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Found 19 papers, 8 papers with code
Learning Actionable World Models for Industrial Process Control
To go from (passive) process monitoring to active process control, an effective AI system must learn about the behavior of the complex system from very limited training data, forming an ad-hoc digital twin with respect to process in- and outputs that captures the consequences of actions on the process's world.
AI Agents for Computer Use: A Review of Instruction-based Computer Control, GUI Automation, and Operator Assistants
Instruction-based computer control agents (CCAs) execute complex action sequences on personal computers or mobile devices to fulfill tasks using the same graphical user interfaces as a human user would, provided instructions in natural language.
Augmenting the Veracity and Explanations of Complex Fact Checking via Iterative Self-Revision with LLMs
However, previous studies on explanation generation has shown several limitations, such as being confined to English scenarios, involving overly complex inference processes, and not fully unleashing the potential of the mutual feedback between veracity labels and explanation texts.
Personalized Image Generation with Large Multimodal Models
Personalized content filtering, such as recommender systems, has become a critical infrastructure to alleviate information overload.
Self-Evolutionary Large Language Models through Uncertainty-Enhanced Preference Optimization
Iterative preference optimization has recently become one of the de-facto training paradigms for large language models (LLMs), but the performance is still underwhelming due to too much noisy preference data yielded in the loop.
Personalized Interpretation on Federated Learning: A Virtual Concepts approach
These conceptual vectors could be pre-defined or refined in a human-in-the-loop process or be learnt via the optimization procedure of the federated learning system.
LiPost: Improved Content Understanding With Effective Use of Multi-task Contrastive Learning
This work provides a robust foundation for vertical teams across LinkedIn to customize and fine-tune the LLM to their specific applications.
Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
Besides, by personalized integration of domain features from other domains for each user and the innovation in the training mode, the DFEI framework can yield more accurate conversion identification.
Calibrating the Confidence of Large Language Models by Eliciting Fidelity
Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless.
Client-supervised Federated Learning: Towards One-model-for-all Personalization
Personalized Federated Learning (PerFL) is a new machine learning paradigm that delivers personalized models for diverse clients under federated learning settings.
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions
However, due to the dynamic nature of the industrial processes and environment, it is impractical to acquire large-scale labeled data for standard deep learning training for every slightly different case anew.
Personalization Disentanglement for Federated Learning: An explainable perspective
Personalized federated learning (PFL) jointly trains a variety of local models through balancing between knowledge sharing across clients and model personalization per client.
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
However, this separation of the recommendation model and users' private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings.
GPFedRec: Graph-guided Personalization for Federated Recommendation
The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner.
Dual Personalization on Federated Recommendation
Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings.
Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion.
Magnonic frequency comb through nonlinear magnon-skyrmion scattering
An optical frequency comb consists of a set of discrete and equally spaced frequencies and has found wide applications in the synthesis over broad spectral frequencies of electromagnetic wave and precise optical frequency metrology.
Mesoscale and Nanoscale Physics Optics
Spin-wave focusing induced skyrmion generation
We propose a new method to generate magnetic skyrmions through spin-wave focusing in chiral ferromagnets. A lens is constructed to focus spin waves by a curved interface between two ferromagnetic thin films with different perpendicular magnetic anisotropies.
Mesoscale and Nanoscale Physics
Long short-term memory networks in memristor crossbars
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence.
Emerging Technologies Applied Physics
