Fetching the paper…
Reading the bibliography…
Deep Neural Networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data.
1902
Earlier work this paper cites.
1903
Earlier work this paper cites.
1906
Earlier work this paper cites.
1909
Earlier work this paper cites.
1911
Earlier work this paper cites.
Fukushima K (1980) Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36:193–202
1980
Earlier work this paper cites.
LeCun Y, Boser BE, Denker JS, et al (1989) Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems 2, [NIPS Conference, Denver, Colorado, USA, November 27-30, 1989], pp 396–404
1989
Earlier work this paper cites.
LeCun Y, Boser B, Denker JS, et al (1990) Handwritten digit recognition with a back-propagation network. In: Touretzky D (ed) Advances in Neural Information Processing Systems (NIPS 1989), vol 2. Morgan Kaufman, Denver, CO
1989
Earlier work this paper cites.
Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13(9):891–906
1991
Earlier work this paper cites.
Schulz-Mirbach H (1992) On the existence of complete invariant feature spaces in pattern recognition. In: Pattern Recognition: Eleventh International Conference 1992, pp 178 – 182
1992
Earlier work this paper cites.
Schulz-Mirbach H (1994) Algorithms for the construction of invariant features. In: Tagungsband Mustererkennung 1994 (16. DAGM Symposium), Reihe Informatik Xpress, Nr.5, pp 324–332
1994
Earlier work this paper cites.
Schulz-Mirbach H (1995) Invariant features for gray scale images. In: Mustererkennung 1995, 17. DAGM-Symposium, Bielefeld, 13.-15. September 1995, Proceedings, pp 1–14
1995
Earlier work this paper cites.
2002
Earlier work this paper cites.
2004
Earlier work this paper cites.
2006
Earlier work this paper cites.
Larochelle H, Erhan D, Courville AC, et al (2007) An empirical evaluation of deep architectures on problems with many factors of variation. In: Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007, pp 473–480
2007
Earlier work this paper cites.
2010
Earlier work this paper cites.
Coates A, Ng AY, Lee H (2011) An analysis of single-layer networks in unsupervised feature learning. In: Gordon GJ, Dunson DB, Dudík M (eds) Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April 11-13, 2011, JMLR Proceedings, vol 15. JMLR.org, pp 215–223, URL http://proceedings.mlr.press/v15/coates11a/coates11a.pdf
2011
Earlier work this paper cites.
Hinton GE, Krizhevsky A, Wang SD (2011) Transforming auto-encoders. In: Honkela T, Duch W, Girolami MA, et al (eds) Artificial Neural Networks and Machine Learning - ICANN 2011 - 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I, Lecture Notes in Computer Science, vol 6791. Springer, pp 44–51
2011
Earlier work this paper cites.
Müller F, Mertins A (2011) Contextual invariant-integration features for improved speaker-independent speech recognition. Speech Communication 53(6):830–841
2011
Earlier work this paper cites.
Condurache AP, Mertins A (2012) Sparse representations and invariant sequence-feature extraction for event detection. VISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications 1
2012
Earlier work this paper cites.
Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? the kitti vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition (CVPR)
2012
Earlier work this paper cites.
Mallat S (2012) Group invariant scattering. Communications on Pure and Applied Mathematics 65
2012
Earlier work this paper cites.
Sohn K, Lee H (2012) Learning invariant representations with local transformations. In: Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012. icml.cc / Omnipress
2012
Earlier work this paper cites.
Bruna J, Mallat S (2013) Invariant scattering convolution networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8):1872–1886
2013
Earlier work this paper cites.
Sifre L, Mallat S (2013) Rotation, scaling and deformation invariant scattering for texture discrimination. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23-28, 2013, pp 1233–1240
2013
Earlier work this paper cites.
Tieleman T (2013) The affnist dataset URL http://www.cs.toronto.edu/~tijmen/affNIST
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
Gens R, Domingos PM (2014) Deep symmetry networks. In: Ghahramani Z, Welling M, Cortes C, et al (eds) Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pp 2537–2545
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Oyallon E, Mallat S, Sifre L (2014) Generic deep networks with wavelet scattering. In: Bengio Y, LeCun Y (eds) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Workshop Track Proceedings
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Hall BC (2015) Lie Groups, Lie Algebras, and Representations, 2nd edn. Springer International Publishing
2015
Earlier work this paper cites.
LeCun Y, Bengio Y, Hinton GE (2015) Deep learning. Nature 521(7553):436–444
2015
Earlier work this paper cites.
