Search Results for author:
Found 24 papers, 10 papers with code
Yet@SMM4H’22: Improved BERT-based classification models with Rdrop and PolyLoss
This paper describes our approach for 11 classification tasks (Task1a, Task2a, Task2b, Task3a, Task3b, Task4, Task5, Task6, Task7, Task8 and Task9) from Social Media Mining for Health (SMM4H) 2022 Shared Tasks.
Using Structural Similarity and Kolmogorov-Arnold Networks for Anatomical Embedding of 3-hinge Gyrus
The 3-hinge gyrus (3HG) is a newly defined folding pattern, which is the conjunction of gyri coming from three directions in cortical folding.
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
GP-GPT: Large Language Model for Gene-Phenotype Mapping
To address these challenges, we present GP-GPT, the first specialized large language model for genetic-phenotype knowledge representation and genomics relation analysis.
PertEval: Unveiling Real Knowledge Capacity of LLMs with Knowledge-Invariant Perturbations
Our toolkit further includes a suite of \textbf{response consistency analyses} that compare performance on raw vs. perturbed test sets to precisely assess LLMs' genuine knowledge capacity.
MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI
Background: Segmentation of organs and structures in abdominal MRI is useful for many clinical applications, such as disease diagnosis and radiotherapy.
Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance.
Linear optimal transport subspaces for point set classification
Our approach employs the Linear Optimal Transport (LOT) transform to obtain a linear embedding of set-structured data.
Semantic Image Synthesis for Abdominal CT
As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis.
ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptation
In this paper, we propose ECLM, an edge-cloud collaborative learning framework for rapid model adaptation for dynamic edge environments.
From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation
As AI systems continue to grow, particularly generative models like Large Language Models (LLMs), their rigorous evaluation is crucial for development and deployment.
From Sparse to Precise: A Practical Editing Approach for Intracardiac Echocardiography Segmentation
Furthermore, our framework accommodates multiple edits to the segmentation output in a sequential manner without compromising previous edits.
A sliced-Wasserstein distance-based approach for out-of-class-distribution detection
These methods often require extensive training data, are computationally expensive, and are vulnerable to out-of-distribution samples, e. g., adversarial attacks.
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
However, the corresponding survey paper is relatively scarce, and it is imminent to make a summary of this field.
End-to-End Signal Classification in Signed Cumulative Distribution Transform Space
This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT).
Point Spread Function Estimation of Defocus
In this work, we develop an alternative method to estimate the precise mathematical model of the point spread function to describe the defocus process.
Local Sliced-Wasserstein Feature Sets for Illumination-invariant Face Recognition
Face recognition is then performed using a nearest subspace in R-CDT domain of local gradient distributions.
Invariance encoding in sliced-Wasserstein space for image classification with limited training data
However, they are known to underperform when training data are limited and thus require data augmentation strategies that render the method computationally expensive and not always effective.
Nearest Subspace Search in The Signed Cumulative Distribution Transform Space for 1D Signal Classification
This paper presents a new method to classify 1D signals using the signed cumulative distribution transform (SCDT).
A Systematic Survey of Blockchained Federated Learning
Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL.
Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities
Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields.
Ferryman as SemEval-2020 Task 5: Optimized BERT for Detecting Counterfactuals
The main purpose of this article is to state the effect of using different methods and models for counterfactual determination and detection of causal knowledge.
Will\_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy
Mixing languages are widely used in social media, especially in multilingual societies like India.
Human Gender Classification: A Review
Gender contains a wide range of information regarding to the characteristics difference between male and female.
