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Khám phá khoa học theo phương pháp, lĩnh vực và bằng chứng.

Một danh mục duy nhất về các phương pháp nghiên cứu — tìm hiểu cách mỗi phương pháp hoạt động, khi nào nên dùng và điều nó không làm được.

6,435 phương pháp11 lĩnh vực7 họ phương pháp40 ngôn ngữ
Atlas khoa họcLập bản đồ cấu trúc của khoa học trước khi sử dụng.Lĩnh vực · phương pháp · lộ trình bằng chứngKhám phá bản đồ
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Phương phápThống kê1,836Trí tuệ nhân tạo & học máy1,661Khoa học quyết định932Phương pháp nghiên cứu1,354Đo lường1,745Nhân quả & bằng chứng532Thực hành nghiên cứu118
1,522 phương pháp · Trí tuệ nhân tạo & học máyXóa
Các phương pháp thực khớp với bộ lọc của bạn.
Sắp xếpĐộ phổ biếnA–ZZ–AMới nhất
deep learning

Multimodal LSTM

Multimodal LSTM extends the standard Long Short-Term Memory network to jointly process sequential data from multiple input modalities — such as text, audio, and video — within a unified recurrent architecture. By fusing representations from different sources before or within the LSTM cells, it captures temporal depende

2 nguồn2016
deep learning

Multimodal Multilayer Perceptron

A Multimodal Multilayer Perceptron (MM-MLP) is a feedforward neural network that ingests features from two or more heterogeneous input modalities — such as structured tabular data, text embeddings, and image feature vectors — by encoding each stream separately and fusing them into a shared representation before passing

2 nguồn2011
deep learning

Multimodal Named Entity Recognition

Multimodal Named Entity Recognition (MNER) extends classical NER by fusing textual sequences with complementary modalities — most commonly images — to improve the identification and classification of named entities such as persons, organizations, and locations in settings where visual context disambiguates ambiguous or

2 nguồn2018
text mining

Multimodal NLP

Multimodal NLP is a family of natural-language-processing pipelines that combine text with one or more additional data modalities — most commonly images, but also audio and video — to perform understanding and generation tasks such as visual question answering, image captioning, and multimodal sentiment recognition. Th

2 nguồn2021
deep learning

Multimodal NMF Topic Model

Multimodal NMF Topic Model extends Non-negative Matrix Factorization to simultaneously discover latent topics across multiple data modalities — such as text and images — by enforcing shared or aligned low-rank factor matrices. It uncovers coherent, interpretable topics that jointly explain patterns in both textual and

2 nguồn2010
deep learning

Multimodal Object Detection

Multimodal object detection extends single-modality object detectors by jointly processing signals from multiple sensor types — such as RGB cameras, depth sensors, LiDAR, radar, or text descriptions — to localize and classify objects with higher accuracy and robustness than any single modality alone. Fusion of compleme

2 nguồn2015
deep learning

Multimodal question answering

Multimodal question answering (Multimodal QA) is a class of deep-learning methods that answer natural-language questions by jointly reasoning over information from multiple modalities — most commonly text and images, but also video, audio, and structured tables. Introduced prominently through the VQA benchmark in 2015,

2 nguồn2015
deep learning

Multimodal Recurrent Neural Network

A Multimodal Recurrent Neural Network combines inputs from two or more data modalities — such as images, text, and audio — within a recurrent sequence-processing framework. It encodes each modality separately, fuses the representations, and then processes the combined signal through recurrent units (RNN, LSTM, or GRU)

2 nguồn2011
deep learning

Multimodal Reinforcement Learning

Multimodal Reinforcement Learning trains agents to make sequential decisions by perceiving and integrating multiple input modalities — such as raw pixels, language instructions, audio, and proprioceptive sensors — simultaneously. Rather than acting on a single data stream, the agent fuses heterogeneous signals into a u

2 nguồn2015
deep learning

Multimodal RoBERTa-based Classification

Multimodal RoBERTa-based Classification combines the RoBERTa transformer encoder — a robustly optimised variant of BERT — with auxiliary modalities such as images, structured metadata, or tabular features. The fused representation is passed to a classification head, allowing the model to leverage both rich language und

2 nguồn2019
deep learning

Multimodal Semantic Segmentation

Multimodal semantic segmentation assigns a semantic class label to every pixel in a scene by fusing information from two or more sensor modalities — most commonly RGB images paired with depth maps (RGB-D), LiDAR point clouds, thermal cameras, or text descriptions. Deep encoder-decoder networks learn to align and fuse c