Oyallon E, Mallat S (2015) Deep roto-translation scattering for object classification. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp 2865–2873
2015
Earlier work this paper cites.
Cohen T, Welling M (2016) Group equivariant convolutional networks. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pp 2990–2999
2016
Earlier work this paper cites.
Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Lee DD, Sugiyama M, von Luxburg U, et al (eds) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pp 3837–3845, URL https://proceedings.neurips.cc/paper/2016/hash/04df4d434d481c5bb723be1b6df1ee65-Abstract.html
2016
Earlier work this paper cites.
Laptev D, Savinov N, Buhmann JM, et al (2016) TI-POOLING: transformation-invariant pooling for feature learning in convolutional neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pp 289–297
2016
Earlier work this paper cites.
Zagoruyko S, Komodakis N (2016) Wide residual networks. In: Wilson RC, Hancock ER, Smith WAP (eds) Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK, September 19-22, 2016. BMVA Press, URL http://www.bmva.org/bmvc/2016/papers/paper087/index.html
2016
Earlier work this paper cites.
Cohen TS, Welling M (2017) Steerable cnns. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net
2017
Earlier work this paper cites.
Cotter F, Kingsbury NG (2017) Visualizing and improving scattering networks. In: 27th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017, Tokyo, Japan, September 25-28, 2017, pp 1–6
2017
Earlier work this paper cites.
Henriques JF, Vedaldi A (2017) Warped convolutions: Efficient invariance to spatial transformations. In: Precup D, Teh YW (eds) Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, Proceedings of Machine Learning Research, vol 70. PMLR, pp 1461–1469
2017
Earlier work this paper cites.
Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, URL https://openreview.net/forum?id=SJU4ayYgl
2017
Earlier work this paper cites.
Marcos D, Volpi M, Komodakis N, et al (2017) Rotation equivariant vector field networks. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017. IEEE Computer Society, pp 5058–5067
2017
Earlier work this paper cites.
Oyallon E, Belilovsky E, Zagoruyko S (2017) Scaling the scattering transform: Deep hybrid networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp 5619–5628
2017
Earlier work this paper cites.
Ravanbakhsh S, Schneider JG, Póczos B (2017) Equivariance through parameter-sharing. In: Precup D, Teh YW (eds) Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, Proceedings of Machine Learning Research, vol 70. PMLR, pp 2892–2901, URL http://proceedings.mlr.press/v70/ravanbakhsh17a.html
2017
Earlier work this paper cites.
Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA, pp 3856–3866
2017
Earlier work this paper cites.
Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In: Guyon I, von Luxburg U, Bengio S, et al (eds) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp 5998–6008, URL https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
2017
Earlier work this paper cites.
Worrall DE, Garbin SJ, Turmukhambetov D, et al (2017) Harmonic networks: Deep translation and rotation equivariance. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pp 7168–7177
2017
Earlier work this paper cites.
Bekkers EJ, Lafarge MW, Veta M, et al (2018) Roto-translation covariant convolutional networks for medical image analysis. In: Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I, pp 440–448
2018
Cited alongside, same era.
Cohen TS, Geiger M, Köhler J, et al (2018) Spherical cnns. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings
2018
Cited alongside, same era.
Coors B, Condurache A, Mertins A, et al (2018) Learning transformation invariant representations with weak supervision. In: International Conference on Computer Vision Theory and Applications
2018
Cited alongside, same era.
2018
2021
Closest in time.
Dehmamy N, Walters R, Liu Y, et al (2021) Automatic symmetry discovery with lie algebra convolutional network. In: Ranzato M, Beygelzimer A, Dauphin YN, et al (eds) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pp 2503–2515, URL https://proceedings.neurips.cc/paper/2021/hash/148148d62be67e0916a833931bd32b26-Abstract.html
2021
Closest in time.
Dey N, Chen A, Ghafurian S (2021) Group equivariant generative adversarial networks. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, URL https://openreview.net/forum?id=rgFNuJHHXv
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Esteves C, Allen-Blanchette C, Makadia A, et al (2018a) Learning SO(3) equivariant representations with spherical cnns. In: Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIII, pp 54–70
2018
Cited alongside, same era.
Hinton GE, Sabour S, Frosst N (2018) Matrix capsules with EM routing. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings
2018
Cited alongside, same era.
Hoogeboom E, Peters JWT, Cohen TS, et al (2018) Hexaconv. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net, URL https://openreview.net/forum?id=r1vuQG-CW
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Kondor R, Trivedi S (2018) On the generalization of equivariance and convolution in neural networks to the action of compact groups. In: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, pp 2752–2760
2018
Cited alongside, same era.
