Fetching the paper…
Reading the bibliography…
Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and a reduced complexity of inference.
Regression and classification using gaussian process priors
Neal, R. M · 1998
Earlier work this paper cites.
Gaussian processes for machine learning
Rasmussen, C. E. and Williams, C. K · 2006
Earlier work this paper cites.
Gaussian process regression with heteroscedastic or non-gaussian residuals
Wang, C. and Neal, R. M · 2012
Earlier work this paper cites.
Taking the human out of the loop: A review of bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and De Freitas, N · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Deep kernel learning
Wilson, A. G., Hu, Z., Salakhutdinov, R., and Xing, E. P · 2016
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
Earlier work this paper cites.
Convolutional conditional neural processes
Gordon, J., Bruinsma, W. P., Foong, A. Y. K., Requeima, J., Dubois, Y., and Turner, R. E · 2019
Earlier work this paper cites.
Attentive neural processes
Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, S. M. A., Rosenbaum, D., Vinyals, O., and Teh, Y. W · 2019
Earlier work this paper cites.
Neural process family
Dubois, Y., Gordon, J., and Foong, A. Y · 2020
Earlier work this paper cites.
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Foong, A. Y. K., Bruinsma, W. P., Gordon, J., Dubois, Y., Requeima, J., and Turner, R. E · 2020
Earlier work this paper cites.
GP-VAE: Deep probabilistic time series imputation
Fortuin, V., Baranchuk, D., Rätsch, G., and Mandt, S · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2020
Cited alongside, same era.
Approximate inference for fully Bayesian Gaussian process regression
Lalchand, V. and Rasmussen, C. E · 2020
Cited alongside, same era.
Task-agnostic amortized inference of Gaussian process hyperparameters
Liu, S., Sun, X., Ramadge, P. J., and Adams, R. P · 2020
Cited alongside, same era.
pivae: Encoding stochastic process priors with variational autoencoders
Mishra, S., Flaxman, S., Berah, T., Pakkanen, M., Zhu, H., and Bhatt, S · 2020
Trieste, 2022
Berkeley, J., Moss, H. B., Artemev, A., Pascual-Diaz, S., Granta, U., Stojic, H., Couckuyt, I., Qing, J., Loka, N., Paleyes, A., Ober, S. W., and Picheny, V · 2022
Closest in time.
Equivariant diffusion for molecule generation in 3D
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M · 2022
Closest in time.
Diffusion Generative Models in Infinite Dimensions, 2022
Kerrigan, G., Ley, J., and Smyth, P · 2022
Closest in time.
Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L · 2022
Closest in time.
Spectral Diffusion Processes, November 2022
Phillips, A., Seror, T., Hutchinson, M., De Bortoli, V., Doucet, A., and Mathieu, E · 2022
Closest in time.
Hierarchical text-conditional image generation with CLIP latents
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
Cited alongside, same era.
A framework for interdomain and multioutput Gaussian processes
van der Wilk, M., Dutordoir, V., John, S., Artemev, A., Adam, V., and Hensman, J · 2020
Cited alongside, same era.
The Gaussian neural process
Bruinsma, W. P., Requeima, J., Foong, A. Y., Gordon, J., and Turner, R. E · 2021
Cited alongside, same era.
Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
Kossen, J., Band, N., Gomez, A. N., Lyle, C., Rainforth, T., and Gal, Y · 2021
Cited alongside, same era.
Diffusion probabilistic models for 3d point cloud generation
Luo, S. and Hu, W · 2021
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M · 2021
Cited alongside, same era.
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
Closest in time.
Geodiff: A geometric diffusion model for molecular conformation generation
Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J · 2022
Closest in time.
infinite-diff: Infinite resolution diffusion with subsampled mollified states
Bond-Taylor, S. and Willcocks, C. G · 2023
Closest in time.
Autoregressive Conditional Neural Processes
Bruinsma, W., Markou, S., Requeima, J., Foong, A. Y. K., Vaughan, A., Andersson, T., Buonomo, A., Hosking, S., and Turner, R. E · 2023
Closest in time.
Continuous-time functional diffusion processes
Franzese, G., Rossi, S., Rossi, D., Heinonen, M., Filippone, M., and Michiardi, P · 2023
Closest in time.
Score-based diffusion models in function space, 2023
Lim, J. H., Kovachki, N. B., Baptista, R., Beckham, C., Azizzadenesheli, K., Kossaifi, J., Voleti, V., Song, J., Kreis, K., Kautz, J., Pal, C., Vahdat, A., and Anandkumar, A · 2023
Closest in time.
Infinite-Dimensional Diffusion Models for Function Spaces, February 2023
Pidstrigach, J., Marzouk, Y., Reich, S., and Wang, S · 2023
Closest in time.