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20162026
most citedMachine learning models for DOTA 2 outcomes prediction

16 citations · 28 across the 12 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.LG2023

TERM Model: Tensor Ring Mixture Model for Density Estimation

Ruituo Wu, Jiani Liu, Ce Zhu +3

Efficient probability density estimation is a core challenge in statistical machine learning. Tensor-based probabilistic graph methods address interpretability and stability concer…

cs.LG2023

Quantization Aware Factorization for Deep Neural Network Compression

Daria Cherniuk, Stanislav Abukhovich, Anh-Huy Phan +3

Tensor decomposition of convolutional and fully-connected layers is an effective way to reduce parameters and FLOP in neural networks. Due to memory and power consumption limitatio…

cs.CV2023

Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition

Ashish Jha, Dimitrii Ermilov, Konstantin Sobolev +8

Pedestrian Attribute Recognition (PAR) focuses on identifying various attributes in pedestrian images, with key applications in person retrieval, suspect re-identification, and sof…

cs.CV2023

Image Reconstruction using Superpixel Clustering and Tensor Completion

Maame G. Asante-Mensah, Anh Huy Phan, Salman Ahmadi-Asl +2

This paper presents a pixel selection method for compact image representation based on superpixel segmentation and tensor completion. Our method divides the image into several regi…

math.NA2023

Adaptive Cross Tubal Tensor Approximation

Salman Ahmadi-Asl, Anh Huy Phan, Andrzej Cichocki +4

In this paper, we propose a new adaptive cross algorithm for computing a low tubal rank approximation of third-order tensors, with less memory and lower computational complexity th…

math.NA2023

Robust Low-Tubal-rank tensor recovery Using Discrete Empirical Interpolation Method with Optimized Slice/Feature Selection

Salman Ahmadi-Asl, Anh-Huy Phan, Cesar F. Caiafa +1

In this paper, we extend the Discrete Empirical Interpolation Method (DEIM) to the third-order tensor case based on the t-product and use it to select important/ significant latera…