Kondor R, Lin Z, Trivedi S (2018) Clebsch-gordan nets: a fully fourier space spherical convolutional neural network. In: Bengio S, Wallach HM, Larochelle H, et al (eds) Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada, pp 10,138–10,147
2018
Cited alongside, same era.
Lenssen JE, Fey M, Libuschewski P (2018) Group equivariant capsule networks. In: Bengio S, Wallach HM, Larochelle H, et al (eds) Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada, pp 8858–8867
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Elesedy B, Zaidi S (2021) Provably strict generalisation benefit for equivariant models. In: Meila M, Zhang T (eds) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, Proceedings of Machine Learning Research, vol 139. PMLR, pp 2959–2969, URL http://proceedings.mlr.press/v139/elesedy21a.html
2021
Closest in time.
Finzi M, Benton G, Wilson AG (2021a) Residual pathway priors for soft equivariance constraints. In: Ranzato M, Beygelzimer A, Dauphin YN, et al (eds) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pp 30,037–30,049, URL https://proceedings.neurips.cc/paper/2021/hash/fc394e9935fbd62c8aedc372464e1965-Abstract.html
2021
Closest in time.
Franzen D, Wand M (2021) General nonlinearities in so(2)-equivariant cnns. In: Ranzato M, Beygelzimer A, Dauphin Y, et al (eds) Advances in Neural Information Processing Systems, vol 34. Curran Associates, Inc., pp 9086–9098, URL https://proceedings.neurips.cc/paper/2021/file/4bfbd52f4e8466dc12aaf30b7e057b66-Paper.pdf
2021
Closest in time.
Fuchs FB, Wagstaff E, Dauparas J, et al (2021) Iterative se(3)-transformers. In: Nielsen F, Barbaresco F (eds) Geometric Science of Information - 5th International Conference, GSI 2021, Paris, France, July 21-23, 2021, Proceedings, Lecture Notes in Computer Science, vol 12829. Springer, pp 585–595, 10.1007/978-3-030-80209-7_63 , URL https://doi.org/10.1007/978-3-030-80209-7_63
2021
Closest in time.
de Haan P, Weiler M, Cohen T, et al (2021) Gauge equivariant mesh cnns: Anisotropic convolutions on geometric graphs. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, URL https://openreview.net/forum?id=Jnspzp-oIZE
2021
Closest in time.
He L, Chen Y, Shen Z, et al (2021a) Efficient equivariant network. In: Ranzato M, Beygelzimer A, Dauphin YN, et al (eds) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pp 5290–5302, URL https://proceedings.neurips.cc/paper/2021/hash/2a79ea27c279e471f4d180b08d62b00a-Abstract.html
2021
Closest in time.
He L, Dong Y, Wang Y, et al (2021b) Gauge equivariant transformer. In: Ranzato M, Beygelzimer A, Dauphin YN, et al (eds) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pp 27,331–27,343, URL https://proceedings.neurips.cc/paper/2021/hash/e57c6b956a6521b28495f2886ca0977a-Abstract.html
2021
Closest in time.
Holderrieth P, Hutchinson M, Teh YW (2021) Equivariant learning of stochastic fields: Gaussian processes and steerable conditional neural processes. In: Meila M, Zhang T (eds) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, Proceedings of Machine Learning Research, vol 139. PMLR, pp 4297–4307, URL http://proceedings.mlr.press/v139/holderrieth21a.html
2021
Closest in time.
Horie M, Morita N, Hishinuma T, et al (2021) Isometric transformation invariant and equivariant graph convolutional networks. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, URL https://openreview.net/forum?id=FX0vR39SJ5q
2021
Closest in time.
Hutchinson M, Lan CL, Zaidi S, et al (2021) Lietransformer: Equivariant self-attention for lie groups. In: Meila M, Zhang T (eds) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, Proceedings of Machine Learning Research, vol 139. PMLR, pp 4533–4543, URL http://proceedings.mlr.press/v139/hutchinson21a.html
2021
Closest in time.
Kawano M, Kumagai W, Sannai A, et al (2021) Group equivariant conditional neural processes. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, URL https://openreview.net/forum?id=e8W-hsu_q5
2021
Closest in time.
Lang L, Weiler M (2021) A wigner-eckart theorem for group equivariant convolution kernels. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, URL https://openreview.net/forum?id=ajOrOhQOsYx
2021
Closest in time.
2021
Closest in time.
