Documents Representation via Generalized Coupled Tensor Chain with the Rotation Group constraint
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Andrzej Cichocki | Alexander Panchenko | Igor Vorona | Anh-Huy Phan | A. Cichocki | A. Phan | I. Vorona | A. Panchenko
[1] Sanjeev Arora,et al. A Latent Variable Model Approach to PMI-based Word Embeddings , 2015, TACL.
[2] Minmin Chen,et al. Efficient Vector Representation for Documents through Corruption , 2017, ICLR.
[3] Douwe Kiela,et al. Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry , 2018, ICML.
[4] Sanjeev Arora,et al. A Simple but Tough-to-Beat Baseline for Sentence Embeddings , 2017, ICLR.
[5] Yoon Kim,et al. Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.
[6] Shuchin Aeron,et al. Word Embeddings via Tensor Factorization , 2017, ArXiv.
[7] Thierry Poibeau,et al. A Tensor-based Factorization Model of Semantic Compositionality , 2013, NAACL.
[8] Ali Taylan Cemgil,et al. Generalised Coupled Tensor Factorisation , 2011, NIPS.
[9] Sebastian Rudolph,et al. Compositional Matrix-Space Models of Language , 2010, ACL.
[10] Mikhail Khodak,et al. A Theoretical Analysis of Contrastive Unsupervised Representation Learning , 2019, ICML.
[11] Rong Ge,et al. Understanding Composition of Word Embeddings via Tensor Decomposition , 2019, ICLR.
[12] Sebastian Rudolph,et al. Gradual Learning of Matrix-Space Models of Language for Sentiment Analysis , 2017, Rep4NLP@ACL.
[13] Rotem Dror,et al. The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing , 2018, ACL.
[14] Luke S. Zettlemoyer,et al. Deep Contextualized Word Representations , 2018, NAACL.
[15] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[16] Matteo Pagliardini,et al. Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features , 2017, NAACL.
[17] Zhuang Ma,et al. Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency , 2018, EMNLP.
[18] Omer Levy,et al. Neural Word Embedding as Implicit Matrix Factorization , 2014, NIPS.
[19] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[20] Sanjoy Dasgupta,et al. A Generalization of Principal Components Analysis to the Exponential Family , 2001, NIPS.
[21] Yu Meng,et al. Spherical Text Embedding , 2019, NeurIPS.
[22] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[23] HyvärinenAapo,et al. Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics , 2012 .
[24] Yulia Tsvetkov,et al. Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs , 2018, ICLR.
[25] Vatsal Sharan,et al. Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use , 2017, ICML.
[26] Andrew M. Dai,et al. Embedding Text in Hyperbolic Spaces , 2018, TextGraphs@NAACL-HLT.
[27] Douwe Kiela,et al. Poincaré Embeddings for Learning Hierarchical Representations , 2017, NIPS.
[28] Timothy Baldwin,et al. An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation , 2016, Rep4NLP@ACL.
[29] Surya Ganguli,et al. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks , 2013, ICLR.
[30] Yizhou Sun,et al. On Sampling Strategies for Neural Network-based Collaborative Filtering , 2017, KDD.
[31] Claire Cardie,et al. Compositional Matrix-Space Models for Sentiment Analysis , 2011, EMNLP.
[32] B. Khoromskij. O(dlog N)-Quantics Approximation of N-d Tensors in High-Dimensional Numerical Modeling , 2011 .
[33] Iryna Gurevych,et al. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks , 2019, EMNLP.
[34] D. Gabay. Minimizing a differentiable function over a differential manifold , 1982 .
[35] Guillaume Lample,et al. Neural Architectures for Named Entity Recognition , 2016, NAACL.
[36] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[37] Levent Tunçel,et al. Optimization algorithms on matrix manifolds , 2009, Math. Comput..
[38] Sergey Pavlov,et al. “Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology , 2019, Open Engineering.
[39] Ansgar Scherp,et al. CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model , 2019, ICLR.
[40] Quoc V. Le,et al. Distributed Representations of Sentences and Documents , 2014, ICML.
[41] Mohak Shah,et al. Evaluating Learning Algorithms: A Classification Perspective , 2011 .
[42] Philipp Birken,et al. Numerical Linear Algebra , 2011, Encyclopedia of Parallel Computing.
[43] Frank Verstraete,et al. Matrix product state representations , 2006, Quantum Inf. Comput..
[44] Gary Bécigneul,et al. Riemannian Adaptive Optimization Methods , 2018, ICLR.
[45] Hao Wu,et al. Long Document Classification From Local Word Glimpses via Recurrent Attention Learning , 2019, IEEE Access.
[46] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[47] Masashi Sugiyama,et al. Learning Efficient Tensor Representations with Ring-structured Networks , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).