22 papers · ranked by Valyu relevance
Luis Maßny, Christoph Hofmeister, Maximilian Egger, Rawad Bitar + 1 more
'Antonia Wachter-Zeh'] Abstract—We consider distributed learning in the presence of slow and unresponsive worker nodes, referred to as stragglers. In order to mitigate the effect of stragglers, gradient coding redundantly assigns partial computations to the worker such that the overall result can be recovered from only…
Yuxin Jiang, Wenqin Zhang, Lele Wang
Gradient coding is a distributed computing technique aiming to provide robustness against slow or non-responsive computing nodes, known as stragglers, while balancing the computational load for responsive computing nodes. Among existing gradient codes, a construction based on combinatorial designs, called BIBD gradient…
Ming Xiao, Mikael Skoglund, H. Vincent Poor, Onur Günlü + 2 more
'Rafael F. Schaefer' 'Holger Boche'] This article aims to give a comprehensive and rigorous review of the principles and recent development of coding for large-scale distributed machine learning (DML). With increasing data volumes and the pervasive deployment of sensors and computing machines, machine learning has…
Ari S. Benjamin, Ling-Qi Zhang, Cheng Qiu, Alan Stocker + 1 more
Animal sensory systems are more sensitive to common features in the environment than uncommon features. For example, small deviations from the more frequently encountered horizontal orientations can be more easily detected than small deviations from the less frequent diagonal ones. Here we find that artificial neural…
Ari S. Benjamin, Ling-Qi Zhang, Cheng Qiu, Alan A. Stocker + 1 more
'Konrad P. Kording'] Human sensory systems are more sensitive to common features in the environment than uncommon features. For example, small deviations from the more frequently encountered horizontal orientations can be more easily detected than small deviations from the less frequent diagonal ones. Here we find that…
Muhammet Balcılar, Bharath Bhushan Damodaran, Karam Naser, Franck Galpin + 1 more
'Franck Galpin' 'Pierre Hellier'] End-to-end image/video codecs are getting competitive compared to traditional compression techniques that have been developed through decades of manual engineering efforts. These trainable codecs have many advantages over traditional techniques such as easy adaptation on perceptual…
David J. Ottenheimer, Madelyn M. Hjort, Anna J. Bowen, Nicholas A. Steinmetz + 1 more
The ability to associate reward-predicting stimuli with adaptive behavior is frequently attributed to the prefrontal cortex, but the stimulus-specificity, spatial distribution, and stability of neural cue-reward associations are unresolved. We trained headfixed mice on an olfactory Pavlovian conditioning task and…
Louis-Adrien Dufrène, Quentin Lampin, Guillaume Larue
—This study investigates the problem of learning linear block codes optimized for Belief-Propagation decoders significantly improving performance compared to the state-ofthe-art. Our previous research is extended with an enhanced system design that facilitates a more effective learning process for the parity check…
Tadashi Wadayama, Lantian Wei
—This paper presents the Gradient Flow (GF) decoding for LDPC codes. GF decoding, a continuous-time methodology based on gradient flow, employs a potential energy function associated with bipolar codewords of LDPC codes. The decoding process of the GF decoding is concisely defined by an ordinary differential equation…
Jiamin Sun, Zhongjie Zhu, Renwei Tu, Zhibo Xie + 1 more
In Versatile Video Coding (VVC), the partition patterns for coding units (CUs) have significant impact on the encoding efficiency. Determining the optimal CU partition is particularly time-consuming due to the calculation and comparison of rate-distortion costs for all possible partition patterns, especially during the…
Davide Noè, Hideaki Yamamoto, Yuichi Katori, Shigeo Sato
The predictive coding framework offers a compelling model for temporal signal processing in the cortex. Recent studies explored its implementation in spiking architectures using Hebbian plasticity rules or offline learning; however, a biologically inspired model that enables gradient-based minimization of prediction…
Robert Rosenbaum, Gennady S. Cymbalyuk
Artificial neural networks are often interpreted as abstract models of biological neuronal networks, but they are typically trained using the biologically unrealistic backpropagation algorithm and its variants. Predictive coding has been proposed as a potentially more biologically realistic alternative to…
Sorinel A. Oprisan, Ana Oprisan, Xiangjie Kong
This study presents a novel analytical framework for understanding the relationship between the image gradients and the symmetries of the Gray Level Co-occurrence Matrix (GLCM). Analytical expression for four key features-sum average (SA), sum variance (SV), difference variance (DV), and entropy-were derived to capture…
Tony Shaska
We introduce the Graded Transformer framework, embedding algebraic inductive biases via grading transformations on vector spaces. Extending Graded Neural Networks (GNNs), we propose the Linearly Graded Transformer (LGT) and Exponentially Graded Transformer (EGT), which apply parameterized scaling—via fixed or learnable…
Andrei Ciuparu, Raul C. Mureșan
We introduce Gradient-k, an upgrade of the k-means algorithm that improves clustering accuracy and reduces the number of iterations required for convergence. This is achieved by correcting the distance used in the k-means algorithm by a factor based on the angle between the density gradient and the direction to the…
Sarah Rutan, Kathryn Cash, Dwight Stoll II
The present work describes a re-parameterization of the Neue Kuss (NK) model for describing retention in liquid chromatography, and this re-parameterized model is used fit a large set of isocratic retention measurements with improved convergence properties relative to the original parameterization of the model. Next…
Thach V. Bui
Neural coding is an important tool to discover the inner workings of mind. In this work, we propose and consider a simple but novel self-decoding model for neural coding based on the principle that the neuron body represents ongoing stimulus while dendrites are used to store that stimulus as a memory. In particular…
Authors not listed
This work provides a rigorous theoretical investigation of selective error correction strategies for variational quantum algorithms, with focus on understanding the interplay between error suppression, circuit trainability, and computational resource requirements. We develop a mathematical framework that characterizes…
Hyunmin Cho, Jaejun Yoo, Kyong Hwan Jin
We study sinusoidal recurrence as an iterative mechanism for harmonic spectral enrichment in implicit neural representations (INRs). Our analysis reveals that sinusoidal activations induce a harmonic line spectrum, providing a spectral account of how recurrent unrolling enriches the effective spectral support. We…
Wanghuai Zhou, Deping Hu, Arkajit Mandal, Pengfei Huo
We derive a rigorous nuclear gradient for a molecule-cavity hybrid system using the Quantum Electrodynamics Hamiltonian. We treat the electronic-photonic DOFs as the quantum subsystem, and the nuclei as the classical subsystem. Using the adiabatic basis for the electronic DOF and the Fock basis for the photonic DOF…
Michael Hutcheon, Andrew Teale
Algorithms are presented for performing a topological analysis of an arbitrary function, evaluated on an arbitrary grid of points. These algorithms work strictly by post-processing the data and require no additional function evaluations. This is achieved by connecting the grid points with a neighbourhood graph…
Deval Shah, Zi Yu Xue, Tor M. Aamodt
Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regression in which a network is trained by minimizing the squared or absolute error of output labels. Prior work has shown that solving a…