Romero DW, Cordonnier J (2021) Group equivariant stand-alone self-attention for vision. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, URL https://openreview.net/forum?id=JkfYjnOEo6M
2021
Closest in time.
2021
Closest in time.
Satorras VG, Hoogeboom E, Welling M (2021) E(n) equivariant graph neural networks. In: Meila M, Zhang T (eds) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, Proceedings of Machine Learning Research, vol 139. PMLR, pp 9323–9332, URL http://proceedings.mlr.press/v139/satorras21a.html
2021
Closest in time.
Shakerinava M, Ravanbakhsh S (2021) Equivariant networks for pixelized spheres. In: Meila M, Zhang T (eds) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, Proceedings of Machine Learning Research, vol 139. PMLR, pp 9477–9488, URL http://proceedings.mlr.press/v139/shakerinava21a.html
2021
Closest in time.
Sosnovik I, Moskalev A, Smeulders AWM (2021b) Scale equivariance improves siamese tracking. In: IEEE Winter Conference on Applications of Computer Vision, WACV 2021, Waikoloa, HI, USA, January 3-8, 2021. IEEE, pp 2764–2773, 10.1109/WACV48630.2021.00281 , URL https://doi.org/10.1109/WACV48630.2021.00281
2021
Closest in time.
Walters R, Li J, Yu R (2021) Trajectory prediction using equivariant continuous convolution. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, URL https://openreview.net/forum?id=J8_GttYLFgr
2021
Closest in time.
Xu J, Kim H, Rainforth T, et al (2021) Group equivariant subsampling. In: Ranzato M, Beygelzimer A, Dauphin YN, et al (eds) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pp 5934–5946, URL https://proceedings.neurips.cc/paper/2021/hash/2ea6241cf767c279cf1e80a790df1885-Abstract.html
2021
Closest in time.
Zhou A, Knowles T, Finn C (2021) Meta-learning symmetries by reparameterization. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, URL https://openreview.net/forum?id=-QxT4mJdijq
2021
Closest in time.
Zhu X, Xu C, Tao D (2021) Commutative lie group VAE for disentanglement learning. In: Meila M, Zhang T (eds) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, Proceedings of Machine Learning Research, vol 139. PMLR, pp 12,924–12,934, URL http://proceedings.mlr.press/v139/zhu21f.html
2021
Closest in time.
2022
Closest in time.
Bardes A, Ponce J, LeCun Y (2022) Vicreg: Variance-invariance-covariance regularization for self-supervised learning. In: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, URL https://openreview.net/forum?id=xm6YD62D1Ub
2022
Closest in time.
2022
Closest in time.
Cesa G, Lang L, Weiler M (2022) A program to build e(n)-equivariant steerable cnns. In: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, URL https://openreview.net/forum?id=WE4qe9xlnQw
2022
Closest in time.
Chen D, Krähenbühl P (2022) Learning from all vehicles. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022. IEEE, pp 17,201–17,210, 10.1109/CVPR52688.2022.01671 , URL https://doi.org/10.1109/CVPR52688.2022.01671
2022
Closest in time.
2022
Closest in time.
Gauthier S, Thérien B, Alsène-Racicot L, et al (2022) Parametric scattering networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022. IEEE, pp 5739–5748, 10.1109/CVPR52688.2022.00566 , URL https://doi.org/10.1109/CVPR52688.2022.00566
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Kvinge H, Emerson T, Jorgenson G, et al (2022) In what ways are deep neural networks invariant and how should we measure this? In: Oh AH, Agarwal A, Belgrave D, et al (eds) Advances in Neural Information Processing Systems, URL https://openreview.net/forum?id=SCD0hn3kMHw
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Rath M, Condurache AP (2022) Improving the sample-complexity of deep classification networks with invariant integration. Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022)
2022
Closest in time.
2022
Closest in time.
Shakerinava M, Mondal AK, Ravanbakhsh S (2022) Structuring representations using group invariants. In: Oh AH, Agarwal A, Belgrave D, et al (eds) Advances in Neural Information Processing Systems, URL https://openreview.net/forum?id=vWUmBjin_-o
2022
Closest in time.
2022
Closest in time.
Rath M, Condurache AP (2023) Deep neural networks with efficient guaranteed invariances. 26th International Conference on Artificial Intelligence and Statistics, AISTATS 2023, Valencia, Spain, April 25-27, 2023
2023
Closest in time.
Jaderberg M, Simonyan K, Zisserman A, et al (2015) Spatial transformer networks. In: Advances in Neural Information Processing Systems 28. Curran Associates, Inc., p 2017–2025
2025
Closest in time.