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Found 11 papers, 5 papers with code
Exploring Human-Like Thinking in Search Simulations with Large Language Models
As existing search datasets do not include users' thought processes, we conducted a user study to collect a new dataset enriched with users' explicit thinking.
Learning Cascade Ranking as One Network
Recent advances such as RankFlow and FS-LTR have introduced interaction-aware training paradigms but still struggle to 1) align training objectives with the goal of the entire cascade ranking (i. e., end-to-end recall) and 2) learn effective collaboration patterns for different stages.
Adaptive$^2$: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling
To the best of our knowledge, Adaptive$^2$ is the first approach to automatically learn both domain identification and adaptation in online advertising, opening new research directions for this area.
Scaling Laws for Online Advertisement Retrieval
To the best of our knowledge, this is the first work to study the scaling laws for online advertisement retrieval of real-world systems, showing great potential for scaling law in advertising system optimization.
Reinfier and Reintrainer: Verification and Interpretation-Driven Safe Deep Reinforcement Learning Frameworks
In this work, we propose a novel verification-driven interpretation-in-the-loop framework Reintrainer to develop trustworthy DRL models, which are guaranteed to meet the expected constraint properties.
GraphEdit: Large Language Models for Graph Structure Learning
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures.
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
Moreover, we theoretically establish that the number of distinguishable eigenvalues plays a pivotal role in determining the expressive power of spectral graph neural networks.
Seeing is not Believing: An Identity Hider for Human Vision Privacy Protection
Concretely, the identity hider benefits from two specially designed modules: 1) The virtual face generation module generates a virtual face with a new appearance by manipulating the latent space of StyleGAN2.
Controllability of Multilayer Networked Sampled-data Systems
This paper explores the state controllability of multilayer networked sampled-data systems with inter-layer couplings, where zero-order holders (ZOHs) are on the control and transmission channels.
Controllability of Networked Sampled-data Systems
The controllability of networked sampled-data systems with zero-order holders on the control and transmission channels is explored, where single- and multi-rate sampling patterns are considered, respectively.
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution
Convolutional neural networks (CNNs) have demonstrated superior performance in super-resolution (SR).
