Rank (graph theory)
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LoRA: Low-Rank Adaptation of Large Language Models Open
An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model param…
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GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy Open
The Genome Taxonomy Database (GTDB; https://gtdb.ecogenomic.org) provides a phylogenetically consistent and rank normalized genome-based taxonomy for prokaryotic genomes sourced from the NCBI Assembly database. GTDB R06-RS202 spans 254 090…
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Tensor Decomposition for Signal Processing and Machine Learning Open
Tensors or {\em multi-way arrays} are functions of three or more indices $(i,j,k,\cdots)$ -- similar to matrices (two-way arrays), which are functions of two indices $(r,c)$ for (row,column). Tensors have a rich history, stretching over al…
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Touché-25-Advertisement-in-Retrieval-Augmented-Generation Open
Dataset for Sub-Task 1 (Generation) of the Touché 2025 Task 4. The goal of this task is to research advertisements in retrieval augmented generation (RAG). Towards this goal, the dataset provides queries from the Webis Generated Native Ads…
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Deeper, Broader and Artier Domain Generalization Open
The problem of domain generalization is to learn from multiple training domains, and extract a domain-agnostic model that can then be applied to an unseen domain. Domain generalization (DG) has a clear motivation in contexts where there ar…
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A review of multi-objective optimization: Methods and its applications Open
Several reviews have been made regarding the methods and application of multi-objective optimization (MOO). There are two methods of MOO that do not require complicated mathematical equations, so the problem becomes simple. These two metho…
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Efficient Low-rank Multimodal Fusion With Modality-Specific Factors Open
Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, Paul Pu Liang, AmirAli Bagher Zadeh, Louis-Philippe Morency. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.
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Prediction of Organic Reaction Outcomes Using Machine Learning Open
Computer assistance in synthesis design has existed for over 40 years, yet retrosynthesis planning software has struggled to achieve widespread adoption. One critical challenge in developing high-quality pathway suggestions is that propose…
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DALI and the persistence of protein shape Open
DALI is a popular resource for comparing protein structures. The software is based on distance‐matrix alignment. The associated web server provides tools to navigate, integrate and organize some data pushed out by genomics and structural g…
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Recurrent Recommender Networks Open
Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-…
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Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code Open
Species distribution modeling (SDM) is widely used in ecology and conservation. Currently, the most available data for SDM are species presence‐only records (available through digital databases). There have been many studies comparing the …
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Benchmark for filter methods for feature selection in high-dimensional classification data Open
Feature selection is one of the most fundamental problems in machine learning and has drawn increasing attention due to high-dimensional data sets emerging from different fields like bioinformatics. For feature selection, filter methods pl…
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Human Semantic Parsing for Person Re-identification Open
Person re-identification is a challenging task mainly due to factors such as background clutter, pose, illumination and camera point of view variations. These elements hinder the process of extracting robust and discriminative representati…
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Dynamic Image Networks for Action Recognition Open
We introduce the concept of dynamic image, a novel compact representation of videos useful for video analysis especially when convolutional neural networks (CNNs) are used. The dynamic image is based on the rank pooling concept and is obta…
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Unbiased Learning-to-Rank with Biased Feedback Open
Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user centric, and timely), its inherent biases…
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An Overview of Low-Rank Matrix Recovery From Incomplete Observations Open
Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, these matrices cannot be fully sampled or directly observed, an…
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Introducing EzAAI: a pipeline for high throughput calculations of prokaryotic average amino acid identity Open
The average amino acid identity (AAI) is an index of pairwise genomic relatedness, and multiple studies have proposed its application in prokaryotic taxonomy and related disciplines. AAI demonstrates better resolution in elucidating taxono…
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AlignedReID: Surpassing Human-Level Performance in Person Re-Identification Open
In this paper, we propose a novel method called AlignedReID that extracts a global feature which is jointly learned with local features. Global feature learning benefits greatly from local feature learning, which performs an alignment/matc…
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Are Risk Preferences Stable? Open
It is ultimately an empirical question whether risk preferences are stable over time. The evidence comes from diverse strands of literature, covering the stability of risk preferences in panel data over shorter periods of time, life-cycle …
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Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization Open
Multiview data clustering attracts more attention than their single-view counterparts due to the fact that leveraging multiple independent and complementary information from multiview feature spaces outperforms the single one. Multiview sp…
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions Open
Machine learning and data mining algorithms are becoming increasingly\nimportant in analyzing large volume, multi-relational and multi--modal\ndatasets, which are often conveniently represented as multiway arrays or\ntensors. It is therefo…
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Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint l2,1 Norm Open
To improve the detection ability of infrared small targets in complex backgrounds, a novel method based on non-convex rank approximation minimization joint l2,1 norm (NRAM) was proposed. Due to the defects of the nuclear norm and l1 norm, …
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A Case Study Competition Among Methods for Analyzing Large Spatial Data Open
Supplementary materials for this article are available at 10.1007/s13253-018-00348-w.
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Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview Open
Substantial progress has been made recently on developing provably accurate\nand efficient algorithms for low-rank matrix factorization via nonconvex\noptimization. While conventional wisdom often takes a dim view of nonconvex\noptimizatio…
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A Modified CRITIC Method to Estimate the Objective Weights of Decision Criteria Open
In this study, we developed a modified version of the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method, namely the Distance Correlation-based CRITIC (D-CRITIC) method. The usage of the method was illustrated by evalua…
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Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations Open
This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral in…
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Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences Open
Prediction of taxonomy for marker gene sequences such as 16S ribosomal RNA (rRNA) is a fundamental task in microbiology. Most experimentally observed sequences are diverged from reference sequences of authoritatively named organisms, creat…
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A New Fuzzy MARCOS Method for Road Traffic Risk Analysis Open
In this paper, a new fuzzy multi-criteria decision-making model for traffic risk assessment was developed. A part of a main road network of 7.4 km with a total of 38 Sections was analyzed with the aim of determining the degree of risk on t…
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Tensor Ring Decomposition Open
Tensor networks have in recent years emerged as the powerful tools for solving the large-scale optimization problems. One of the most popular tensor network is tensor train (TT) decomposition that acts as the building blocks for the compli…
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Low-Rank Matrix Recovery via Efficient Schatten p-Norm Minimization Open
As an emerging machine learning and information retrieval technique, the matrix completion has been successfully applied to solve many scientific applications, such as collaborative prediction in information retrieval, video completion in …