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2020 – today
- 2022
[c79]Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank J. Reddi, Sanjiv Kumar:
Robust Training of Neural Networks Using Scale Invariant Architectures. ICML 2022: 12656-12684- [c78]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar:
In defense of dual-encoders for neural ranking. ICML 2022: 15376-15400 - [i74]Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank J. Reddi, Sanjiv Kumar:
Robust Training of Neural Networks using Scale Invariant Architectures. CoRR abs/2202.00980 (2022) - [i73]Taman Narayan, Heinrich Jiang, Sen Zhao, Sanjiv Kumar:
Predicting on the Edge: Identifying Where a Larger Model Does Better. CoRR abs/2202.07652 (2022) - [i72]Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
ELM: Embedding and Logit Margins for Long-Tail Learning. CoRR abs/2204.13208 (2022) - [i71]Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar:
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s. CoRR abs/2206.14286 (2022) - [i70]Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar:
Teacher Guided Training: An Efficient Framework for Knowledge Transfer. CoRR abs/2208.06825 (2022) - [i69]Zonglin Li, Ruiqi Guo, Sanjiv Kumar:
Decoupled Context Processing for Context Augmented Language Modeling. CoRR abs/2210.05758 (2022) - [i68]Zonglin Li, Chong You, Srinadh Bhojanapalli, Daliang Li, Ankit Singh Rawat, Sashank J. Reddi, Ke Ye, Felix X. Chern, Felix X. Yu, Ruiqi Guo, Sanjiv Kumar:
Large Models are Parsimonious Learners: Activation Sparsity in Trained Transformers. CoRR abs/2210.06313 (2022) - [i67]Arslan Chaudhry, Aditya Krishna Menon, Andreas Veit, Sadeep Jayasumana, Srikumar Ramalingam, Sanjiv Kumar:
When does mixup promote local linearity in learned representations? CoRR abs/2210.16413 (2022) - [i66]Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix X. Yu, Cho-Jui Hsieh, Inderjit S. Dhillon, Sanjiv Kumar:
Preserving In-Context Learning ability in Large Language Model Fine-tuning. CoRR abs/2211.00635 (2022) - [i65]Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix X. Yu, Sanjiv Kumar:
Large Language Models with Controllable Working Memory. CoRR abs/2211.05110 (2022) - 2021
- [j15]Ankita Verma, Savita
, Sanjiv Kumar:
Routing Protocols in Delay Tolerant Networks: Comparative and Empirical Analysis. Wirel. Pers. Commun. 118(1): 551-574 (2021) - [c77]Sashank J. Reddi, Rama Kumar Pasumarthi, Aditya Krishna Menon, Ankit Singh Rawat, Felix X. Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar:
RankDistil: Knowledge Distillation for Ranking. AISTATS 2021: 2368-2376 - [c76]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. ICLR 2021 - [c75]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar:
Long-tail learning via logit adjustment. ICLR 2021 - [c74]Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
Overparameterisation and worst-case generalisation: friend or foe? ICLR 2021 - [c73]Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan:
Adaptive Federated Optimization. ICLR 2021 - [c72]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. ICLR 2021 - [c71]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar:
A statistical perspective on distillation. ICML 2021: 7632-7642 - [c70]Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces. ICML 2021: 8890-8901 - [c69]Erik Lindgren, Sashank J. Reddi, Ruiqi Guo, Sanjiv Kumar:
Efficient Training of Retrieval Models using Negative Cache. NeurIPS 2021: 4134-4146 - [c68]Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, Sanjiv Kumar:
Batch Active Learning at Scale. NeurIPS 2021: 11933-11944 - [i64]Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit, Ankit Singh Rawat, Seungyeon Kim, Aditya Krishna Menon, Sanjiv Kumar:
On the Reproducibility of Neural Network Predictions. CoRR abs/2102.03349 (2021) - [i63]Srikumar Ramalingam, Daniel Glasner, Kaushal Patel, Raviteja Vemulapalli, Sadeep Jayasumana, Sanjiv Kumar:
