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Found 19 papers, 7 papers with code
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction
Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success.
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments
We instantiate the path on structured environments and provide feedback to edit the path if anything goes wrong.
UFO: A UI-Focused Agent for Windows OS Interaction
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.
Xpert: Empowering Incident Management with Query Recommendations via Large Language Models
This paper presents a thorough empirical study on the utilization of queries of KQL, a DSL employed for incident management in a large-scale cloud management system at Microsoft.
TaskWeaver: A Code-First Agent Framework
TaskWeaver provides support for rich data structures, flexible plugin usage, and dynamic plugin selection, and leverages LLM coding capabilities for complex logic.
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
To address these limitations, we introduce a novel thought prompting approach called "Everything of Thoughts" (XoT) to defy the law of "Penrose triangle of existing thought paradigms.
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection
To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.
Improving Rating Prediction in Multi-Criteria Recommender Systems via a Collective Factor Model
Experiments on three real-world datasets show that our proposed architecture achieves up to 13. 14% lower prediction error over baseline approaches.
MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation
In this paper, we introduce a novel Multi-View Approach with Hybrid Attentive Networks (MV-HAN) for contents retrieval at the matching stage of recommender systems.
QuickSkill: Novice Skill Estimation in Online Multiplayer Games
However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player.
Field-aware Variational Autoencoders for Billion-scale User Representation Learning
Moreover, we deploy the proposed method in real-world applications and conduct online A/B tests in a look-alike system.
Predicting ratings in multi-criteria recommender systems via a collective factor model
In a multi-criteria recommender system, users are allowed to give an overall rating to an item and provide a score on each of its attribute.
Ranked #1 on
Recommendation Systems
on BeerAdvocate
Deep Neural Mobile Networking
The next generation of mobile networks is set to become increasingly complex, as these struggle to accommodate tremendous data traffic demands generated by ever-more connected devices that have diverse performance requirements in terms of throughput, latency, and reliability.
CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting
This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources.
Multi-Service Mobile Traffic Forecasting via Convolutional Long Short-Term Memories
Network slicing is increasingly used to partition network infrastructure between different mobile services.
Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets with Multi-stream Inputs
Experiments conducted with a dataset that we collect in a mock-up car environment demonstrate that the proposed InterCNN with MobileNet convolutional blocks can classify 9 different behaviors with 73. 97% accuracy, and 5 'aggregated' behaviors with 81. 66% accuracy.
Deep Learning in Mobile and Wireless Networking: A Survey
One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications.
Ranked #1 on
Link Prediction
on SINS
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network
Large-scale mobile traffic analytics is becoming essential to digital infrastructure provisioning, public transportation, events planning, and other domains.
Sequence-to-point learning with neural networks for nonintrusive load monitoring
Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem.
