projUNN: efficient method for training deep networks with unitary matrices
暂无分享,去创建一个
[1] Albert Gu,et al. Efficiently Modeling Long Sequences with Structured State Spaces , 2021, ICLR.
[2] A. Dienes,et al. Implicit Bias of Linear Equivariant Networks , 2021, ICML.
[3] Quynh T. Nguyen,et al. Quantum algorithms for group convolution, cross-correlation, and equivariant transformations , 2021, Physical Review A.
[4] Sahil Singla,et al. Skew Orthogonal Convolutions , 2021, ICML.
[5] J. Z. Kolter,et al. Orthogonalizing Convolutional Layers with the Cayley Transform , 2021, ICLR.
[6] M. Cerezo,et al. Variational quantum algorithms , 2020, Nature Reviews Physics.
[7] Omri Azencot,et al. Lipschitz Recurrent Neural Networks , 2020, ICLR.
[8] James M. Rehg,et al. Orthogonal Over-Parameterized Training , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Dacheng Tao,et al. Orthogonal Deep Neural Networks , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[10] Michael W. Mahoney,et al. Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning , 2018, J. Mach. Learn. Res..
[11] Eric R. Anschuetz. Critical Points in Hamiltonian Agnostic Variational Quantum Algorithms , 2021 .
[12] Seth Lloyd,et al. Quantum Earth Mover's Distance: A New Approach to Learning Quantum Data , 2021, ArXiv.
[13] C. Ré,et al. HiPPO: Recurrent Memory with Optimal Polynomial Projections , 2020, NeurIPS.
[14] Sridhar Swaminathan,et al. Sparse low rank factorization for deep neural network compression , 2020, Neurocomputing.
[15] Rainer Engelken,et al. Lyapunov spectra of chaotic recurrent neural networks , 2020, Physical Review Research.
[16] Jakub M. Tomczak,et al. The Convolution Exponential and Generalized Sylvester Flows , 2020, NeurIPS.
[17] Ling Shao,et al. Controllable Orthogonalization in Training DNNs , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Ievgeniia Oshurko. Quantum Machine Learning , 2020, Quantum Computing.
[19] T. Osborne,et al. Training deep quantum neural networks , 2020, Nature Communications.
[20] Jun Li,et al. Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform , 2020, ICLR.
[21] Seth Lloyd,et al. Learning Unitaries by Gradient Descent , 2020, ArXiv.
[22] Akira Sone,et al. Cost-Function-Dependent Barren Plateaus in Shallow Quantum Neural Networks , 2020, ArXiv.
[23] Stella X. Yu,et al. Orthogonal Convolutional Neural Networks , 2019, Computer Vision and Pattern Recognition.
[24] A. Prakash,et al. Quantum Algorithms for Deep Convolutional Neural Networks , 2019, ICLR.
[25] Seth Lloyd,et al. Quantum-inspired algorithms in practice , 2019, Quantum.
[26] Iordanis Kerenidis,et al. Quantum Algorithms for Feedforward Neural Networks , 2018, ACM Transactions on Quantum Computing.
[27] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[28] Cem Anil,et al. Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks , 2019, NeurIPS.
[29] Chris Eliasmith,et al. Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks , 2019, NeurIPS.
[30] Sanjeev Arora,et al. Implicit Regularization in Deep Matrix Factorization , 2019, NeurIPS.
[31] Joan Bruna,et al. Approximating Orthogonal Matrices with Effective Givens Factorization , 2019, ICML.
[32] Martin Jaggi,et al. PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization , 2019, NeurIPS.
[33] Ed H. Chi,et al. AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks , 2019, ICLR.
[34] Mario Lezcano Casado,et al. Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group , 2019, ICML.
[35] Ying Li,et al. Theory of variational quantum simulation , 2018, Quantum.
[36] Seth Lloyd,et al. Continuous-variable quantum neural networks , 2018, Physical Review Research.
[37] Philip M. Long,et al. The Singular Values of Convolutional Layers , 2018, ICLR.
[38] S. Brierley,et al. Accelerated Variational Quantum Eigensolver. , 2018, Physical review letters.
[39] Xiaohan Chen,et al. Can We Gain More from Orthogonality Regularizations in Training Deep CNNs? , 2018, NeurIPS.
[40] Jascha Sohl-Dickstein,et al. Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks , 2018, ICML.
[41] Carla P. Gomes,et al. Understanding Batch Normalization , 2018, NeurIPS.
