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Found 14 papers, 1 papers with code
FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?
The rapid evolution of Large Language Models (LLMs) highlights the necessity for ethical considerations and data integrity in AI development, particularly emphasizing the role of FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
DMC4ML: Data Movement Complexity for Machine Learning
The greatest demand for today's computing is machine learning.
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores
However, the application of tensor decomposition is largely hindered by the exponential increment of the computational complexity and storage consumption with the size of tensors.
BGGAN: Generative AI Enables Representing Brain Structure-Function Connections for Alzheimer's Disease
Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BGGAN can employ features of direct and indirect brain regions to learn the mapping function between structural domain and functional domain.
NBIAS: A Natural Language Processing Framework for Bias Identification in Text
Therefore, it is crucial to detect and remove these biases to ensure the fair and ethical use of data.
Mitigating Bias in Conversations: A Hate Speech Classifier and Debiaser with Prompts
Discriminatory language and biases are often present in hate speech during conversations, which usually lead to negative impacts on targeted groups such as those based on race, gender, and religion.
Dbias: Detecting biases and ensuring Fairness in news articles
Because of the increasing use of data-centric systems and algorithms in machine learning, the topic of fairness is receiving a lot of attention in the academic and broader literature.
Active Learning on a Programmable Photonic Quantum Processor
Here, we design and implement two AL-enpowered variational quantum classifiers, to investigate the potential applications and effectiveness of AL in quantum machine learning.
Parameter-Parallel Distributed Variational Quantum Algorithm
Variational quantum algorithms (VQAs) have emerged as a promising near-term technique to explore practical quantum advantage on noisy intermediate-scale quantum (NISQ) devices.
Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System
News reading is also driven by a blend of a reader's long-term and short-term interests.
The Imprint of Large Scale Structure on the Ultra-High-Energy Cosmic Ray Sky
Without specifying any particular production mechanism, but only postulating that the source distribution follows the matter distribution of the local Universe, a good accounting of the magnitude, direction and energy dependence of the dipole anisotropy at energies above $8 \times 10^{18}$ eV is obtained, after taking into account the impact of energy losses during propagation (the "GZK horizon"), diffusion in extragalactic magnetic field and deflections in the Galactic magnetic field (GMF).
High Energy Astrophysical Phenomena
News Recommender System: A review of recent progress, challenges, and opportunities
Nowadays, more and more news readers tend to read news online where they have access to millions of news articles from multiple sources.
Quantum-Inspired Support Vector Machine
Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points.
Codestitcher: Inter-Procedural Basic Block Layout Optimization
Modern software executes a large amount of code.
Programming Languages
