Chen, T Medini, J Farwell Proceedings of Machine Learning and Systems 2, 291-306, 2020 | 9 | 2020 |
Solar: Sparse orthogonal learned and random embeddings T Medini, B Chen, A Shrivastava arXiv preprint arXiv:2008.13225, 2020 | 8 | 2020 |
Distributed SLIDE: Enabling training large neural networks on low bandwidth and simple CPU-clusters via model parallelism and sparsity M Yan, N Meisburger, T Medini, A Shrivastava arXiv preprint arXiv:2201.12667, 2022 | 6 | 2022 |
RAMBO: Repeated And Merged BloOm filter for ultra-fast Multiple Set Membership Testing (MSMT) on large-scale data G Gupta, M Yan, B Coleman, RA Elworth, T Medini, T Treangen, ... arXiv preprint arXiv:1910.02611, 2019 | 6 | 2019 |
Sdm-net: a simple and effective model for generalized zero-shot learning S Daghaghi, T Medini, A Shrivastava Uncertainty in Artificial Intelligence, 2103-2113, 2021 | 4 | 2021 |
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Neural Networks on Commodity CPU Hardware N Meisburger, V Lakshman, B Geordie, J Engels, DT Ramos, P Pranav, ... arXiv preprint arXiv:2303.17727, 2023 | 3 | 2023 |
Structural contrastive representation learning for zero-shot multi-label text classification T Zhang, Z Xu, T Medini, A Shrivastava Findings of the Association for Computational Linguistics: EMNLP 2022, 4937-4947, 2022 | 3 | 2022 |
Semantic similarity based softmax classifier for zero-shot learning S Daghaghi, T Medini, A Shrivastava arXiv preprint arXiv:1909.04790, 2019 | 3 | 2019 |
Extreme classification in log memory Q Huang, Y Wang, T Medini, A Shrivastava arXiv preprint arXiv:1810.04254, 2018 | 3 | 2018 |
From Research to Production: Towards Scalable and Sustainable Neural Recommendation Models on Commodity CPU Hardware A Shrivastava, V Lakshman, T Medini, N Meisburger, J Engels, ... Proceedings of the 17th ACM Conference on Recommender Systems, 1071-1074, 2023 | 1 | 2023 |
IRLI: Iterative Re-partitioning for Learning to Index G Gupta, T Medini, A Shrivastava, AJ Smola arXiv preprint arXiv:2103.09944, 2021 | 1 | 2021 |
Simultaneous matching and ranking as end-to-end deep classification: A case study of information retrieval with 50M Documents TKR Medini, Q Huang, Y Wang, V Mohan, A Shrivastava | 1 | 2019 |
A4c: Anticipatory asynchronous advantage actor-critic T Medini, X Luan, A Shrivastava | 1 | 2018 |
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware N Meisburger, V Lakshman, B Geordie, J Engels, DT Ramos, P Pranav, ... Proceedings of the 32nd ACM International Conference on Information and …, 2023 | | 2023 |
Randomized Algorithms for Training Deep Models with Large Outputs T Medini | | 2022 |