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It is common practice in deep learning to represent a measurement of the world on a discrete grid, e.g.
Compositional pattern producing networks: A novel abstraction of development
Stanley, K. O · 2007
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Generative adversarial networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Shapenet: An information-rich 3d model repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
Maturana, D. and Scherer, S · 2015
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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End-to-end optimized image compression
Ballé, J., Laparra, V., and Simoncelli, E. P · 2016
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Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P · 2016
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Generating large images from latent vectors
Ha, D · 2016
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Gaussian error linear units (gelus)
Hendrycks, D. and Gimpel, K · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Gans trained by a two time-scale update rule converge to a local Nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Learnable explicit density for continuous latent space and variational inference
Huang, C.-W., Touati, A., Dinh, L., Drozdzal, M., Havaei, M., Charlin, L., and Courville, A · 2017
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Kaiser, L., Gomez, A. N., Shazeer, N., Vaswani, A., Parmar, N., Jones, L., and Uszkoreit, J · 2017
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Meta-sgd: Learning to learn quickly for few-shot learning
Li, Z., Zhou, F., Chen, F., and Li, H · 2017
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Searching for activation functions
Ramachandran, P., Zoph, B., and Le, Q. V · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Antoniou, A., Edwards, H., and Storkey, A · 2018
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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
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Spherical cnns
Cohen, T. S., Geiger, M., Köhler, J., and Welling, M · 2018
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Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
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Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., and Courville, A · 2018
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Uniter: Learning universal image-text representations
Chen, Y.-C., Li, L., Yu, L., El Kholy, A., Ahmed, F., Gan, Z., Cheng, Y., and Liu, J · 2019
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Learning implicit fields for generative shape modeling
Chen, Z. and Zhang, H · 2019
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Neural spline flows
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G · 2019
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Convolutional conditional neural processes
Gordon, J., Bruinsma, W. P., Foong, A. Y., Requeima, J., Dubois, Y., and Turner, R. E · 2019
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ERA5 monthly averaged data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N · 2019
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Attentive neural processes
Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, A., Rosenbaum, D., Vinyals, O., and Teh, Y. W · 2019
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Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
Barron, J. T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., and Srinivasan, P. P · 2021
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Nerv: Neural representations for videos
Chen, H., He, B., Wang, H., Ren, Y., Lim, S. N., and Shrivastava, A · 2021
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Unconstrained scene generation with locally conditioned radiance fields
DeVries, T., Bautista, M. A., Srivastava, N., Taylor, G. W., and Susskind, J. M · 2021
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Learning signal-agnostic manifolds of neural fields
Du, Y., Collins, M. K., Tenenbaum, B. J., and Sitzmann, V · 2021
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Implicit 2 \text{Implicit}^{2} : Implicit layers for implicit representations
Huang, Z., Bai, S., and Kolter, J. Z · 2021
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Lu, J. and Kumar, M. P · 2019
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Occupancy networks: Learning 3d reconstruction in function space
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., and Geiger, A · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Park, J. J., Florence, P., Straub, J., Newcombe, R., and Lovegrove, S · 2019
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Generating diverse high-fidelity images with vq-vae-2
Razavi, A., van den Oord, A., and Vinyals, O · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Sitzmann, V., Zollhöfer, M., and Wetzstein, G · 2019
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Xiao, Z., Yan, Q., and Amit, Y · 2019
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Fast context adaptation via meta-learning
Zintgraf, L., Shiarli, K., Kurin, V., Hofmann, K., and Whiteson, S · 2019
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Jaeckle, F. and Kumar, M. P · 2021
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Parameter prediction for unseen deep architectures
Knyazev, B., Drozdzal, M., Taylor, G. W., and Romero Soriano, A · 2021
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Nerf-vae: A geometry aware 3d scene generative model
Kosiorek, A. R., Strathmann, H., Zoran, D., Moreno, P., Schneider, R., Mokrá, S., and Rezende, D. J · 2021
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Neural scene flow fields for space-time view synthesis of dynamic scenes
Li, Z., Niklaus, S., Snavely, N., and Wang, O · 2021
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Acorn: Adaptive coordinate networks for neural scene representation
Martel, J. N., Lindell, D. B., Lin, C. Z., Chan, E. R., Monteiro, M., and Wetzstein, G · 2021
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Modulated periodic activations for generalizable local functional representations, 2021
Mehta, I., Gharbi, M., Barnes, C., Shechtman, E., Ramamoorthi, R., and Chandraker, M · 2021
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Symbolic music generation with diffusion models
Mittal, G., Engel, J., Hawthorne, C., and Simon, I · 2021
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Generating images with sparse representations
Nash, C., Menick, J., Dieleman, S., and Battaglia, P. W · 2021
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GIRAFFE: Representing scenes as compositional generative neural feature fields
Niemeyer, M. and Geiger, A · 2021
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Terminerf: Ray termination prediction for efficient neural rendering
Piala, M. and Clark, R · 2021
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Beyond periodicity: Towards a unifying framework for activations in coordinate-mlps
Ramasinghe, S. and Lucey, S · 2021
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Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps
Reiser, C., Peng, S., Liao, Y., and Geiger, A · 2021
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Self-supervised representation learning on neural network weights for model characteristic prediction
Schürholt, K., Kostadinov, D., and Borth, D · 2021
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D2c: Diffusion-denoising models for few-shot conditional generation
Sinha, A., Song, J., Meng, C., and Ermon, S · 2021
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Adversarial generation of continuous images
Skorokhodov, I., Ignatyev, S., and Elhoseiny, M · 2021
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Implicit neural representations for image compression
Strümpler, Y., Postels, J., Yang, R., Van Gool, L., and Tombari, F · 2021
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Learned initializations for optimizing coordinate-based neural representations
Tancik, M., Mildenhall, B., Wang, T., Schmidt, D., Srinivasan, P. P., Barron, J. T., and Ng, R · 2021
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Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J · 2021
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Diffusion priors in variational autoencoders
Wehenkel, A. and Louppe, G · 2021
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Plenoctrees for real-time rendering of neural radiance fields
Yu, A., Li, R., Tancik, M., Li, H., Ng, R., and Kanazawa, A · 2021
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Implicit neural video compression
Zhang, Y., van Rozendaal, T., Brehmer, J., Nagel, M., and Cohen, T · 2021
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