Balancing Constraints and Submodularity in Data Subset Selection. CoRR abs/2104.12835 (2021) - [i62]Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces. CoRR abs/2105.05736 (2021) - [i61]Seungyeon Kim, Daniel Glasner, Srikumar Ramalingam, Cho-Jui Hsieh, Kishore Papineni, Sanjiv Kumar:
Balancing Robustness and Sensitivity using Feature Contrastive Learning. CoRR abs/2105.09394 (2021) - [i60]Baris Sumengen, Anand Rajagopalan, Gui Citovsky, David Simcha, Olivier Bachem, Pradipta Mitra, Sam Blasiak, Mason Liang, Sanjiv Kumar:
Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets. CoRR abs/2105.11653 (2021) - [i59]Srinadh Bhojanapalli, Ayan Chakrabarti, Himanshu Jain, Sanjiv Kumar, Michal Lukasik, Andreas Veit:
Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation. CoRR abs/2106.08823 (2021) - [i58]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Teacher's pet: understanding and mitigating biases in distillation. CoRR abs/2106.10494 (2021) - [i57]Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, Sanjiv Kumar:
Batch Active Learning at Scale. CoRR abs/2107.14263 (2021) - [i56]Srinadh Bhojanapalli, Ayan Chakrabarti, Andreas Veit, Michal Lukasik, Himanshu Jain, Frederick Liu, Yin-Wen Chang, Sanjiv Kumar:
Leveraging redundancy in attention with Reuse Transformers. CoRR abs/2110.06821 (2021) - [i55]Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Amr Ahmed, Sanjiv Kumar:
When in Doubt, Summon the Titans: Efficient Inference with Large Models. CoRR abs/2110.10305 (2021) - 2020
- [c67]Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar:
Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy. AISTATS 2020: 89-99 - [c66]Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh:
How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework. CVPR 2020: 279-287 - [c65]Michal Lukasik, Himanshu Jain, Aditya Krishna Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix X. Yu, Sanjiv Kumar:
Semantic Label Smoothing for Sequence to Sequence Problems. EMNLP (1) 2020: 4992-4998 - [c64]Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, Sanjiv Kumar:
Pre-training Tasks for Embedding-based Large-scale Retrieval. ICLR 2020 - [c63]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Can gradient clipping mitigate label noise? ICLR 2020 - [c62]Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho-Jui Hsieh:
Learning to Learn by Zeroth-Order Oracle. ICLR 2020 - [c61]Yang You, Jing Li, Sashank J. Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh:
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. ICLR 2020 - [c60]Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Are Transformers universal approximators of sequence-to-sequence functions? ICLR 2020 - [c59]Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Low-Rank Bottleneck in Multi-head Attention Models. ICML 2020: 864-873 - [c58]Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar:
Accelerating Large-Scale Inference with Anisotropic Vector Quantization. ICML 2020: 3887-3896 - [c57]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Does label smoothing mitigate label noise? ICML 2020: 6448-6458 - [c56]Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Federated Learning with Only Positive Labels. ICML 2020: 10946-10956 - [c55]Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Robust large-margin learning in hyperbolic space. NeurIPS 2020 - [c54]Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane S. Boning, Cho-Jui Hsieh:
Multi-Stage Influence Function. NeurIPS 2020 - [c53]Yuhan Liu, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Michael Riley:
Learning discrete distributions: user vs item-level privacy. NeurIPS 2020 - [c52]Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers. NeurIPS 2020 - [c51]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why are Adaptive Methods Good for Attention Models? NeurIPS 2020 - [i54]Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, Sanjiv Kumar:
