Papers General Classification
“General Classification” 태그가 달린 논문 14,585편 · 필터 해제
Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications
This paper studies the utility of using data analytics and machine learning techniques for identifying, classifying, and characterizing the dynamics of large-scale parallel (MPI) programs. To this end, we run microbenchm…
ClusteringGeneral ClassificationProgressive Class Semantic Matching for Semi-supervised Text Classification
Semi-supervised learning is a promising way to reduce the annotation cost for text-classification. Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-supervised learning methods achieved impressiv…
General ClassificationLanguage ModelingLanguage ModellingSemi-Supervised Text Classification+1EXACT: How to Train Your Accuracy
Classification tasks are usually evaluated in terms of accuracy. However, accuracy is discontinuous and cannot be directly optimized using gradient ascent. Popular methods minimize cross-entropy, hinge loss, or other sur…
General Classificationimage-classificationImage ClassificationA Classification of $G$-invariant Shallow Neural Networks
When trying to fit a deep neural network (DNN) to a $G$-invariant target function with $G$ a group, it only makes sense to constrain the DNN to be $G$-invariant as well. However, there can be many different ways to do th…
General ClassificationNeural Architecture SearchFew-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling
Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks. Nevertheless, we observe that these current benchmarks do not accurately repres…
Few-Shot Image ClassificationGeneral Classificationimage-classificationImage Classification+2Sibylvariant Transformations for Robust Text Classification
The vast majority of text transformation techniques in NLP are inherently limited in their ability to expand input space coverage due to an implicit constraint to preserve the original class label. In this work, we propo…
Adversarial RobustnessDefect DetectionGeneral ClassificationText ClassificationTowards Feature Selection for Ranking and Classification Exploiting Quantum Annealers
Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the…
feature selectionGeneral ClassificationGenerating Representative Samples for Few-Shot Classification
Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot class representations are often biased due to data scarcity. To mitigate this issue, we propose to generate visual sample…
Few-Shot LearningGeneral ClassificationFastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector Machines
Neural Networks and related Deep Learning methods are currently at the leading edge of technologies used for classifying objects. However, they generally demand large amounts of time and data for model training; and thei…
General ClassificationInterpretable Machine LearningMinimax risk classifiers with 0-1 loss
Supervised classification techniques use training samples to learn a classification rule with small expected 0-1 loss (error probability). Conventional methods enable tractable learning and provide out-of-sample generali…
ClassificationGeneral ClassificationFeature Generation for Long-tail Classification
The visual world naturally exhibits an imbalance in the number of object or scene instances resulting in a \emph{long-tailed distribution}. This imbalance poses significant challenges for classification models based on d…
ClassificationFew-Shot LearningGeneral Classificationimbalanced classification+1Binary classification of proteins by a Machine Learning approach
In this work we present a system based on a Deep Learning approach, by using a Convolutional Neural Network, capable of classifying protein chains of amino acids based on the protein description contained in the Protein …
BIG-bench Machine LearningBinary ClassificationClassificationDeep Learning+2Is Attention always needed? A Case Study on Language Identification from Speech
Language Identification (LID) is a crucial preliminary process in the field of Automatic Speech Recognition (ASR) that involves the identification of a spoken language from audio samples. Contemporary systems that can pr…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)General ClassificationLanguage Identification+3Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers
Hyperbolic space can naturally embed hierarchies, unlike Euclidean space. Hyperbolic Neural Networks (HNNs) exploit such representational power by lifting Euclidean features into hyperbolic space for classification, outp…
Adversarial RobustnessGeneral ClassificationOut-of-Distribution DetectionRule-Based Classification of Hyperspectral Imaging Data
Due to its high spatial and spectral information content, hyperspectral imaging opens up new possibilities for a better understanding of data and scenes in a wide variety of applications. An essential part of this proces…
ClassificationGeneral ClassificationVideo Swin Transformer
The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks. These video models are all built o…
Action ClassificationAction RecognitionGeneral ClassificationInductive Bias+3Canonical-Correlation-Based Fast Feature Selection for Structural Health Monitoring
Feature selection refers to the process of selecting useful features for machine learning tasks, and it is also a key step for structural health monitoring (SHM). This paper proposes a fast feature selection algorithm by…
Edge-computingfeature selectionGeneral ClassificationStructural Health MonitoringXBNet : An Extremely Boosted Neural Network
Neural networks have proved to be very robust at processing unstructured data like images, text, videos, and audio. However, it has been observed that their performance is not up to the mark in tabular data; hence tree-b…
Anomaly DetectionBreast Cancer DetectionDiabetes PredictionFraud Detection+3Tabular Data: Deep Learning is Not All You Need
A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular dat…
AllAutoMLDeep LearningGeneral ClassificationSA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence
In this paper, we present a process of building a social listening system based on aspect-based sentiment analysis in Vietnamese from creating a dataset to building a real application. Firstly, we create UIT-ViSFD, a Vie…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)General ClassificationSentiment Analysis+4