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Found 32 papers, 13 papers with code
Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods
Stiff ordinary differential equations (ODEs) are common in many science and engineering fields, but standard neural ODE approaches struggle to accurately learn these stiff systems, posing a significant barrier to widespread adoption of neural ODEs.
Training Stiff Neural Ordinary Differential Equations with Implicit Single-Step Methods
Stiff systems of ordinary differential equations (ODEs) are pervasive in many science and engineering fields, yet standard neural ODE approaches struggle to learn them.
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law: domains characterized by their reliance on professional expertise, challenging data acquisition, high-stakes, and stringent regulatory compliance.
Quokka: An Open-source Large Language Model ChatBot for Material Science
This paper presents the development of a specialized chatbot for materials science, leveraging the Llama-2 language model, and continuing pre-training on the expansive research articles in the materials science domain from the S2ORC dataset.
A Survey on Detection of LLMs-Generated Content
The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of sectors, including media, cybersecurity, public discourse, and education.
Zero-Shot Detection of Machine-Generated Codes
We then modify the previous zero-shot text detection method, DetectGPT (Mitchell et al., 2023) by utilizing a surrogate white-box model to estimate the probability of the rightmost tokens, allowing us to identify code snippets generated by language models.
Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models
This study serves as a clarion call for a collective effort to overhaul and fortify the safety of open-source LLMs against malicious attackers.
Bayesian polynomial neural networks and polynomial neural ordinary differential equations
Symbolic regression with polynomial neural networks and polynomial neural ordinary differential equations (ODEs) are two recent and powerful approaches for equation recovery of many science and engineering problems.
DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text
However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs.
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.
Dynamic Prompting: A Unified Framework for Prompt Tuning
Experimental results underscore the significant performance improvement achieved by dynamic prompt tuning across a wide range of tasks, including NLP tasks, vision recognition tasks, and vision-language tasks.
MatKB: Semantic Search for Polycrystalline Materials Synthesis Procedures
In this paper, we present a novel approach to knowledge extraction and retrieval using Natural Language Processing (NLP) techniques for material science.
OASum: Large-Scale Open Domain Aspect-based Summarization
Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model.
PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text
Scientific action graphs extraction from materials synthesis procedures is important for reproducible research, machine automation, and material prediction.
Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
Our method first addresses irregularity in each single modality by (1) modeling irregular time series by dynamically incorporating hand-crafted imputation embeddings into learned interpolation embeddings via a gating mechanism, and (2) casting a series of clinical note representations as multivariate irregular time series and tackling irregularity via a time attention mechanism.
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.
Few-Shot Document-Level Event Argument Extraction
To fill this gap, we present FewDocAE, a Few-Shot Document-Level Event Argument Extraction benchmark, based on the existing document-level event extraction dataset.
Document-level Event Extraction
Event Argument Extraction
+2
Interpretable Polynomial Neural Ordinary Differential Equations
Neural networks have the ability to serve as universal function approximators, but they are not interpretable and don't generalize well outside of their training region.
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.
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.
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.
Robust and integrative Bayesian neural networks for likelihood-free parameter inference
State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference.
Model Optimization for Deep Space Exploration via Simulators and Deep Learning
Machine learning, and eventually true artificial intelligence techniques, are extremely important advancements in astrophysics and astronomy.
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.
Advances in Deep Space Exploration via Simulators & Deep Learning
The StarLight program conceptualizes fast interstellar travel via small wafer satellites (wafersats) that are propelled by directed energy.
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
Selecting the Metric in Hamiltonian Monte Carlo
We present a selection criterion for the Euclidean metric adapted during warmup in a Hamiltonian Monte Carlo sampler that makes it possible for a sampler to automatically pick the metric based on the model and the availability of warmup draws.
Computation Methodology
