Fetching the paper…
Reading the bibliography…
Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning.
Critical-path planning and scheduling
Kelley Jr, J. E. and Walker, M. R · 1959
Earlier work this paper cites.
Iterative solution of nonlinear equations in several variables , volume 30
Ortega, J. M. and Rheinboldt, W. C · 1970
Earlier work this paper cites.
Iterative methods for sparse linear systems , volume 82
Saad, Y · 2003
Earlier work this paper cites.
Gaussian belief propagation: Theory and aplication
Bickson, D · 2008
Earlier work this paper cites.
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2008
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.
MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
Earlier work this paper cites.
Tuffy: Scaling up statistical inference in markov logic networks using an rdbms
Niu, F., Ré, C., Doan, A., and Shavlik, J · 2011
Earlier work this paper cites.
Residual belief propagation: Informed scheduling for asynchronous message passing
Elidan, G., McGraw, I., and Koller, D · 2012
Earlier work this paper cites.
Iterative solution of large linear systems
Young, D. M · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
Cited alongside, same era.
Iterative sparse triangular solves for preconditioning
Anzt, H., Chow, E., and Dongarra, J · 2015
Cited alongside, same era.
Made: Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
Cited alongside, same era.
Domain overlap for iterative sparse triangular solves on gpus
Anzt, H., Chow, E., Szyld, D. B., and Dongarra, J · 2016
Cited alongside, same era.
Fast generation for convolutional autoregressive models
Ramachandran, P., Paine, T. L., Khorrami, P., Babaeizadeh, M., Chang, S., Zhang, Y., Hasegawa-Johnson, M. A., Campbell, R. H., and Huang, T. S · 2017
Later among the works it cites.
Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
Later among the works it cites.
JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
Later among the works it cites.
Using jacobi iterations and blocking for solving sparse triangular systems in incomplete factorization preconditioning
Chow, E., Anzt, H., Scott, J., and Dongarra, J · 2018
Later among the works it cites.
Graph partition neural networks for semi-supervised classification
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2016
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
Parallel complexity of forward and backward propagation
Naumov, M · 2017
Cited alongside, same era.
Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
Cited alongside, same era.
Liao, R., Brockschmidt, M., Tarlow, D., Gaunt, A. L., Urtasun, R., and Zemel, R · 2018
Later among the works it cites.
Parallel wavenet: Fast high-fidelity speech synthesis
van den Oord, A., Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., Driessche, G., Lockhart, E., Cobo, L., Stimberg, F., et al · 2018
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Later among the works it cites.
Mintnet: Building invertible neural networks with masked convolutions
Song, Y., Meng, C., and Ermon, S · 2019
Later among the works it cites.
Flax: A neural network library and ecosystem for JAX, 2020
Heek, J., Levskaya, A., Oliver, A., Ritter, M., Rondepierre, B., Steiner, A., and van Zee, M · 2020
Closest in time.
Predictive sampling with forecasting autoregressive models
Wiggers, A. and Hoogeboom, E · 2020
Closest in time.