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Found 23 papers, 5 papers with code
SportQA: A Benchmark for Sports Understanding in Large Language Models
A deep understanding of sports, a field rich in strategic and dynamic content, is crucial for advancing Natural Language Processing (NLP).
FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records
In the high-stakes realm of healthcare, ensuring fairness in predictive models is crucial.
Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models
To address this gap, our study undertakes a thorough evaluation of Gemini's performance in complex reasoning tasks that necessitate the integration of commonsense knowledge across modalities.
TRAM: Benchmarking Temporal Reasoning for Large Language Models
In this paper, we introduce TRAM, a temporal reasoning benchmark composed of ten datasets, encompassing various temporal aspects of events such as order, arithmetic, frequency, and duration, designed to facilitate a comprehensive evaluation of the temporal reasoning capabilities of large language models (LLMs).
Advanced Volleyball Stats for All Levels: Automatic Setting Tactic Detection and Classification with a Single Camera
Our frameworks combine setting ball trajectory recognition with a novel set trajectory classifier to generate comprehensive and advanced statistical data.
Metacognitive Prompting Improves Understanding in Large Language Models
This study underscores the potential to amplify the understanding abilities of LLMs and highlights the benefits of mirroring human introspective reasoning in NLU tasks.
An Empirical Study on the Robustness of the Segment Anything Model (SAM)
The Segment Anything Model (SAM) is a foundation model for general image segmentation.
Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding
In this study, we conduct a comprehensive evaluation of state-of-the-art LLMs, namely GPT-3. 5, GPT-4, and Bard, within the realm of clinical language understanding tasks.
Brain Model State Space Reconstruction Using an LSTM Neural Network
Approach An LSTM filter was trained on simulated EEG data generated by a neural mass model using a wide range of parameters.
VREN: Volleyball Rally Dataset with Expression Notation Language
The second goal is to introduce a volleyball descriptive language to fully describe the rally processes in the games and apply the language to our dataset.
Optical Neural Ordinary Differential Equations
Increasing the layer number of on-chip photonic neural networks (PNNs) is essential to improve its model performance.
Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning
As critically ill patients frequently develop anemia or coagulopathy, transfusion of blood products is a frequent intervention in the Intensive Care Units (ICU).
Enhancing Transformer Efficiency for Multivariate Time Series Classification
Most current multivariate time series (MTS) classification algorithms focus on improving the predictive accuracy.
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis
In this paper, we propose a multimodal Transformer model for early sepsis prediction, using the physiological time series data and clinical notes for each patient within $36$ hours of ICU admission.
Empirical Quantitative Analysis of COVID-19 Forecasting Models
In this paper, we perform quantitative analysis of COVID-19 forecasting of confirmed cases and deaths across different regions in the United States with different forecasting horizons, and evaluate the relative impacts of the following three dimensions on the predictive performance (improvement and variation) through different evaluation metrics: model selection, hyperparameter tuning, and the length of time series required for training.
Morse-STF: Improved Protocols for Privacy-Preserving Machine Learning
Finally, we introduce Morse-STF, an end-to-end privacy-preserving system for machine learning training that leverages all these improved protocols.
Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networks
Multiple organ failure (MOF) is a severe syndrome with a high mortality rate among Intensive Care Unit (ICU) patients.
BART based semantic correction for Mandarin automatic speech recognition system
Although automatic speech recognition (ASR) systems achieved significantly improvements in recent years, spoken language recognition error occurs which can be easily spotted by human beings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients
However, there is a lack of deep learning methods that can model the relationship between measurements, clinical notes and mortality outcomes.
Empirical Analysis of Machine Learning Configurations for Prediction of Multiple Organ Failure in Trauma Patients
However, blindly pursuing complex classifiers is unwise as it also brings the risk of greater performance variation.
How Much Does It Hurt: A Deep Learning Framework for Chronic Pain Score Assessment
Chronic pain is defined as pain that lasts or recurs for more than 3 to 6 months, often long after the injury or illness that initially caused the pain has healed.
An Auxiliary Classifier Generative Adversarial Framework for Relation Extraction
Relation extraction models suffer from limited qualified training data.
A Deep Learning Framework for Classification of in vitro Multi-Electrode Array Recordings
In this paper, we address the problem of classifying in vitro MEA recordings of mouse and human neuronal cultures from different genotypes, where there is no easy way to directly utilize raw sequences as inputs to train an end-to-end classification model.
Cultural Vocal Bursts Intensity Prediction
General Classification