[42] H. Neven,et al. Barren plateaus in quantum neural network training landscapes , 2018, Nature Communications.
[43] Walter Vinci,et al. Quantum variational autoencoder , 2018, Quantum Science and Technology.
[44] Yi Zhang,et al. Stronger generalization bounds for deep nets via a compression approach , 2018, ICML.
[45] Hongyang Zhang,et al. Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations , 2017, COLT.
[46] Qiang Ye,et al. Orthogonal Recurrent Neural Networks with Scaled Cayley Transform , 2017, ICML.
[47] Nathan Srebro,et al. Implicit Regularization in Matrix Factorization , 2017, 2018 Information Theory and Applications Workshop (ITA).
[48] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[49] Dacheng Tao,et al. On Compressing Deep Models by Low Rank and Sparse Decomposition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Yann LeCun,et al. Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs , 2016, ICML.
[51] James Bailey,et al. Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections , 2016, ICML.
[52] Gunnar Rätsch,et al. Learning Unitary Operators with Help From u(n) , 2016, AAAI.
[53] Les E. Atlas,et al. Full-Capacity Unitary Recurrent Neural Networks , 2016, NIPS.
[54] Yann LeCun,et al. Recurrent Orthogonal Networks and Long-Memory Tasks , 2016, ICML.
[55] Justin K. Romberg,et al. An Overview of Low-Rank Matrix Recovery From Incomplete Observations , 2016, IEEE Journal of Selected Topics in Signal Processing.
[56] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[57] Yoshua Bengio,et al. Unitary Evolution Recurrent Neural Networks , 2015, ICML.
[58] Roberto Cipolla,et al. Training CNNs with Low-Rank Filters for Efficient Image Classification , 2015, ICLR.
[59] Xiaogang Wang,et al. Convolutional neural networks with low-rank regularization , 2015, ICLR.
[60] Juha Karhunen,et al. Bidirectional Recurrent Neural Networks as Generative Models , 2015, NIPS.
[61] M. Hastings,et al. Progress towards practical quantum variational algorithms , 2015, 1507.08969.
[62] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[63] Maria Schuld,et al. The quest for a Quantum Neural Network , 2014, Quantum Information Processing.
[64] Daniel Jurafsky,et al. First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs , 2014, ArXiv.
[65] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[66] Andrew Zisserman,et al. Speeding up Convolutional Neural Networks with Low Rank Expansions , 2014, BMVC.
[67] Jürgen Schmidhuber,et al. A Clockwork RNN , 2014, ICML.
[68] Yann LeCun,et al. Fast Training of Convolutional Networks through FFTs , 2013, ICLR.
[69] Sophie Schirmer,et al. A CLOSER LOOK AT QUANTUM CONTROL LANDSCAPES AND THEIR IMPLICATION FOR CONTROL OPTIMIZATION , 2013 .
[70] Silvere Bonnabel,et al. Stochastic Gradient Descent on Riemannian Manifolds , 2011, IEEE Transactions on Automatic Control.
[71] Yi-Kai Liu,et al. Universal low-rank matrix recovery from Pauli measurements , 2011, NIPS.
[72] Steve Mullett,et al. Read the fine print. , 2009, RN.
[73] Alexander Kirillov,et al. An Introduction to Lie Groups and Lie Algebras , 2008 .
[74] Petros Drineas,et al. Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix , 2006, SIAM J. Comput..
[75] Shotaro Akaho,et al. Learning algorithms utilizing quasi-geodesic flows on the Stiefel manifold , 2005, Neurocomputing.
[76] N. Higham. The Scaling and Squaring Method for the Matrix Exponential Revisited , 2005, SIAM J. Matrix Anal. Appl..
[77] B. Hall. Lie Groups, Lie Algebras, and Representations: An Elementary Introduction , 2004 .
[78] Juergen Luettin,et al. Fast Face Detection using MLP and FFT , 1999 .
[79] Alan M. Frieze,et al. Fast Monte-Carlo algorithms for finding low-rank approximations , 1998, Proceedings 39th Annual Symposium on Foundations of Computer Science (Cat. No.98CB36280).
[80] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[81] Santosh S. Vempala,et al. Latent semantic indexing: a probabilistic analysis , 1998, PODS '98.
[82] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[83] J. Kautsky,et al. A Matrix Approach to Discrete Wavelets , 1994 .
[84] J. Keller. Closest Unitary, Orthogonal and Hermitian Operators to a Given Operator , 1975 .