Search Results for author:
Found 40 papers, 14 papers with code
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
The core of FedCAP is a model update calibration mechanism to help a server capture the differences in the direction and magnitude of model updates among clients.
Brain Tumor Classification on MRI in Light of Molecular Markers
As a result, the diagnostic results are unreliable by the transfer learning model.
Fairness in Large Language Models in Three Hours
Large Language Models (LLMs) have demonstrated remarkable success across various domains but often lack fairness considerations, potentially leading to discriminatory outcomes against marginalized populations.
AI-Driven Healthcare: A Survey on Ensuring Fairness and Mitigating Bias
Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc.
Fairness Definitions in Language Models Explained
Specifically, we begin with a brief introduction to LMs and fairness in LMs, followed by a comprehensive, up-to-date overview of existing fairness notions in LMs and the introduction of a novel taxonomy that categorizes these concepts based on their foundational principles and operational distinctions.
BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection
To tackle this issue, we propose an innovative 2D-oriented backdoor attack against LiDAR-camera fusion methods for 3D object detection, named BadFusion, for preserving trigger effectiveness throughout the entire fusion process.
On the Efficiency of Privacy Attacks in Federated Learning
Second, we propose three early-stopping techniques to effectively reduce the computational costs of these privacy attacks.
Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI
In the rapidly evolving landscape of generative artificial intelligence (AI), the increasingly pertinent issue of copyright infringement arises as AI advances to generate content from scraped copyrighted data, prompting questions about ownership and protection that impact professionals across various careers.
Fairness in Large Language Models: A Taxonomic Survey
Additionally, the concept of fairness in LLMs is discussed categorically, summarizing metrics for evaluating bias in LLMs and existing algorithms for promoting fairness.
History, Development, and Principles of Large Language Models-An Introductory Survey
Language models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and generation.
ZS4C: Zero-Shot Synthesis of Compilable Code for Incomplete Code Snippets using LLMs
On average, ZS4C can infer more accurate import statements (with an F1 score of 0. 98) than SnR, with an improvement of 8. 5% in the F1.
The Internet of Responsibilities-Connecting Human Responsibilities using Big Data and Blockchain
Accountability in the workplace is critically important and remains a challenging problem, especially with respect to workplace safety management.
Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules
Pre-trained language models (PLMs) have achieved remarkable results on NLP tasks but at the expense of huge parameter sizes and the consequent computational costs.
Practical Deep Dispersed Watermarking with Synchronization and Fusion
Compared with state-of-the-art approaches, our blind watermarking can achieve better performance: averagely improve the bit accuracy by 5. 28\% and 5. 93\% against single and combined attacks, respectively, and show less file size increment and better visual quality.
Rethinking Learning Rate Tuning in the Era of Large Language Models
Second, we present LRBench++ to benchmark learning rate policies and facilitate learning rate tuning for both traditional DNNs and LLMs.
Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning
So in this paper, we try to use equations as IMRs to solve the numerical reasoning task by addressing two problems: (1) Theoretically, how to prove that the equation is an IMR with higher generation accuracy than programs; (2) Empirically, how to improve the generation accuracy of equations with LLMs.
Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement
The goal of this study is to implement diffusion models for speech enhancement (SE).
Individual Fairness under Uncertainty
Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is an established area in ML.
Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking
The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern.
Preventing Discriminatory Decision-making in Evolving Data Streams
However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting.
Fair Decision-making Under Uncertainty
There has been concern within the artificial intelligence (AI) community and the broader society regarding the potential lack of fairness of AI-based decision-making systems.
Attention Mechanism based Cognition-level Scene Understanding
Given a question-image input, the Visual Commonsense Reasoning (VCR) model can predict an answer with the corresponding rationale, which requires inference ability from the real world.
Longitudinal Fairness with Censorship
Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistic.
Fairness Amidst Non-IID Graph Data: Current Achievements and Future Directions
The importance of understanding and correcting algorithmic bias in machine learning (ML) has led to an increase in research on fairness in ML, which typically assumes that the underlying data is independent and identically distributed (IID).
Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences
The need to analyze information from streams arises in a variety of applications.
A Generic Knowledge Based Medical Diagnosis Expert System
The proposed disease diagnosis system also uses a graphical user interface (GUI) to facilitate users to interact with the expert system.
A survey on datasets for fairness-aware machine learning
For a deeper understanding of bias in the datasets, we investigate the interesting relationships using exploratory analysis.
FARF: A Fair and Adaptive Random Forests Classifier
This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyperparameters that alters fairness-accuracy balance.
Online Fairness-Aware Learning with Imbalanced Data Streams
Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc.
Interpretable Visual Understanding with Cognitive Attention Network
While image understanding on recognition-level has achieved remarkable advancements, reliable visual scene understanding requires comprehensive image understanding on recognition-level but also cognition-level, which calls for exploiting the multi-source information as well as learning different levels of understanding and extensive commonsense knowledge.
Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory
Moreover, the proposed model provides intuitive interpretation into visual commonsense reasoning.
Complex Spectral Mapping With Attention Based Convolution Recurrent Neural Network for Speech Enhancement
Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality.
LSTM Based Sentiment Analysis for Cryptocurrency Prediction
Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information.
Using Machine Learning to Automate Mammogram Images Analysis
Early detection of breast cancer in X-ray mammography is believed to have effectively reduced the mortality rate.
A Data-driven Human Responsibility Management System
An ideal safe workplace is described as a place where staffs fulfill responsibilities in a well-organized order, potential hazardous events are being monitored in real-time, as well as the number of accidents and relevant damages are minimized.
Online Decision Trees with Fairness
In this paper, we propose a novel framework of online decision tree with fairness in the data stream with possible distribution drifting.
Disentangled Dynamic Graph Deep Generation
Extending existing deep generative models from static to dynamic graphs is a challenging task, which requires to handle the factorization of static and dynamic characteristics as well as mutual interactions among node and edge patterns.
FAHT: An Adaptive Fairness-aware Decision Tree Classifier
However, there is a growing concern about the accountability and fairness of the employed models by the fact that often the available historic data is intrinsically discriminatory, i. e., the proportion of members sharing one or more sensitive attributes is higher than the proportion in the population as a whole when receiving positive classification, which leads to a lack of fairness in decision support system.
A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification
A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space with the topology relations preserved.
