Fetching the paper…
Reading the bibliography…
Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning.
The approximation of one matrix by another of lower rank
Eckart, C. and Young, G · 1936
Earlier work this paper cites.
Approximate nearest neighbors: towards removing the curse of dimensionality
Indyk, P. and Motwani, R · 1998
Earlier work this paper cites.
Database-friendly random projections
Achlioptas, D · 2001
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Bartlett, P. L. and Mendelson, S · 2002
Earlier work this paper cites.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Fei-Fei, L., Fergus, R., and Perona, P · 2004
Earlier work this paper cites.
Communication lower bounds for distributed-memory matrix multiplication
Irony, D., Toledo, S., and Tiskin, A · 2004
Earlier work this paper cites.
Improved Approximation Algorithms for Large Matrices via Random Projections
Sarlos, T · 2006
Earlier work this paper cites.
The Fast Johnson-Lindenstrauss Transform and Approximate Nearest Neighbors
Ailon, N. and Chazelle, B · 2009
Earlier work this paper cites.
On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
Kakade, S. M., Sridharan, K., and Tewari, A · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Online dictionary learning for sparse coding
Mairal, J., Bach, F., Ponce, J., and Sapiro, G · 2009
Earlier work this paper cites.
A sparse johnson: Lindenstrauss transform
Dasgupta, A., Kumar, R., and Sarlós, T · 2010
Earlier work this paper cites.
Eigen v3
Guennebaud, G., Jacob, B., et al · 2010
Earlier work this paper cites.
Product quantization for nearest neighbor search
Jegou, H., Douze, M., and Schmid, C · 2011
Earlier work this paper cites.
Super-bit locality-sensitive hashing
Ji, J., Li, J., Yan, S., Zhang, B., and Tian, Q · 2012
Earlier work this paper cites.
Simple and Deterministic Matrix Sketching
Liberty, E · 2012
Earlier work this paper cites.
Fast, accurate detection of 100,000 object classes on a single machine
Dean, T., Ruzon, M. A., Segal, M., Shlens, J., Vijayanarasimhan, S., and Yagnik, J · 2013
Earlier work this paper cites.
Osnap: Faster numerical linear algebra algorithms via sparser subspace embeddings
Nelson, J. and Nguyên, H. L · 2013
Earlier work this paper cites.
Compressed matrix multiplication
Pagh, R · 2013
Earlier work this paper cites.
Additive quantization for extreme vector compression
Babenko, A. and Lempitsky, V · 2014
Earlier work this paper cites.
Optimized product quantization
Ge, T., He, K., Ke, Q., and Sun, J · 2014
Earlier work this paper cites.
Stochastic neighbor compression
Kusner, M., Tyree, S., Weinberger, K., and Agrawal, K · 2014
Earlier work this paper cites.
Approximate Matrix Multiplication with Application to Linear Embeddings
Kyrillidis, A., Vlachos, M., and Zouzias, A · 2014
Cited alongside, same era.
Fast Approximate Matrix Multiplication by Solving Linear Systems
Manne, S. and Pal, M · 2014
Cited alongside, same era.
Stacked quantizers for compositional vector compression
Martinez, J., Hoos, H. H., and Little, J. J · 2014
Cited alongside, same era.
Composite Quantization for Approximate Nearest Neighbor Search
Zhang, T., Du, C., and Wang, J · 2014
Cited alongside, same era.
Tree Quantization for Large-Scale Similarity Search and Classification
Babenko, A. and Lempitsky, V · 2015
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Later among the works it cites.
In-datacenter performance analysis of a tensor processing unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al · 2017
Later among the works it cites.
Scnn: An accelerator for compressed-sparse convolutional neural networks
Parashar, A., Rhu, M., Mukkara, A., Puglielli, A., Venkatesan, R., Khailany, B., Emer, J., Keckler, S. W., and Dally, W. J · 2017
Later among the works it cites.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Later among the works it cites.
Scalable and sustainable deep learning via randomized hashing
Spring, R. and Shrivastava, A · 2017
Later among the works it cites.
Polynomial codes: an optimal design for high-dimensional coded matrix multiplication
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bakhtiary, A. H., Lapedriza, A., and Masip, D · 2015
Cited alongside, same era.
Compressing neural networks with the hashing trick
Chen, W., Wilson, J. T., Tyree, S., Weinberger, K. Q., and Chen, Y · 2015
Cited alongside, same era.
Sparse composite quantization
Zhang, T., Qi, G.-J., Tang, J., and Wang, J · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., et al · 2016
Cited alongside, same era.
Babenko, A., Arandjelović, R., and Lempitsky, V · 2016
Cited alongside, same era.
Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks
Chen, Y.-H., Emer, J., and Sze, V · 2016
Cited alongside, same era.
Improved practical matrix sketching with guarantees
Desai, A., Ghashami, M., and Phillips, J. M · 2016
Cited alongside, same era.
Yu, Q., Maddah-Ali, M., and Avestimehr, S · 2017
Later among the works it cites.
Fast classification with binary prototypes
Zhong, K., Guo, R., Kumar, S., Yan, B., Simcha, D., and Dhillon, I · 2017
Later among the works it cites.
A selective overview of sparse principal component analysis
Zou, H. and Xue, L · 2017
Later among the works it cites.
The ucr time series classification archive, October 2018
Dau, H. A., Keogh, E., Kamgar, K., Yeh, C.-C. M., Zhu, Y., Gharghabi, S., Ratanamahatana, C. A., Yanping, Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G., and Hexagon-ML · 2018
Later among the works it cites.
A practical streaming approximate matrix multiplication algorithm
Francis, D. P. and Raimond, K · 2018
Later among the works it cites.
cifar-vgg, 3 2018
Geifman, Y · 2018
Later among the works it cites.
Open-sourcing fbgemm for state-of-the-art server-side inference, 2018
Khudia, D., Basu, P., and Deng, S · 2018
Later among the works it cites.
A Fast Frequent Directions Algorithm for Low Rank Approximation
Teng, D. and Chu, D · 2018
Later among the works it cites.
Quicker adc: Unlocking the hidden potential of product quantization with simd
André, F., Kermarrec, A.-M., and Le Scouarnec, N · 2019
Later among the works it cites.
Chen, B., Medini, T., and Shrivastava, A · 2019
Later among the works it cites.
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
Huang, Z · 2019
Later among the works it cites.
Robust Frequent Directions with Application in Online Learning
Luo, L., Chen, C., Zhang, Z., Li, W.-J., and Zhang, T · 2019
Later among the works it cites.
Machine learning at facebook: Understanding inference at the edge
Wu, C.-J., Brooks, D., Chen, K., Chen, D., Choudhury, S., Dukhan, M., Hazelwood, K., Isaac, E., Jia, Y., Jia, B., et al · 2019
Later among the works it cites.
What is the state of neural network pruning?
Blalock, D. W., Ortiz, J. J. G., Frankle, J., and Guttag, J. V · 2020
Later among the works it cites.
All sparse pca models are wrong, but some are useful. part i: Computation of scores, residuals and explained variance
Camacho, J., Smilde, A., Saccenti, E., and Westerhuis, J · 2020
Later among the works it cites.
Straggler mitigation in distributed matrix multiplication: Fundamental limits and optimal coding
Yu, Q., Ali, M., and Avestimehr, A. S · 2020
Later among the works it cites.