Pre-training Tasks for Embedding-based Large-scale Retrieval. CoRR abs/2002.03932 (2020) - [i53]Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Low-Rank Bottleneck in Multi-head Attention Models. CoRR abs/2002.07028 (2020) - [i52]Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, H. Brendan McMahan:
Adaptive Federated Optimization. CoRR abs/2003.00295 (2020) - [i51]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Does label smoothing mitigate label noise? CoRR abs/2003.02819 (2020) - [i50]Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Robust Large-Margin Learning in Hyperbolic Space. CoRR abs/2004.05465 (2020) - [i49]Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Federated Learning with Only Positive Labels. CoRR abs/2004.10342 (2020) - [i48]Ankit Singh Rawat, Aditya Krishna Menon, Andreas Veit, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Doubly-stochastic mining for heterogeneous retrieval. CoRR abs/2004.10915 (2020) - [i47]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar:
Why distillation helps: a statistical perspective. CoRR abs/2005.10419 (2020) - [i46]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. CoRR abs/2006.00442 (2020) - [i45]Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
$O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers. CoRR abs/2006.04862 (2020) - [i44]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar:
Long-tail learning via logit adjustment. CoRR abs/2007.07314 (2020) - [i43]Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane S. Boning, Cho-Jui Hsieh:
Multi-Stage Influence Function. CoRR abs/2007.09081 (2020) - [i42]Yuhan Liu, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Michael Riley:
Learning discrete distributions: user vs item-level privacy. CoRR abs/2007.13660 (2020) - [i41]Michal Lukasik, Himanshu Jain, Aditya Krishna Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix X. Yu, Sanjiv Kumar:
Semantic Label Smoothing for Sequence to Sequence Problems. CoRR abs/2010.07447 (2020) - [i40]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. CoRR abs/2010.12230 (2020) - [i39]Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix X. Yu, Sanjiv Kumar:
Modifying Memories in Transformer Models. CoRR abs/2012.00363 (2020) - [i38]Sadeep Jayasumana, Srikumar Ramalingam, Sanjiv Kumar:
Kernelized Classification in Deep Networks. CoRR abs/2012.09607 (2020)
2010 – 2019
- 2019
- [c50]Yanjun Li, Kai Zhang, Jun Wang, Sanjiv Kumar:
Learning Adaptive Random Features. AAAI 2019: 4229-4236 - [c49]Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar:
Optimal Noise-Adding Mechanism in Additive Differential Privacy. AISTATS 2019: 11-20 - [c48]Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Jiecao Chen, Sanjiv Kumar:
Stochastic Negative Mining for Learning with Large Output Spaces. AISTATS 2019: 1940-1949 - [c47]Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh:
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks. ICLR (Poster) 2019 - [c46]Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra:
Escaping Saddle Points with Adaptive Gradient Methods. ICML 2019: 5956-5965 - [c45]Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Niels Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar:
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling. ICML 2019: 6828-6839 - [c44]Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Niels Holtmann-Rice, Satyen Kale, Sashank J. Reddi, Sanjiv Kumar:
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. NeurIPS 2019: 4944-4954 - [c43]Ankit Singh Rawat, Jiecao Chen, Felix X. Yu, Ananda Theertha Suresh, Sanjiv Kumar:
Sampled Softmax with Random Fourier Features. NeurIPS 2019: 13834-13844 - [i37]Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra:
Escaping Saddle Points with Adaptive Gradient Methods. CoRR abs/1901.09149 (2019) - [i36]Xiang Wu, Ruiqi Guo, David Simcha, Dave Dopson, Sanjiv Kumar:
