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Designing machine learning architectures for processing neural networks in their raw weight matrix form is a newly introduced research direction.
On the algebraic structure of feedforward network weight spaces
Hecht-Nielsen, R · 1990
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Approximation capabilities of multilayer feedforward networks
Hornik, K · 1991
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On the geometry of feedforward neural network error surfaces
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Representation theory and invariant neural networks
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Gradient-based learning applied to document recognition
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Learning multiple layers of features from tiny images
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Representation theory: a first course , volume 129
Fulton, W. and Harris, J · 2013
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A method for finding similarity between multi-layer perceptrons by forward bipartite alignment
Ashmore, S. and Gashler, M · 2015
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Understanding symmetries in deep networks
Badrinarayanan, V., Mishra, B., and Cipolla, R · 2015
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Path-sgd: Path-normalized optimization in deep neural networks
Neyshabur, B., Salakhutdinov, R. R., and Srebro, N · 2015
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Group equivariant convolutional networks
Cohen, T. and Welling, M · 2016
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Binarized neural networks
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., and Bengio, Y · 2016
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Ntire 2017 challenge on single image super-resolution: Dataset and study
Agustsson, E. and Timofte, R · 2017
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Steerable cnns
Cohen, T. S. and Welling, M · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
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Equivariance through parameter-sharing
Ravanbakhsh, S., Schneider, J., and Poczos, B · 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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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Deep learning using rectified linear units (relu)
Agarap, A. F · 2018
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Accelerating neural architecture search using performance prediction
Baker, B., Gupta, O., Raskar, R., and Naik, N · 2018
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Spherical cnns
Cohen, T. S., Geiger, M., Köhler, J., and Welling, M · 2018
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Learning so (3) equivariant representations with spherical cnns
Esteves, C., Allen-Blanchette, C., Makadia, A., and Daniilidis, K · 2018
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Deep models of interactions across sets
Hartford, J., Graham, D., Leyton-Brown, K., and Ravanbakhsh, S · 2018
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On the generalization of equivariance and convolution in neural networks to the action of compact groups
Kondor, R. and Trivedi, S · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2018
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Incidence networks for geometric deep learning
Albooyeh, M., Bertolini, D., and Ravanbakhsh, S · 2019
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Equivariant networks for hierarchical structures
Wang, R., Albooyeh, M., and Ravanbakhsh, S · 2020
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Analytic study of families of spurious minima in two-layer relu neural networks: a tale of symmetry ii
Arjevani, Y. and Field, M · 2021
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Expressive power of invariant and equivariant graph neural networks
Azizian, W. and Lelarge, M · 2021
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Bronstein, M. M., Bruna, J., Cohen, T., and Veličković, P · 2021
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Provably strict generalisation benefit for equivariant models
Elesedy, B. and Zaidi, S · 2021
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The role of permutation invariance in linear mode connectivity of neural networks
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Brea, J., Simsek, B., Illing, B., and Gerstner, W · 2019
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Principled weight initialization for hypernetworks
Chang, O., Flokas, L., and Lipson, H · 2019
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Universal invariant and equivariant graph neural networks
Keriven, N. and Peyré, G · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., and Teh, Y. W · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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Neural network branching for neural network verification
Lu, J. and Kumar, M. P · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 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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Entezari, R., Sedghi, H., Saukh, O., and Neyshabur, B · 2021
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A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups
Finzi, M., Welling, M., and Wilson, A. G · 2021
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Generating adversarial examples with graph neural networks
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: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., and Ng, R · 2021
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Weisfeiler and leman go machine learning: The story so far
Morris, C., Lipman, Y., Maron, H., Rieck, B., Kriege, N. M., Grohe, M., Fey, M., and Borgwardt, K · 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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Geometry of the loss landscape in overparameterized neural networks: Symmetries and invariances
Simsek, B., Ged, F., Jacot, A., Spadaro, F., Hongler, C., Gerstner, W., and Brea, J · 2021
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Git re-basin: Merging models modulo permutation symmetries
Ainsworth, S. K., Hayase, J., and Srinivasa, S · 2022
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From data to functa: Your data point is a function and you can treat it like one
Dupont, E., Kim, H., Eslami, S. A., Rezende, D. J., and Rosenbaum, D · 2022
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On the symmetries of deep learning models and their internal representations
Godfrey, C., Brown, D., Emerson, T., and Kvinge, H · 2022
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Sign and basis invariant networks for spectral graph representation learning
Lim, D., Robinson, J., Zhao, L., Smidt, T., Sra, S., Maron, H., and Jegelka, S · 2022
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Federated learning with heterogeneous architectures using graph hypernetworks
Litany, O., Maron, H., Acuna, D., Kautz, J., Chechik, G., and Fidler, S · 2022
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Learning to learn with generative models of neural network checkpoints
Peebles, W., Radosavovic, I., Brooks, T., Efros, A. A., and Malik, J · 2022
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Re-basin via implicit sinkhorn differentiation
Peña, F. A. G., Medeiros, H. R., Dubail, T., Aminbeidokhti, M., Granger, E., and Pedersoli, M · 2022
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Wang, G., Wang, G., Liang, W., and Lai, J · 2022
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Signal processing for implicit neural representations
Xu, D., Wang, P., Jiang, Y., Fan, Z., and Wang, Z · 2022
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Deep learning on implicit neural representations of shapes
Luigi, L. D., Cardace, A., Spezialetti, R., Ramirez, P. Z., Salti, S., and di Stefano, L · 2023
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