2 nguồn2014
deep learning

Multimodal Sentence Embeddings

Multimodal sentence embeddings map text and images (and sometimes audio or video) into a shared continuous vector space, so that semantically related pairs from different modalities land close together. Trained by contrastive objectives on large paired corpora, these representations power cross-modal retrieval, zero-sh

2 nguồn2013
deep learning

Multimodal Text Summarization

Multimodal text summarization generates a concise textual summary by jointly processing multiple input modalities — most commonly text and images, but also video frames or audio — using deep learning models that align visual and linguistic representations. The output is a natural-language summary that captures salient

2 nguồn2018
deep learning

Multimodal Topic Modeling

Multimodal topic modeling discovers latent thematic structure shared across multiple data modalities — for example, co-occurring words and images — by learning a joint probabilistic representation that aligns topics across modalities. It extends classical text-only approaches such as LDA to settings where each document

2 nguồn2003
deep learning

Multimodal Transformer

A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabli

2 nguồn2019
deep learning

Multimodal Variational Autoencoder

The Multimodal Variational Autoencoder (MVAE) is a deep generative model that learns a shared latent representation across two or more data modalities — such as images and captions — using a product-of-experts fusion of modality-specific encoders, enabling generation and inference even when only a subset of modalities

2 nguồn2018
deep learning

Multimodal Vision Transformer

Multimodal Vision Transformer (Multimodal ViT) extends the Vision Transformer architecture to jointly process and align representations from multiple modalities — typically images and text — using self-attention and cross-attention mechanisms. By learning shared or aligned embedding spaces across modalities, it enables

2 nguồn2021
deep learning

Multimodal Word2Vec

Multimodal Word2Vec extends the classic Word2Vec framework by grounding word representations in perceptual signals — typically image features — alongside distributional text statistics. The result is word vectors that capture both linguistic co-occurrence patterns and visual meaning, enabling richer semantic similarity

2 nguồn2014
network analysis

Multiplex Network Analysis

Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensat

2 nguồn2014
deep learning

Multitask Learning

Multitask Learning (MTL) is a machine learning paradigm in which a model is trained simultaneously on multiple related tasks, sharing representations across them to improve generalization. Introduced formally by Rich Caruana in 1997, MTL draws on the intuition that auxiliary tasks act as inductive bias, providing extra

1 nguồn1997
biomechanics

Muscle Synergy Analysis

Muscle synergy analysis decomposes complex motor behavior into a small set of coactivated muscle groups (synergies or motor primitives). Pioneered by Marc Tresch and colleagues studying frog motor control, this approach reveals how the nervous system simplifies the control of many muscles by organizing them into task-r

2 nguồn1999
music information retrieval

Music Genre Classification

Music genre classification is the task of automatically assigning genre labels (rock, jazz, classical, pop, etc.) to audio recordings. Introduced formally by Tzanetakis and Cook (2002), it is one of the earliest and most studied music information retrieval problems. It remains critical for music discovery, recommendati

3 nguồn2002
music information retrieval

Music Segmentation

Music segmentation is the task of dividing a musical recording into distinct structural sections (e.g., verse, chorus, bridge, pre-chorus, outro). Introduced by Goto (2001), it identifies major structural boundaries and labels sections according to musical form. Segmentation is essential for music understanding, audio

3 nguồn2001
music information retrieval

Music Similarity Measure

Music similarity measures are computational methods for assessing how musically related two audio recordings are. Introduced by Logan (2001), similarity measures enable content-based music recommendation, playlist generation, and music discovery. Unlike fingerprinting, which identifies the same song, similarity measure

3 nguồn2001
music information retrieval

Musical Key Detection

Musical key detection is the task of automatically determining the key (tonal center) and scale mode of a musical composition from its audio. Introduced formally by Gómez (2006), it is essential for music analysis, transposition, harmonic understanding, and music theory education. The key defines the tonal center aroun

3 nguồn2006
civil engineering

Muskingum Routing

The Muskingum method is a hydrologic flood routing technique that predicts how a flood wave attenuates (reduces in peak) and spreads as it travels down a river reach. Developed by McCarthy in 1938 for the US Army Corps of Engineers, the method is simple enough for hand calculations while capturing the essential physics

3 nguồn1938
deep learning

N-BEATS

N-BEATS is a deep learning architecture for time series forecasting, introduced by Oreshkin and colleagues in 2020, built from interpretable trend and seasonality stacks. It was the first purely neural forecasting model to reach state-of-the-art performance on the M4 competition without relying on any classical statist

2 nguồn2020
deep learning

N-BEATSx

N-BEATSx is an extension of the N-BEATS neural time series forecasting model that incorporates exogenous (external) variables through a cross-learner architecture. Published in 2023, N-BEATSx improves upon N-BEATS by enabling the model to leverage additional features beyond the historical time series values.