Efficient Inner Product Approximation in Hybrid Spaces. CoRR abs/1903.08690 (2019) - [i35]Xiang Wu, Ruiqi Guo, Sanjiv Kumar, David Simcha:
Local Orthogonal Decomposition for Maximum Inner Product Search. CoRR abs/1903.10391 (2019) - [i34]Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
On the Convergence of Adam and Beyond. CoRR abs/1904.09237 (2019) - [i33]Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh:
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise. CoRR abs/1906.02355 (2019) - [i32]Ankit Singh Rawat, Jiecao Chen, Felix X. Yu, Ananda Theertha Suresh, Sanjiv Kumar:
Sampled Softmax with Random Fourier Features. CoRR abs/1907.10747 (2019) - [i31]Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
AdaCliP: Adaptive Clipping for Private SGD. CoRR abs/1908.07643 (2019) - [i30]Ruiqi Guo, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar, Xiang Wu:
New Loss Functions for Fast Maximum Inner Product Search. CoRR abs/1908.10396 (2019) - [i29]Aditya Krishna Menon, Anand Rajagopalan, Baris Sumengen, Gui Citovsky, Qin Cao, Sanjiv Kumar:
Online Hierarchical Clustering Approximations. CoRR abs/1909.09667 (2019) - [i28]Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho-Jui Hsieh:
Learning to Learn by Zeroth-Order Oracle. CoRR abs/1910.09464 (2019) - [i27]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why ADAM Beats SGD for Attention Models. CoRR abs/1912.03194 (2019) - [i26]Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Are Transformers universal approximators of sequence-to-sequence functions? CoRR abs/1912.10077 (2019) - 2018
- [c42]Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
On the Convergence of Adam and Beyond. ICLR 2018 - [c41]Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar:
Loss Decomposition for Fast Learning in Large Output Spaces. ICML 2018: 5626-5635 - [c40]Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Brendan McMahan:
cpSGD: Communication-efficient and differentially-private distributed SGD. NeurIPS 2018: 7575-7586 - [c39]Manzil Zaheer, Sashank J. Reddi, Devendra Singh Sachan, Satyen Kale, Sanjiv Kumar:
Adaptive Methods for Nonconvex Optimization. NeurIPS 2018: 9815-9825 - [i25]Si Si, Sanjiv Kumar, Yang Li:
Nonlinear Online Learning with Adaptive Nyström Approximation. CoRR abs/1802.07887 (2018) - [i24]Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan:
cpSGD: Communication-efficient and differentially-private distributed SGD. CoRR abs/1805.10559 (2018) - [i23]Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Niels Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar:
The Sparse Recovery Autoencoder. CoRR abs/1806.10175 (2018) - [i22]Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar:
Optimal Noise-Adding Mechanism in Additive Differential Privacy. CoRR abs/1809.10224 (2018) - [i21]Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar:
Truncated Laplacian Mechanism for Approximate Differential Privacy. CoRR abs/1810.00877 (2018) - [i20]Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel N. Holtmann-Rice, Jiecao Chen, Sanjiv Kumar:
Stochastic Negative Mining for Learning with Large Output Spaces. CoRR abs/1810.07076 (2018) - [i19]Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh:
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks. CoRR abs/1810.12406 (2018) - 2017
- [j14]Felix X. Yu, Aditya Bhaskara, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang:
On Binary Embedding using Circulant Matrices. J. Mach. Learn. Res. 18: 150:1-150:30 (2017) - [c38]Kai Zhong, Ruiqi Guo, Sanjiv Kumar, Bowei Yan, David Simcha, Inderjit S. Dhillon:
Fast Classification with Binary Prototypes. AISTATS 2017: 1255-1263 - [c37]Xu Zhang, Felix X. Yu, Sanjiv Kumar, Shih-Fu Chang:
Learning Spread-Out Local Feature Descriptors. ICCV 2017: 4605-4613 - [c36]Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song:
Stochastic Generative Hashing. ICML 2017: 913-922 - [c35]Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan:
