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Found 7 papers, 2 papers with code
Computationally-Efficient Linear Periodically Time-Variant Digital PLL Modeling Using Conversion Matrices and Uncorrelated Upsampling
This paper introduces a conversion matrix method for linear periodically time-variant (LPTV) digital phase-locked loop (DPLL) phase noise modeling that offers precise and computationally efficient results to enable rapid design iteration and optimization.
Cross Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction
Deep neural networks (DNNs) that incorporated lifelong sequential modeling (LSM) have brought great success to recommendation systems in various social media platforms.
Scaling Law of Large Sequential Recommendation Models
We find that scaling up the model size can greatly boost the performance on these challenging tasks, which again verifies the benefits of large recommendation models.
Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems.
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data
The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications.
Large Language Models are Zero-Shot Rankers for Recommender Systems
Recently, large language models (LLMs) (e. g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.
Improving Visual Recognition using Ambient Sound for Supervision
Our brains combine vision and hearing to create a more elaborate interpretation of the world.