1 nguồn2023
applied physics

N-Body Simulation

N-body simulation is a computational method for modeling the dynamics of a system of particles under mutual gravitational forces. Originating from Newton's laws of motion and gravitation, it solves the fundamental equations of celestial mechanics. This technique is essential for understanding planetary orbits, star clu

3 nguồn1687
text mining

N-gram Language Model

An n-gram language model is a statistical model that predicts the probability of the next word by looking only at the previous n−1 words. Described in detail by Jurafsky and Martin (Speech and Language Processing), it provides foundational infrastructure for text generation, spelling correction, and speech recognition.

2 nguồn
deep learning

N-HiTS

N-HiTS (Neural Hierarchical Interpolation for Time Series Forecasting), introduced by Challu and colleagues in 2023, is a deep neural forecasting architecture that combines the hierarchical forecasts of multiple stacks operating at different sampling rates and merges them through interpolation. It extends N-BEATS to de

2 nguồn2023
machine learning

Naive Bayes

Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick

1 nguồnintermediate1997
text mining

Named Entity Recognition

Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into ta

2 nguồn
materials science

Nanoindentation

Nanoindentation, or instrumented indentation, is a technique for measuring the hardness and elastic modulus of materials by pressing a hard probe into a sample surface and continuously recording load and penetration depth. Developed by Oliver and Pharr in 1992, nanoindentation enables measurement of mechanical properti

3 nguồn1992
text mining

Natural Language Generation

Natural Language Generation (NLG) is the branch of natural language processing that automatically produces fluent, human-readable text from structured data, knowledge graphs, or semantic representations. Formalised in the classical pipeline by Reiter and Dale (2000) and surveyed comprehensively by Gatt and Krahmer (201

2 nguồn1970
geophysics

NDVI

The Normalized Difference Vegetation Index (NDVI) is a spectral index computed from satellite or aerial multispectral imagery that quantifies vegetation greenness and vigor. Introduced by Rouse and colleagues in 1973 using Landsat data, NDVI has become the most widely used remote sensing metric for vegetation monitorin

2 nguồn1973
deep learning

NEAT

NEAT is a genetic algorithm for evolving artificial neural networks introduced by Kenneth Stanley and Risto Miikkulainen in 2002. Unlike methods that evolve weights alone, NEAT simultaneously evolves both the topology (structure) and the connection weights of neural networks. It achieves this through a direct genome en

1 nguồn2002
text mining

Negation Detection

Negation detection is a natural-language-processing task that locates negation cues in text — words or phrases such as 'no', 'not', 'without', or 'denies' — and determines the span of text (the scope) whose meaning those cues invert. Formalised for clinical text by Chapman et al. (2001) with the NegEx algorithm and ext

2 nguồn2001
network analysis

Network Diffusion Analysis

Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out

2 nguồn1927
network analysis

Network Embedding

Network embedding is a family of representation-learning methods that map each node of a graph into a dense, low-dimensional vector while preserving the network's structural properties. The approach was formalised for social-network data by Perozzi, Al-Rfou, and Skiena with DeepWalk (2014), which adapted the Word2Vec s

2 nguồn2014
network analysis

Network Motif Analysis

Network motif analysis is a statistical method for directed networks, introduced by Milo, Shen-Orr, and Alon in 2002, that identifies small recurring subgraph patterns — motifs — that appear significantly more often than would be expected in a comparable random network. By comparing a real network against a null ensemb

2 nguồn2002
network analysis

Network Resilience Analysis

Network resilience and vulnerability analysis is an analytical framework, formalised by Albert, Jeong, and Barabási (2000), that measures how a network degrades functionally as nodes or edges are progressively removed. By running targeted-attack simulations — removing the highest-centrality nodes first — and random-fai

2 nguồn2000
bioinformatics

Network-based copy number variation analysis

Network-based copy number variation analysis integrates genome-wide CNV data with biological interaction networks — such as protein-protein interaction (PPI) or pathway networks — to identify functionally coherent regions, driver genes, and altered subnetworks that raw CNV calling alone would miss. By propagating CNV s

2 nguồn2011
bioinformatics

Network-based epigenome-wide association study

Network-based EWAS extends conventional epigenome-wide association studies by overlaying differentially methylated positions or regions onto biological interaction networks — such as protein-protein interaction, co-expression, or gene regulatory networks — to identify functionally coherent epigenetic modules rather tha

2 nguồn2010
bioinformatics

Network-based eQTL analysis

Network-based eQTL analysis extends classical eQTL mapping by embedding genetic variant-to-expression associations within gene regulatory or protein interaction networks. Rather than treating each SNP-gene pair independently, this approach leverages network topology — such as co-expression modules or known pathway stru

