16 papers · ranked by Valyu relevance
Ricardo Rios-Carrillo, Alonso Ramírez-Manzanares, Hiram Luna-Munguía, Mirelta Regalado + 1 more
Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is a non-invasive technique that is sensitive to microstructural geometry in neural tissue and is useful for the detection of neuropathology in research and clinical settings. Tensor valued diffusion encoding schemes (b-tensor) have been developed to enrich the…
Yiang Pan, Yuanjing Feng, Jianzhong He, William Consagra + 3 more
Diffusion MRI (dMRI) enables noninvasive characterization of white-matter fiber orientations and tissue microstructure, but widely used approaches, such as constrained spherical deconvolution (CSD) and parametric multicompartment models, typically address these features separately. The diffusion tensor distribution…
Arthur P C Spencer, Jasmine Nguyen-Duc, Inès de Riedmatten, Filip Szczepankiewicz + 1 more
Functional MRI (fMRI) using the blood-oxygen level dependent (BOLD) signal provides valuable insight into grey matter activity. However, uncertainty surrounds the white matter BOLD signal. Apparent diffusion coefficient (ADC) offers an alternative fMRI contrast sensitive to transient cellular deformations during neural…
Samo Lasič, Nathalie Just, Markus Nilsson, Filip Szczepankiewicz + 2 more
'Matthew Budde' 'Henrik Lundell'] Title: Abstract Tensor-valued encoding in diffusion MRI allows probing of microscopic anisotropy in tissue, however, time-dependent diffusion (TDD) can bias results unless b-tensors are carefully tuned to account for TDD. We propose two novel strategies for tuning b-tensors to enable…
Filip Szczepankiewicz, Malwina Molendowska, Samo Lasič, Marcella E Safi + 7 more
Tensor-valued diffusion encoding employs gradient waveforms that enable unique sensitivity to microstructural features of tissue, but the interpretation of signal and parameters may be confounded by diffusion-time dependence. We introduce a framework for restriction-weighted q-space trajectory imaging (ResQ) that…
Kulam Najmudeen Magdoom, Alexandru V. Avram, Joelle E. Sarlls, Gasbarra Dario + 1 more
Neural tissue microstructure plays an important role in developmental, physiological and pathophysiological processes. Diffusion tensor distribution (DTD) MRI helps probe heterogeneity at the mesoscopic length scale, orders of magnitude smaller than the nominal MRI voxel size, by describing water diffusion within a…
L. Wright, F. Barratt, J. Dborin, V. Wimalaweera + 2 more
'A. G. Green'] We present tensor networks for feature extraction and refinement of classifier performance. These networks can be initialised deterministically and have the potential for implementation on nearterm intermediate-scale quantum (NISQ) devices. Feature extraction proceeds through a direct combination and…
Tae-Hyung Kwon, Jihoon Ko, Jinhong Jung, Kijung Shin
In this work, we propose TENSORCODEC, a lossy compression algorithm for general tensors that do not necessarily adhere to strong input data assumptions. TENSORCODEC incorporates three key ideas. The first idea is Neural Tensor-Train Decomposition (NTTD) where we integrate a recurrent neural network into Tensor-Train…
Paul Haubenwallner, Matthias Heller
The conversion of functions to quantics tensor trains is a well-established procedure and can either be done analytically or numerically. Numerical conversion schemes are based on singular value decompositions, where access to the full tensor is necessary, or on cross interpolations, which only depend on sampling a…
Zhiwei Bao, Liu Liao-Liao, Zhiyu Wu, Yifan Zhou + 3 more
'Michał Aibin' 'Yvonne Coady'] Abstract— The exponential growth of artificial intelligence (AI) and machine learning (ML) applications has necessitated the development of efficient storage solutions for vector and tensor data. This paper presents a novel approach for tensor storage in a Lakehouse architecture using…
Jia Xu, Han Pu, Dong Wang
In the quest for computational efficiency, binary neural networks (BNNs) have emerged as a promising paradigm, offering significant reductions in memory footprint and computational latency. In traditional BNN implementation, the first and last layers are typically full-precision, which causes higher logic usage in…
Laura Smets, Werner Van Leekwijck, Ing Jyh Tsang, Steven Latré
Introduction Hyperdimensional Computing (HDC) is a brain-inspired and lightweight machine learning method. It has received significant attention in the literature as a candidate to be applied in the wearable Internet of Things, near-sensor artificial intelligence applications, and on-device processing. HDC is…
Laura Sainz Villalba, P. Michael Furlong, Madeleine Bartlett, Nicole Sandra-Yaffa Dumont
The brain faces the feature binding problem: how are multiple stimulus features and variables combined into coherent representations that support flexible behavior? A key finding from neuroscience is that some brain regions employ factorized representations, where distinct features are encoded in neural state space in…
Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato
Current methods for compressing neural network weights, such as decomposition, pruning, quantization, and channel simulation, often overlook the inherent symmetries within these networks and thus waste bits on encoding redundant information. In this paper, we propose a format based on bits-back coding for storing…
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…
Gian Marco Visani, Michael N. Pun, Armita Nourmohammad
Group-equivariant neural networks have emerged as a data-efficient approach to solve classification and regression tasks, while respecting the relevant symmetries of the data. However, little work has been done to extend this paradigm to the unsupervised and generative domains. Here, we present Holographic-(V)AE…