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A systematic review on affective computing: Emotion models, databases, and recent advances
Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …
[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives
X Liu, C Yoo, F **ng, H Oh, G El Fakhri… - … on Signal and …, 2022 - nowpublishers.com
Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …
domains, partly because of its ability to learn from data and achieve impressive performance …
Test-time training with masked autoencoders
Y Gandelsman, Y Sun, X Chen… - Advances in Neural …, 2022 - proceedings.neurips.cc
Test-time training adapts to a new test distribution on the fly by optimizing a model for each
test input using self-supervision. In this paper, we use masked autoencoders for this one …
test input using self-supervision. In this paper, we use masked autoencoders for this one …
Leace: Perfect linear concept erasure in closed form
N Belrose, D Schneider-Joseph… - Advances in …, 2023 - proceedings.neurips.cc
Abstract Concept erasure aims to remove specified features from a representation. It can
improve fairness (eg preventing a classifier from using gender or race) and interpretability …
improve fairness (eg preventing a classifier from using gender or race) and interpretability …
XLM-T: Multilingual language models in Twitter for sentiment analysis and beyond
F Barbieri, LE Anke, J Camacho-Collados - arxiv preprint arxiv …, 2021 - arxiv.org
Language models are ubiquitous in current NLP, and their multilingual capacity has recently
attracted considerable attention. However, current analyses have almost exclusively focused …
attracted considerable attention. However, current analyses have almost exclusively focused …
State of the art: a review of sentiment analysis based on sequential transfer learning
Recently, sequential transfer learning emerged as a modern technique for applying the
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …
Information retrieval: recent advances and beyond
KA Hambarde, H Proenca - IEEE Access, 2023 - ieeexplore.ieee.org
This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …
utilized in the first and second stages of the typical information retrieval processing chain …
Test-time training with self-supervision for generalization under distribution shifts
In this paper, we propose Test-Time Training, a general approach for improving the
performance of predictive models when training and test data come from different …
performance of predictive models when training and test data come from different …
A survey of graph neural networks in various learning paradigms: methods, applications, and challenges
In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …
many problems in computer vision, speech recognition, natural language processing, and …
A survey on recent approaches for natural language processing in low-resource scenarios
MA Hedderich, L Lange, H Adel, J Strötgen… - arxiv preprint arxiv …, 2020 - arxiv.org
Deep neural networks and huge language models are becoming omnipresent in natural
language applications. As they are known for requiring large amounts of training data, there …
language applications. As they are known for requiring large amounts of training data, there …