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This work studies the design of neural networks that can process the weights or gradients of other neural networks, which we refer to as neural functional networks (NFNs).
A new measure of rank correlation
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Evolution and design of distributed learning rules
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Learning multiple layers of features from tiny images
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Mnist handwritten digit database
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On the optimization of a synaptic learning rule
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Auto-encoding variational bayes
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ShapeNet: An Information-Rich 3D Model Repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Open source computer vision library
Itseez · 2015
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Learning structured output representation using deep conditional generative models
K. Sohn, H. Lee, and X. Yan · 2015
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Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, B. Shillingford, and N. De Freitas · 2016
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Group equivariant convolutional networks
T. Cohen and M. Welling · 2016
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D. Ha, A. Dai, and Q. V. Le · 2016
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K. Li and J. Malik · 2016
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Pointnet: deep learning on point sets for 3d classification and segmentation. cvpr (2017)
C. Qi, H. Su, K. Mo, and L. Guibas · 2016
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner · 2017
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D. Krueger, C.-W. Huang, R. Islam, R. Turner, A. Lacoste, and A. Courville · 2017
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Equivariance through parameter-sharing
S. Ravanbakhsh, J. Schneider, and B. Poczos · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Deep sets. doi: 10.48550
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. Salakhutdinov, and A. Smola · 2017
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T. S. Cohen, M. Geiger, J. Köhler, and M. Welling · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
J. Frankle and M. Carbin · 2018
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Loss surfaces, mode connectivity, and fast ensembling of DNNs
A. Sinitsin, V. Plokhotnyuk, D. Pyrkin, S. Popov, and A. Babenko · 2020
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Implicit neural representations with periodic activation functions
V. Sitzmann, J. Martel, A. Bergman, D. Lindell, and G. Wetzstein · 2020
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Optimizing mode connectivity via neuron alignment
N. Tatro, P.-Y. Chen, P. Das, I. Melnyk, P. Sattigeri, and R. Lai · 2020
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E. H. Thiede, T. S. Hy, and R. Kondor · 2020
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Predicting neural network accuracy from weights
T. Unterthiner, D. Keysers, S. Gelly, O. Bousquet, and I. Tolstikhin · 2020
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T. Garipov, P. Izmailov, D. Podoprikhin, D. P. Vetrov, and A. G. Wilson · 2018
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Deep models of interactions across sets
J. Hartford, D. Graham, K. Leyton-Brown, and S. Ravanbakhsh · 2018
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On the generalization of equivariance and convolution in neural networks to the action of compact groups
R. Kondor and S. Trivedi · 2018
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Graph hypernetworks for neural architecture search
C. Zhang, M. Ren, and R. Urtasun · 2018
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J. Brea, B. Simsek, B. Illing, and W. Gerstner · 2019
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Learning implicit fields for generative shape modeling
Z. Chen and H. Zhang · 2019
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A generative model for sampling high-performance and diverse weights for neural networks
L. Deutsch, E. Nijkamp, and Y. Yang · 2019
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
M. M. Bronstein, J. Bruna, T. Cohen, and P. Veličković · 2021
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Editing factual knowledge in language models
N. De Cao, W. Aziz, and I. Titov · 2021
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Generative models as distributions of functions
E. Dupont, Y. W. Teh, and A. Doucet · 2021
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The role of permutation invariance in linear mode connectivity of neural networks
R. Entezari, H. Sedghi, O. Saukh, and B. Neyshabur · 2021
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A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups
M. Finzi, M. Welling, and A. G. Wilson · 2021
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Methods and analysis of the first competition in predicting generalization of deep learning
Y. Jiang, P. Natekar, M. Sharma, S. K. Aithal, D. Kashyap, N. Subramanyam, C. Lassance, D. M. Roy, G. K. Dziugaite, S. Gunasekar, et al · 2021
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Parameter prediction for unseen deep architectures
B. Knyazev, M. Drozdzal, G. W. Taylor, and A. Romero Soriano · 2021
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Implicit self-regularization in deep neural networks: Evidence from random matrix theory and implications for learning
C. H. Martin and M. W. Mahoney · 2021
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E. Mitchell, C. Lin, A. Bosselut, C. Finn, and C. D. Manning · 2021
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Git re-basin: Merging models modulo permutation symmetries
S. K. Ainsworth, J. Hayase, and S. Srinivasa · 2022
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From data to functa: Your data point is a function and you should treat it like one
E. Dupont, H. Kim, S. Eslami, D. Rezende, and D. Rosenbaum · 2022
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Velo: Training versatile learned optimizers by scaling up
L. Metz, J. Harrison, C. D. Freeman, A. Merchant, L. Beyer, J. Bradbury, N. Agrawal, B. Poole, I. Mordatch, A. Roberts, et al · 2022
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Learning to learn with generative models of neural network checkpoints
W. Peebles, I. Radosavovic, T. Brooks, A. A. Efros, and J. Malik · 2022
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Einops: Clear and reliable tensor manipulations with einstein-like notation
A. Rogozhnikov · 2022
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A fine-tuning approach to belief state modeling
S. Sokota, H. Hu, D. J. Wu, J. Z. Kolter, J. N. Foerster, and N. Brown · 2022
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Deep learning on implicit neural representations of shapes
L. De Luigi, A. Cardace, R. Spezialetti, P. Zama Ramirez, S. Salti, and L. Di Stefano · 2023
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Equivariant architectures for learning in deep weight spaces
A. Navon, A. Shamsian, I. Achituve, E. Fetaya, G. Chechik, and H. Maron · 2023
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