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Found 17 papers, 4 papers with code
From Code Generation to Software Testing: AI Copilot with Context-Based RAG
The rapid pace of large-scale software development places increasing demands on traditional testing methodologies, often leading to bottlenecks in efficiency, accuracy, and coverage.
Aligning Crowd-sourced Human Feedback for Reinforcement Learning on Code Generation by Large Language Models
This paper studies how AI-assisted programming and large language models (LLM) improve software developers' ability via AI tools (LLM agents) like Github Copilot and Amazon CodeWhisperer, while integrating human feedback to enhance reinforcement learning (RLHF) with crowd-sourced computation to enhance text-to-code generation.
Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning
Advances in artificial intelligence (AI) including foundation models (FMs), are increasingly transforming human society, with smart city driving the evolution of urban living. Meanwhile, vehicle crowdsensing (VCS) has emerged as a key enabler, leveraging vehicles' mobility and sensor-equipped capabilities.
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
Extensive experimental results demonstrate that FedADP effectively manages the trade-off between unlearning efficiency and privacy protection.
FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method
In addition, we introduce a dynamic stopping mechanism to optimize the termination of the unlearning process.
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
Federated Learning (FL) is a distributed machine learning paradigm that achieves a globally robust model through decentralized computation and periodic model synthesis, primarily focusing on the global model's accuracy over aggregated datasets of all participating clients.
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
Federated learning (FL) is a collaborative machine learning approach that enables multiple clients to train models without sharing their private data.
Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot Framework
The advancements in cloud-based Large Languages Models (LLMs) have revolutionized AI-assisted programming.
OpenRANet: Neuralized Spectrum Access by Joint Subcarrier and Power Allocation with Optimization-based Deep Learning
OpenRANet also serves as a foundation for designing resource-constrained AI-native wireless optimization strategies for broader scenarios like multi-cell systems, satellite-terrestrial networks, and future Open RAN deployments with complex power consumption requirements.
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations
To address this, we systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations in various steps of LLM evaluation.
Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients manipulate their updates to affect the global model.
Large Language Model-Driven Classroom Flipping: Empowering Student-Centric Peer Questioning with Flipped Interaction
We develop an LLM-driven chatbot software that digitizes various elements of classroom flipping and facilitates the assessment of students using these routines to deliver peer-generated questions.
FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning
Traditional federated learning uses the number of samples to calculate the weights of each client model and uses this fixed weight value to fusion the global model.
Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models
This paper presents an AI-assisted programming tool called Copilot for Xcode for program composition and design to support human software developers.
Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review
This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks.
EuclidNet: Deep Visual Reasoning for Constructible Problems in Geometry
In this paper, we present a deep learning-based framework for solving geometric construction problems through visual reasoning, which is useful for automated geometry theorem proving.
An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic
In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data.