2 nguồn2008
bioinformatics

Network-based GWAS

Network-based GWAS integrates conventional genome-wide association study results with biological network data — such as protein-protein interaction (PPI) networks or gene co-expression graphs — to identify disease-relevant gene modules or subnetworks. Instead of reporting only the top individual SNPs, this approach pro

2 nguồn2011
bioinformatics

Network-based metabolomics analysis

Network-based metabolomics analysis integrates quantitative metabolite profiling data with biological network structures — metabolic pathways, protein-metabolite interaction graphs, and disease networks — to reveal coordinated biochemical disruptions that individual metabolite lists would miss. Rather than treating eac

2 nguồn2005
bioinformatics

Network-based microbiome diversity analysis

Network-based microbiome diversity analysis integrates graph-theoretic co-occurrence network inference with classical alpha- and beta-diversity metrics to characterize the structural organization of microbial communities. Rather than treating taxa as independent entities, the method models pairwise microbial associatio

2 nguồn2012
bioinformatics

Network-based pathway enrichment analysis

Network-based pathway enrichment analysis integrates molecular interaction networks — protein-protein interactions, signalling graphs, or gene regulatory networks — with omics measurements to identify biological pathways that are coordinately altered in a condition. Unlike classical over-representation or gene-set enri

2 nguồn2002
bioinformatics

Network-based Phylogenetic Analysis

Network-based phylogenetic analysis constructs graph-structured representations of evolutionary relationships that explicitly accommodate reticulate events — including hybridization, horizontal gene transfer, recombination, and incomplete lineage sorting — which strictly bifurcating phylogenetic trees cannot represent.

2 nguồn1992
bioinformatics

Network-based RNA-seq differential expression

Network-based RNA-seq differential expression analysis integrates conventional differential expression testing with gene interaction networks — such as protein-protein interaction graphs or weighted co-expression networks — to identify not just individual differentially expressed genes but coherent, biologically meanin

2 nguồn2002
bioinformatics

Network-based single-cell RNA-seq analysis

Network-based single-cell RNA-seq analysis extends standard scRNA-seq workflows by constructing and interrogating molecular interaction networks — gene regulatory networks, co-expression networks, or cell-cell communication graphs — from single-cell transcriptomic data. Rather than treating each gene independently, thi

2 nguồn2015
bioinformatics

Network-based variant calling

Network-based (graph-genome) variant calling replaces the conventional single linear reference genome with a variation graph — a network in which nodes represent sequence segments and edges represent known alternative paths through the genome. Reads are mapped onto this graph, enabling detection of SNPs, indels, and st

2 nguồn2017
deep learning

Neural Architecture Search

Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.

2 nguồn2017
deep learning

Neural ODE

A Neural ODE, introduced by Chen and colleagues in 2018, models a hidden state as the continuous solution of an ordinary differential equation whose dynamics are parameterised by a neural network. It generalises the limiting case of residual connections, making it well suited to irregularly spaced time series and physi

2 nguồn2018
deep learning

Neural Radiance Fields (NeRF)

Neural Radiance Fields (NeRF) is a method introduced by Mildenhall et al. in 2020 that represents a 3D scene as a continuous function parameterized by a neural network. Given multi-view images of a scene, NeRF learns to predict the color and density of light rays at any spatial location and viewing angle, enabling nove

1 nguồn2020
deep learning

Neural Style Transfer

Neural Style Transfer (NST) is a deep-learning image synthesis technique, introduced by Gatys, Ecker, and Bethge in 2015, that separates the semantic content of one image from the visual texture and artistic style of another, then recombines them into a single synthesized image by iteratively optimizing pixel values to

3 nguồn2015
particle physics

Neutrino Oscillation Analysis

Neutrino oscillation analysis is the study of flavor mixing in the neutrino sector, where neutrinos born as one flavor (electron, muon, or tau) spontaneously convert into other flavors as they propagate. Measuring oscillation parameters provides crucial evidence for physics beyond the Standard Model and tests our under

3 nguồn1957
nuclear physics

Neutron Activation Analysis

Neutron activation analysis (NAA) is an analytical technique for determining elemental composition by bombarding samples with neutrons to produce radioactive isotopes, invented by de Hevesy and Levi in 1936. By measuring decay gamma rays from irradiated samples, NAA quantifies trace and major elements with high sensiti

2 nguồn1936
nuclear physics

Neutron Transport Calculation

Neutron transport calculation is a computational method for determining the distribution and behavior of neutrons in a nuclear medium, developed during the Manhattan Project in the 1940s. It solves the Boltzmann transport equation to predict neutron flux, energy spectra, and reaction rates essential for reactor design

2 nguồn1942
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