2020 Information Theory and Applications Workshop (ITA)最新文献

A Brain-Inspired Framework for Evolutionary Artificial General Intelligence 进化人工通用智能的大脑启发框架

2020 Information Theory and Applications Workshop (ITA) Pub Date : 2020-02-02 DOI: 10.1109/ITA50056.2020.9245000

Mohammad Nadji-Tehrani, A. Eslami

{"title":"A Brain-Inspired Framework for Evolutionary Artificial General Intelligence","authors":"Mohammad Nadji-Tehrani, A. Eslami","doi":"10.1109/ITA50056.2020.9245000","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9245000","url":null,"abstract":"From the medical field to agriculture, from energy to transportation, every industry is going through a revolution by embracing artificial intelligence (AI); nevertheless, AI is still in its infancy. Inspired by the evolution of the human brain, this paper demonstrates a novel method and framework to synthesize an artificial brain with cognitive abilities by taking advantage of the same process responsible for the growth of the biological brain called \"neuroembryogenesis.\" This framework shares some of the key behavioral aspects of the biological brain such as spiking neurons, neuroplasticity, neuronal pruning, and excitatory and inhibitory interactions between neurons, together making it capable of learning and memorizing. One of the highlights of the proposed design is its potential to incrementally improve itself over generations based on system performance, using genetic algorithms. A proof of concept at the end of the paper demonstrates how a simplified implementation of the human visual cortex using the proposed framework is capable of character recognition. Our framework is open-source and the code is shared with the scientific community at www.feagi.org.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125499340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Applications of Online Nonnegative Matrix Factorization to Image and Time-Series Data 在线非负矩阵分解在图像和时间序列数据中的应用

2020 Information Theory and Applications Workshop (ITA) Pub Date : 2020-02-02 DOI: 10.1109/ITA50056.2020.9245004

Hanbaek Lyu, G. Menz, D. Needell, Christopher Strohmeier

引用次数: 4

Residual Based Sampling for Online Low Rank Approximation 基于残差的在线低秩逼近抽样

2020 Information Theory and Applications Workshop (ITA) Pub Date : 2020-02-02 DOI: 10.1109/ITA50056.2020.9244974

Aditya Bhaskara, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam

{"title":"Residual Based Sampling for Online Low Rank Approximation","authors":"Aditya Bhaskara, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam","doi":"10.1109/ITA50056.2020.9244974","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9244974","url":null,"abstract":"We propose online algorithms for Column Subset Selection (CSS) and Principal Component Analysis (PCA), two methods that are widely employed for data analysis, summarization, and visualization. Given a data matrix A that is revealed one column at a time, the online CSS problems asks to keep a small set of columns, S, that best approximates the space spanned by the columns of A. As each column arrives, the algorithm must irrevocably decide whether to add it to S, or to ignore it. In the online PCA problem, the goal is to output a projection of each column to a low dimensional subspace. In other words, the algorithm must provide an embedding for each column as it arrives, which cannot be changed as new columns arrive.While both of these problems have been studied in the online setting, only additive approximations were known prior to our work. The core of our approach is an adaptive sampling technique that gives a practical and efficient algorithm for both of these problems. We prove that by sampling columns using their \"residual norm\" (i.e. their norm orthogonal to directions sampled so far), we end up with a significantly better dependence between the number of columns sampled, and the desired error in the approximation.We further show how to combine our algorithm \"in series\" with prior algorithms. In particular, using the results of Boutsidis et al. [5] and Frieze et al. [15] that have additive guarantees, we show how to improve the bounds on the error of our algorithm.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116212465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Interference-Resilient Relay Beamforming Scheme Inspired by Back-Propagation Algorithm 一种基于反向传播算法的抗干扰中继波束形成方案

2020 Information Theory and Applications Workshop (ITA) Pub Date : 2020-02-02 DOI: 10.1109/ITA50056.2020.9245001

Rui Wang, Yinglin Jiang

引用次数: 2

On-the-fly Uplink Training and Pilot Code Design for Massive MIMO Cellular Networks 大规模MIMO蜂窝网络的动态上行训练与导频码设计

2020 Information Theory and Applications Workshop (ITA) Pub Date : 2020-02-02 DOI: 10.1109/ITA50056.2020.9244985

Chenwei Wang, Zekun Zhang, H. Papadopoulos

引用次数: 1

Limits of Detecting Text Generated by Large-Scale Language Models 大规模语言模型生成文本检测的局限性

2020 Information Theory and Applications Workshop (ITA) Pub Date : 2020-02-02 DOI: 10.1109/ITA50056.2020.9245012

L. Varshney, N. Keskar, R. Socher

引用次数: 13

Non-Negative Matrix Factorization via Low-Rank Stochastic Manifold Optimization 基于低秩随机流形优化的非负矩阵分解

2020 Information Theory and Applications Workshop (ITA) Pub Date : 2020-02-02 DOI: 10.1109/ITA50056.2020.9244937

Ahmed Douik, B. Hassibi

{"title":"Non-Negative Matrix Factorization via Low-Rank Stochastic Manifold Optimization","authors":"Ahmed Douik, B. Hassibi","doi":"10.1109/ITA50056.2020.9244937","DOIUrl":"https://doi.org/10.1109/ITA50056.2020.9244937","url":null,"abstract":"Several real-world applications, notably in non-negative matrix factorization, graph-based clustering, and machine learning, require solving a convex optimization problem over the set of stochastic and doubly stochastic matrices. A common feature of these problems is that the optimal solution is generally a low-rank matrix. This paper suggests reformulating the problem by taking advantage of the low-rank factorization X = UVT and develops a Riemannian optimization framework for solving optimization problems on the set of low-rank stochastic and doubly stochastic matrices. In particular, this paper introduces and studies the geometry of the low-rank stochastic multinomial and the doubly stochastic manifold in order to derive first-order optimization algorithms. Being carefully designed and of lower dimension than the original problem, the proposed Riemannian optimization framework presents a clear complexity advantage. The claim is attested through numerical experiments on real-world and synthetic data for Non-negative Matrix Factorization (NFM) applications. The proposed algorithm is shown to outperform, in terms of running time, state-of-the-art methods for NFM.","PeriodicalId":137257,"journal":{"name":"2020 Information Theory and Applications Workshop (ITA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128585975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Matrix Multiplication: The Sparse Power-of-2 Factorization 高效矩阵乘法:稀疏的2次方分解

2020 Information Theory and Applications Workshop (ITA) Pub Date : 2020-02-02 DOI: 10.1109/ITA50056.2020.9244952

R. Müller, Bernhard Gäde, Ali Bereyhi

引用次数: 6

On Marton's Achievable Region: Local Tensorization for Product Channels with a Binary Component 关于马顿可达区域:具有二元分量的产品通道的局部张化

2020 Information Theory and Applications Workshop (ITA) Pub Date : 2020-02-02 DOI: 10.1109/ITA50056.2020.9244997

Chandra Nair

引用次数: 2

Identifying unpredictable test examples with worst-case guarantees 识别具有最坏情况保证的不可预测的测试示例

2020 Information Theory and Applications Workshop (ITA) Pub Date : 2020-02-02 DOI: 10.1109/ITA50056.2020.9244996

S. Goldwasser, A. Kalai, Y. Kalai, Omar Montasser

引用次数: 0