Results 51 to 60 of about 59,513 (222)

SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi‐Omics Using Multimodal Mixture of Disentangled Experts

Advanced Science, EarlyView.
SpaMode introduces a versatile framework for spatial multi‐omics integration across vertical, horizontal, and mosaic scenarios. By disentangling modality‐invariant and variant features through a mixture‐of‐experts mechanism, it adaptively reconfigures spatially heterogeneous signals.
Xubin Zheng, Xinlei Huang, Xiang Zhou, Dian Meng, Ziyue Qiao, Zhiyuan Yuan, Lixin Cheng   +6 more
wiley   +1 more source

Distributed Vector Quantization Based on Kullback-Leibler Divergence

Entropy, 2015
The goal of vector quantization is to use a few reproduction vectors to represent original vectors/data while maintaining the necessary fidelity of the data.
Pengcheng Shen, Chunguang Li, Yiliang Luo   +2 more
doaj   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

On the symmetrized S-divergence [PDF]

ITM Web of Conferences, 2019
In this paper we worked with the relative divergence of type s, s ∈ ℝ, which include Kullback-Leibler divergence and the Hellinger and χ2 distances as particular cases.
Simić Slavko
doaj   +1 more source

Maximally Divergent Intervals for Anomaly Detection

, 2016
We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series.
Barz, Björn, Bodesheim, Paul, Denzler, Joachim, Flach, Milan, Guanche, Yanira, Mahecha, Miguel, Reichstein, Markus, Rodner, Erik   +7 more
core   +1 more source

A Kullback-Leibler Divergence for Bayesian Model Diagnostics. [PDF]

Open J Stat, 2011
This paper considers a Kullback-Leibler distance (KLD) which is asymptotically equivalent to the KLD by Goutis and Robert [1] when the reference model (in comparison to a competing fitted model) is correctly specified and that certain regularity conditions hold true (ref. Akaike [2]).
Wang CP, Ghosh M.
europepmc   +4 more sources

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source

f-divergence Analysis of Generative Adversarial Network

Foundations of Computing and Decision Sciences
We aim to establish estimation bounds for various divergences, including total variation, Kullback-Leibler (KL) divergence, Hellinger divergence, and Pearson χ2 divergence, within the GAN estimator.
Hasan Mahmud, Sang Hailin
doaj   +1 more source

Entropy Concepts Applied to Option Pricing [PDF]

, 2016
Uncertainty is one of the most important concept in financial mathematics applications. In this paper we review some important aspects related to the application of entropy-related concepts to option pricing.
Tunaru, Radu
core  

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source