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Found 25 papers, 7 papers with code
Predicting Overtakes in Trucks Using CAN Data
Safe overtakes in trucks are crucial to prevent accidents, reduce congestion, and ensure efficient traffic flow, making early prediction essential for timely and informed driving decisions.
Pushing The Limit of LLM Capacity for Text Classification
The value of text classification's future research has encountered challenges and uncertainties, due to the extraordinary efficacy demonstrated by large language models (LLMs) across numerous downstream NLP tasks.
Climate Change from Large Language Models
We propose a set of comprehensive metrics to evaluate the climate crisis knowledge, incorporating indicators from 10 different perspectives.
Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives
Person re-identification (Re-ID) technology plays an increasingly crucial role in intelligent surveillance systems.
Towards Subject Agnostic Affective Emotion Recognition
Our domain adaptation approach is augmented through meta-learning, which consists of a recurrent neural network, a classifier, and a distributional shift controller based on a sum-decomposable function.
DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations
Large language models (LLMs) and their variants have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks, which has presented a new vision for the development of NLP.
VISU at WASSA 2023 Shared Task: Detecting Emotions in Reaction to News Stories Leveraging BERT and Stacked Embeddings
Our system, VISU, participated in the WASSA 2023 Shared Task (3) of Emotion Classification from essays written in reaction to news articles.
An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification
This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting.
Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting
Also, by F1-score and probability distribution analysis, we demonstrate that DVGNN better reflects the causal relationship and uncertainty of dynamic graphs.
Huatuo-26M, a Large-scale Chinese Medical QA Dataset
Moreover, we also experimentally show the benefit of the proposed dataset in many aspects: (i) trained models for other QA datasets in a zero-shot fashion; and (ii) as external knowledge for retrieval-augmented generation (RAG); and (iii) improving existing pre-trained language models by using the QA pairs as a pre-training corpus in continued training manner.
A Novel Deep Learning based Model for Erythrocytes Classification and Quantification in Sickle Cell Disease
We chose SCD as a model disease condition due to the presence of diverse erythrocyte morphologies in the blood samples of SCD patients.
A Survey on Few-Shot Class-Incremental Learning
Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective.
Natural Language Reasoning, A Survey
This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically.
An Overview of Violence Detection Techniques: Current Challenges and Future Directions
The Big Video Data generated in today's smart cities has raised concerns from its purposeful usage perspective, where surveillance cameras, among many others are the most prominent resources to contribute to the huge volumes of data, making its automated analysis a difficult task in terms of computation and preciseness.
Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk
However, the humor aspect of natural language is relatively under-investigated, especially in the age of pre-trained language models.
EEG based Emotion Recognition: A Tutorial and Review
Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic.
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment.
Pre-trained Language Models in Biomedical Domain: A Systematic Survey
In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks.
Deep Learning for Depression Recognition with Audiovisual Cues: A Review
Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection.
Structural block driven - enhanced convolutional neural representation for relation extraction
In this paper, we propose a novel lightweight relation extraction approach of structural block driven - convolutional neural learning.
Kernel Method based on Non-Linear Coherent State
In this paper, by mapping datasets to a set of non-linear coherent states, the process of encoding inputs in quantum states as a non-linear feature map is re-interpreted.
Robustness analytics to data heterogeneity in edge computing
Second, we study a classifier aggregated from a collection of local classifiers trained by data through active sampling at the edge.
Quantum-like Structure in Multidimensional Relevance Judgements
A large number of studies in cognitive science have revealed that probabilistic outcomes of certain human decisions do not agree with the axioms of classical probability theory.
Multi-class Classification Model Inspired by Quantum Detection Theory
Machine Learning has become very famous currently which assist in identifying the patterns from the raw data.
