Search Results for author:
Found 20 papers, 14 papers with code
Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble
Like many Natural Language Processing tasks, Thai word segmentation is domain-dependent.
Ranked #1 on
Thai Word Segmentation
on WS160
(using extra training data)
Robust Fragment-Based Framework for Cross-lingual Sentence Retrieval
Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents.
CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question Answering
A common approach to CL-ReQA is to create a multilingual sentence embedding space such that question-answer pairs across different languages are close to each other.
WangchanLion and WangchanX MRC Eval
Our model is based on SEA-LION and a collection of instruction following datasets.
An Efficient Self-Supervised Cross-View Training For Sentence Embedding
In this paper, we propose a framework called Self-supervised Cross-View Training (SCT) to narrow the performance gap between large and small PLMs.
Typo-Robust Representation Learning for Dense Retrieval
One of the main challenges of dense retrieval in real-world settings is the handling of queries containing misspelled words.
Zero-guidance Segmentation Using Zero Segment Labels
CLIP has enabled new and exciting joint vision-language applications, one of which is open-vocabulary segmentation, which can locate any segment given an arbitrary text query.
Thai Wav2Vec2.0 with CommonVoice V8
However, most of these ASR models are available in English; only a minority of the models are available in Thai.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
ReCasNet: Improving consistency within the two-stage mitosis detection framework
Existing approaches utilize a two-stage pipeline: the detection stage for identifying the locations of potential mitotic cells and the classification stage for refining prediction confidences.
Set Prediction in the Latent Space
Set prediction tasks require the matching between predicted set and ground truth set in order to propagate the gradient signal.
High resolution weakly supervised localization architectures for medical images
In medical imaging, Class-Activation Map (CAM) serves as the main explainability tool by pointing to the region of interest.
Reducing Spelling Inconsistencies in Code-Switching ASR using Contextualized CTC Loss
Code-Switching (CS) remains a challenge for Automatic Speech Recognition (ASR), especially character-based models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning
This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.
Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework
Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases.
Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations
In the Thai sentence segmentation experiments, our model reduced the relative error by 7. 4% and 10. 5% compared with the baseline models on the Orchid and UGWC datasets, respectively.
Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning
The focus is mainly on three types of brain responses: non-imagery EEG (\textit{background EEG}), (\textit{pure imagery}) EEG, and EEG during the transitional period between background EEG and pure imagery (\textit{transitional imagery}).
Affective EEG-Based Person Identification Using the Deep Learning Approach
\textcolor{red}{We proposed a cascade of deep learning using a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)}.
Rice Classification Using Spatio-Spectral Deep Convolutional Neural Network
In this work, we develop a non-destructive rice variety classification system that benefits from the synergy between hyperspectral imaging and deep convolutional neural network (CNN).
Prediction-Adaptation-Correction Recurrent Neural Networks for Low-Resource Language Speech Recognition
In this paper, we investigate the use of prediction-adaptation-correction recurrent neural networks (PAC-RNNs) for low-resource speech recognition.
