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...

1 downloads 0 Views 181KB Size

Recommend Documents

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

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

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

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,” ...

[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

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

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

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

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

Dec 29, 2017 - the University College London Computer Science Department, I was able to travel to all conferences that I wanted to ..... List of Tables. 2.1 Example knowledge base using Prolog syntax (left) and as list repre- sentation as used in the

Leslie Pack Kaelbling and Tim Oates. AI Memo 2002- .... an attentional resource under the control of the agent. The agent .... and rewards. 4.4 Two Deictic Representations. While a deictic name for an object can be conceived as a long string like the

Mar 1, 2016 - In other successful cases, the structure of ... However, oracles also naturally arize from data structure, such as temporal or spatial orderings or ..... served data p(m|D). We use variational inference analogously to learn an approxima

Dec 31, 2017 - (Dated: January 3, 2018). Inspired by coarse-graining approaches used in physics, we show how similar algorithms can be adapted for data. The resulting algorithms are based on layered tree tensor networks and scale linearly with both t

Nov 2, 2017 - encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather ... best generative models (as measured by log-likelihood) will be those without latents but a powerful decoder ... autoencoder (VAE) framew

Abstract. Distributed representations of words (aka word embedding) have proven helpful in solving natural language processing. (NLP) tasks. Training distributed representations of words with neural networks has lately been a major focus of re- searc

discovery is that by calculating for each input word the total error ...... such disorders and whether or not there is more to dyslexia than a simple slowness.

Aug 29, 2017 - Furthermore, designing a distance function is too difficult or error-prone for our intended users, as it may involve ..... embedding the moments computation in the decomposition algorithm, allows to run the tensor de- composition .....

representation type is given in terms of the Gabriel quivers of the algebras A, B. Introduction. In this paper by ... One of our main results is Theorem 3.2 containing a full classification of pairs of weakly sincere simply ... arrows Q1, and I is an

Jun 18, 2015 - dictionary learning. In this paper, we develop novel tensor decomposition algorithms for parameter ... ∗University of California Irvine, Department of Electrical Engineering and Computer Science, [email protected] †University of ...

ket data like firm size, cash flow, book-to-market equity, past return, on stock movements. For example, Dechow (1994) shows that accounting earnings and cash flows help measure the firm performance, as reflected in stock returns. Jegadeesh and Titma

Jan 1, 2017 - Deep Learning in Chemoinformatics using Tensor Flow. THESIS submitted in partial satisfaction of the requirements for the degree of. MASTER OF SCIENCE in Computer Science by. Akshay Jain. Thesis Committee: Professor Pierre Baldi, Chair.

Feb 3, 2015 - MTA-BME Lendület Quantum Chemistry Research Group, Department of Physical ...... application of the Schmidt-decomposition, the required.

Jan 29, 2010 - Tensor Algebra. Oleksii V. Morozov · Patrick R. Hunziker. Abstract Since its introduction by Gauss, Matrix Algebra has facilitated understanding of scientific problems, hiding .... for multidimensional optimization problems. ... exten