Learning with Tensor Representation

as uT Xv + b where u ∈ Rn1 and v ∈ Rn2 . Thus, there are only n1 + n2 + 1 parameters. This .... Specifically, SVM try to find a decision surface t...

4 downloads 0 Views 181KB Size

Recommend Documents

Apr 17, 2018 - 1.2 Representation Learning in Knowledge Graphs . ..... national Conference, ESWC 2016, Heraklion, Crete, Greece, May 29 – June 2, 2016,.

Jan 13, 2014 - Finally, the classification result is obtained by nearest ... acquired by air-borne or space-born platforms. .... Therefore, in this paper, we introduce a novel feature extraction algorithm named sparse transfer manifold embedding.

Tensor networks are approximations of high-order tensors which are efficient to work with and have been very successful for physics and mathematics ...

Our proposed TenSR model is able to empower the sparse representation especially when dealing with high dimensional data (e.g. 3D multi-spectral images as demonstrated in experiments) by greatly reducing the processing cost but meanwhile achieving co

Most contemporary multi-task learning methods assume linear models. This set- ting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing str

different noise rates extended from a real-world dataset, ... dalism, which aims to combat with deliberate destruction- ..... https://github.com/thunlp/CKRL.

Jan 16, 2018 - Unsupervised representation learning, Auto-encoder, Laplacian pyra- mid, Convolutional .... 2 RELATED WORK. Unsupervised representation learning, aiming to use data without any annotation, is a fairly well studied problem in machine le

Nov 19, 2015 - Training on various image datasets, we show convincing evidence that our deep convolu- ... resolution (Freeman et al., 2002) and in-painting (Hays & Efros, 2007). ..... furniture. In order to explore the form that these representations

2015] separately use subspace and manifold to model an im- age set, and the ... the set has a small sample size but big data variations [Hu .... sets for classification. The dictionary-based face recognition from video (DFRV) method [Chen et al., 201

Kilimanjaro is a snow-covered mountain 19,710 feet high, and is said to be the highest mountain in Africa. Its western summit is called the Masai “Ngaje Ngai,” ...

[SAS05] proposed a new barycentric coordinate system with ...... In Proceedings SIGGRAPH '01 (New York, NY, USA,. 2001), ACM, pp. 171–178. [MPBM03] ...Missing:

Jul 12, 2007 - The Dirac equation is associated with a specific transformation behaviour: ...... www.ks.uiuc.edu/Services/Class/PHYS480/qm_PDF/chp10.pdf.

Aug 4, 2016 - texture features. A tensor discriminative locality alignment (TDLA) method [39] is developed to remove ..... decomposition (http://www.sandia.gov/∼tgkolda/TensorToolbox/index-2.6.html) [55], the solution ...... IEEE Geosci.

tems with topological order6 and as quantum error correcting codes. The first such proposal was ... an assignment of quantum dimensions di to the labels. .... this expression can be simplified in the case of an infinite or periodic lattice to yield:

Freebase: A collaboratively created graph database for structuring human knowledge. In Proc. SIGMOD'08,. 1247–1250. Cantador, I.; Konstas, I.; and Jose, J. M. ...

Permission to make digital or hard copies of all or part of this work for personal or classroom use is ... But, sentences rarely stand on their own in a well-formed text. On a finer level, sentences are connected with each other by certain logical re

TranSparse [Ji et al., 2016], KG2E [He et al., 2015], PTransE. [Lin et al., 2015a] can also be easily adopted as relational triple encoder. Due to the space limit, ...

Each layer of this hierarchy consists of three components: sparse coding, saliency ... In this paper, we present a deep learning model for hierarchical image ...

Nov 22, 2017 - GraphGAN: Graph Representation Learning with Generative Adversarial Nets. Hongwei Wang1,2, Jia Wang3, Jialin ... mize the log-likelihood of observing context vertices for the given vertex. Node2vec (Grover and .... sampling of v is dis

[2013] develop Multiple Feature Hashing (MFH). By us- ing the learned hashing hyper-plane, MFH concatenates all the features into a single vector and then maps it into binary codes. Liu et al. [2014] propose Compact Kernel Hashing. (CKH) by formulati

state-of-the-art accuracy on the STL-10 dataset. 1. Introduction. We consider computing high-level image representations with which we can more easily classify ...

Aug 4, 2017 - State Key Lab on Intelligent Technology and Systems, ... analysis (Xianghua et al., 2013), and in section 3.2 .... (Neelakantan et al., ..... power of our attention-based models. ..... Ilya Sutskever, Oriol Vinyals, and Quoc V Le.

up task. q 2003 Elsevier Ltd. All rights reserved. Keywords: Hierarchical reinforcement learning; Via-point; Motor control; Cart-pole; Swing up; Robotics. 1. Introduction ... learning machine. A reinforcement learning framework is fascinating and can

Mar 7, 2016 - K. Weinberger, A. Dasgupta, J. Langford, A. Smola, and J. Attenberg. Feature hashing for large scale multitask learning. In ICML, pages 1113–1120. ACM, 2009. Y. Xue, X. Liao, L. Carin, and B. Krishnapuram. Multi-task learning for clas