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Found 24 papers, 6 papers with code
XMusic: Towards a Generalized and Controllable Symbolic Music Generation Framework
XComposer contains a Generator and a Selector.
MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization
In this paper, we propose a self-supervised music representation learning model for music understanding.
SongEditor: Adapting Zero-Shot Song Generation Language Model as a Multi-Task Editor
The emergence of novel generative modeling paradigms, particularly audio language models, has significantly advanced the field of song generation.
How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses
To quantify the quality of highlighted praise components identified by GPT models, we introduced a Modified Intersection over Union (M-IoU) score.
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification
Within the scope of natural language processing, the domain of multi-label text classification is uniquely challenging due to its expansive and uneven label distribution.
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning
Expected Loss Reduction (ELR) focuses on a Bayesian estimate of the reduction in classification error, and more general costs fit in the same framework.
AcademicGPT: Empowering Academic Research
Our training corpus mainly consists of academic papers, thesis, content from some academic domain, high-quality Chinese data and others.
FMViT: A multiple-frequency mixing Vision Transformer
On CoreML, FMViT outperforms MobileOne by 2. 6% in top-1 accuracy on the ImageNet dataset, with inference latency comparable to MobileOne (78. 5% vs. 75. 9%).
Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?
Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem.
Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognition
Active learning, a widely adopted technique for enhancing machine learning models in text and image classification tasks with limited annotation resources, has received relatively little attention in the domain of Named Entity Recognition (NER).
Using Large Language Models to Provide Explanatory Feedback to Human Tutors
Research demonstrates learners engaging in the process of producing explanations to support their reasoning, can have a positive impact on learning.
Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets
We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution.
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification
Then, the study investigates how the AL methods can select informative samples to support DA classifiers in the AL sampling process.
AUC Maximization for Low-Resource Named Entity Recognition
To the best of our knowledge, this is the first work that brings AUC maximization to the NER setting.
Low Resource Named Entity Recognition
named-entity-recognition
+2
Modelling Pre-fatigue, Low-velocity Impact and Fatigue behaviours of Composite Helicopter Tail Structures under Multipoint Coordinated Loading Spectrum
This paper aims to numerically study the pre-fatigue, low-velocity impact (LVI) and fatigue progressive damage behaviours of a full-scale composite helicopter tail structure under multipoint coordinated loading spectrum.
ECO v1: Towards Event-Centric Opinion Mining
Events are considered as the fundamental building blocks of the world.
Diversity Enhanced Active Learning with Strictly Proper Scoring Rules
We convert the ELR framework to estimate the increase in (strictly proper) scores like log probability or negative mean square error, which we call Bayesian Estimate of Mean Proper Scores (BEMPS).
Matrix Factorization on GPUs with Memory Optimization and Approximate Computing
Current MF implementations are either optimized for a single machine or with a need of a large computer cluster but still are insufficient.
An influence-based fast preceding questionnaire model for elderly assessments
The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90. 56% with a prediction accuracy of 98. 39%.
Dilated Recurrent Neural Networks
To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures.
Ranked #24 on
Sequential Image Classification
on Sequential MNIST
Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs
Matrix factorization (MF) is employed by many popular algorithms, e. g., collaborative filtering.
Distributed, Parallel, and Cluster Computing Performance
Sentiment Analysis based on User Tag for Traditional Chinese Medicine in Weibo
To the best of our knowledge, the present work is the first attempt that applies sentiment analysis to the domain of TCM on Sina Weibo (a twitter-like microblogging service in China).
Cultural Vocal Bursts Intensity Prediction
Sentiment Analysis
+1