Distributed Mean Estimation with Limited Communication. ICML 2017: 3329-3337 - [c34]Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel N. Holtmann-Rice, David Simcha, Felix X. Yu:
Multiscale Quantization for Fast Similarity Search. NIPS 2017: 5745-5755 - [i18]Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song:
Stochastic Generative Hashing. CoRR abs/1701.02815 (2017) - [i17]Matthew L. Henderson, Rami Al-Rfou, Brian Strope, Yun-Hsuan Sung, László Lukács, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, Ray Kurzweil:
Efficient Natural Language Response Suggestion for Smart Reply. CoRR abs/1705.00652 (2017) - [i16]Xu Zhang, Felix X. Yu, Sanjiv Kumar, Shih-Fu Chang:
Learning Spread-out Local Feature Descriptors. CoRR abs/1708.06320 (2017) - [i15]Blaise Agüera y Arcas, Beat Gfeller, Ruiqi Guo, Kevin Kilgour, Sanjiv Kumar, James Lyon, Julian Odell, Marvin Ritter, Dominik Roblek, Matthew Sharifi, Mihajlo Velimirovic:
Now Playing: Continuous low-power music recognition. CoRR abs/1711.10958 (2017) - 2016
- [j13]Sanjiv Kumar, Ritika Chopra, Ratnesh Rajan Saxena:
A Fast Approach to Solve Matrix Games with Payoffs of Trapezoidal Fuzzy Numbers. Asia Pac. J. Oper. Res. 33(6): 1650047:1-1650047:14 (2016) - [j12]Wonjun Lee, Sanjiv Kumar:
Software-Defined Storage-Based Data Infrastructure Supportive of Hydroclimatology Simulation Containers: A Survey. Data Sci. Eng. 1(2): 65-72 (2016) - [j11]Jun Wang, Wei Liu, Sanjiv Kumar, Shih-Fu Chang:
Learning to Hash for Indexing Big Data - A Survey. Proc. IEEE 104(1): 34-57 (2016) - [c33]Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha:
Quantization based Fast Inner Product Search. AISTATS 2016: 482-490 - [c32]Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun:
Binary embeddings with structured hashed projections. ICML 2016: 344-353 - [c31]Felix X. Yu, Ananda Theertha Suresh, Krzysztof Marcin Choromanski, Daniel N. Holtmann-Rice, Sanjiv Kumar:
Orthogonal Random Features. NIPS 2016: 1975-1983 - [i14]Felix X. Yu, Ananda Theertha Suresh, Krzysztof Choromanski, Daniel N. Holtmann-Rice, Sanjiv Kumar:
Orthogonal Random Features. CoRR abs/1610.09072 (2016) - [i13]Ananda Theertha Suresh, Felix X. Yu, H. Brendan McMahan, Sanjiv Kumar:
Distributed Mean Estimation with Limited Communication. CoRR abs/1611.00429 (2016) - 2015
- [c30]Yanbo Xu, Olivier Siohan, David Simcha, Sanjiv Kumar, Hank Liao:
Exemplar-based large vocabulary speech recognition using k-nearest neighbors. ICASSP 2015: 5167-5171 - [c29]Yu Cheng, Felix X. Yu, Rogério Schmidt Feris, Sanjiv Kumar, Alok N. Choudhary, Shih-Fu Chang:
An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections. ICCV 2015: 2857-2865 - [c28]Xu Zhang, Felix X. Yu, Ruiqi Guo, Sanjiv Kumar, Shengjin Wang, Shih-Fu Chang:
Fast Orthogonal Projection Based on Kronecker Product. ICCV 2015: 2929-2937 - [c27]Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar:
A Survey of Modern Questions and Challenges in Feature Extraction. FE@NIPS 2015: 1-18 - [c26]Jeffrey Pennington, Felix X. Yu, Sanjiv Kumar:
Spherical Random Features for Polynomial Kernels. NIPS 2015: 1846-1854 - [c25]Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar:
Structured Transforms for Small-Footprint Deep Learning. NIPS 2015: 3088-3096 - [p2]Felix X. Yu, Yunchao Gong, Sanjiv Kumar:
Fast Binary Embedding for High-Dimensional Data. Multimedia Data Mining and Analytics 2015: 347-371 - [i12]Yu Cheng, Felix X. Yu, Rogério Schmidt Feris, Sanjiv Kumar, Alok N. Choudhary, Shih-Fu Chang:
Fast Neural Networks with Circulant Projections. CoRR abs/1502.03436 (2015) - [i11]Felix X. Yu, Sanjiv Kumar, Henry A. Rowley, Shih-Fu Chang:
Compact Nonlinear Maps and Circulant Extensions. CoRR abs/1503.03893 (2015) - [i10]Krzysztof Choromanski, Sanjiv Kumar, Xiaofeng Liu:
Fast Online Clustering with Randomized Skeleton Sets. CoRR abs/1506.03425 (2015) - [i9]Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha:
Quantization based Fast Inner Product Search. CoRR abs/1509.01469 (2015) - [i8]