24 papers · ranked by Valyu relevance
Kevin De Azevedo, Florian Buettner
In recent years, the exponential growth of high-dimensional, multi-modal molecular data has created both opportunities and challenges in personalized medicine. While existing approaches like matrix decomposition and neural network-based embeddings have been used to analyze such data, they have limitations in…
Zhuoyan Xu, Jiaxin Hu, Miaoyan Wang
Higher-order tensors have received increased attention across science and engineering. While most tensor decomposition methods are developed for a single tensor observation, scientific studies often collect side information, in the form of node features and interactions thereof, together with the tensor data. Such data…
Yao Xu, Kriton Konstantinidis, Danilo P. Mandic
The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms. To this end, recent efforts have generalized existing algorithms to irregular domains through graphs, with the aim to gain additional insights from data through the underlying graph…
Andrzej Cichocki
—Many problems in computational neuroscience, neuroinformatics, pattern/image recognition, signal processing and machine learning generate massive amounts of multidimensional data with multiple aspects and high dimensionality. Tensors (i.e., multi-way arrays) provide often a natural and compact representation for such…
Yajie Liu, Nilanjana Chakraborty, Zhaohui S. Qin, Suprateek Kundu
Identifying biomarkers for Alzheimer's disease with a goal of early detection is a fundamental problem in clinical research. Both medical imaging and genetics have contributed informative biomarkers in literature. To further improve the performance, recently, there is an increasing interest in developing analytic…
Lorin M Towle-Miller, Jeffrey C Miecznikowski
Advancements in genomic sequencing continually improve personalized medicine in complex diseases. Recent breakthroughs generate multiple types of signatures (or multi-omics) from each cell, producing different data ‘omic’ types per single-cell experiment. We introduce MOSCATO, a technique for selecting features across…
Manal Helal
The burgeoning growth of public domain data and the increasing complexity of deep learning model architectures have underscored the need for more efficient data representation and analysis techniques. This paper is motivated by the work of (Helal, 2023) and aims to present a comprehensive overview of tensorization.…
Saritha Kodikara, Brendan Lu, Shuhe Wang, Kim-Anh Lê Cao
Multi-omics studies capture comprehensive molecular profiles across biological layers to understand complex biological processes. A central challenge is integrating information across heterogeneous data types to identify coordinated molecular responses, particularly when measurements are collected longitudinally.…
Jiani Liu, Ce Zhu, Zhen Long, Yipeng Liu
The presence of multidirectional correlations in emerging multidimensional data poses a challenge to traditional regression modeling methods. Traditional modeling methods based on matrix or vector, for example, not only overlook the data's multidimensional information and lower model performance, but also add…
Suprateek Kundu, Alec Reinhardt, Serena Song, Joo Han + 3 more
'M. Lawson Meadows' 'Bruce Crosson' 'Venkatagiri Krishnamurthy'] Title: Abstract A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. However, traditional voxel-wise methods are beset with several pitfalls, which can…
Bokai Cao, Xiangnan Kong, Philip S. Yu
With rapid advances in neuroimaging techniques, the research on brain disorder identification has become an emerging area in the data mining community. Brain disorder data poses many unique challenges for data mining research. For example, the raw data generated by neuroimaging experiments is in tensor representations…
Alex H. Williams
Recordings from large neural populations are becoming an increasingly popular and accessible method in experimental neuroscience. While the activity of individual neurons is often too stochastic to interrogate circuit function on a moment-by-moment basis, multi-neuronal recordings enable us to do so by pooling…
Panqi Chen, Lei Cheng, Jianlong Li, Weichang Li + 3 more
'Jiang Bian' 'Shikai Fang'] Tensor decomposition is a fundamental tool for analyzing multi-dimensional data by learning lowrank factors to represent high-order interactions. While recent works on temporal tensor decomposition have made significant progress by incorporating continuous timestamps in latent factors, they…
Fen Liu, Jianfeng Chen, Weijie Tan, Chang Cai + 1 more
'Amelia Carolina Sparavigna'] Multi-modal fusion can achieve better predictions through the amalgamation of information from different modalities. To improve the performance of accuracy, a method based on Higher-order Orthogonal Iteration Decomposition and Projection (HOIDP) is proposed, in the fusion process…
Beichen Wang, Jiazhang Cai, Luyang Fang, Ping Ma + 1 more
Contemporary neurobehavior research often collects multi-dimensional tensor (MDT) data, consisting of time-series measurements for multiple features from multiple animals subjected to various perturbations. Proper analysis of the MDT data can facilitate the dissection of the underlying neural circuitry driving the…
Suleiman A. Khan, Muhammad Ammad-ud-din
With recent advancements in measurement technologies, many multi-way and tensor datasets have started to emerge. Exploiting the natural tensor structure in the data has been shown to be advantageous for both explorative and predictive studies in several application areas of bioinformatics and computational biology.…
Jiahao Su, Jingling Li, Xiaoyu Liu, Teresa Ranadive + 3 more
'Christopher Coley' 'Tai-Ching Tuan' 'Furong Huang'] We propose a framework of tensorial neural networks (TNNs) extending existing linear layers on low-order tensors to multilinear operations on higher-order tensors. TNNs have three advantages over existing networks: First, TNNs naturally apply to higher-order data…
Angela F Harper, Simone S Köcher, Karsten Reuter, Christoph Scheurer
Machine learning (ML) surrogate modeling is a powerful approach to reduce the computational cost of first-principles calculations. While well established for the prediction of scalar observables like energetics or band gaps, performance metrics for the learning of tensor-based observables have not yet been formalized.…
Authors not listed
Improving the performance of thermoelectric (TE) materials is essential for their wider adoption in sustainable energy and cooling applications. Impurity doping is a common strategy for enhancing transport properties, yet synthesizing every possible TE composition is infeasible, and high-fidelity ab initio simulations…
Junhua Zeng, Yuning Qiu, Yumeng Ma, Andong Wang + 1 more
As a promising data analysis technique, sparse modeling has gained widespread traction in the field of image processing, particularly for image recovery. The matrix rank, served as a measure of data sparsity, quantifies the sparsity within the Kronecker basis representation of a given piece of data in the matrix…
Maxwell Venetos, Mingjian Wen, Kristin Persson
The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Re- cently, machine learning has been applied to NMR in the prediction of isotropic chemi- cal shifts from a structure. Current machine learning models…
W. Jeffrey Johnston, Stefano Fusi
Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning.…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…