Search Results for author:
Found 15 papers, 6 papers with code
MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking
Zero-shot dialogue state tracking (DST) transfers knowledge to unseen domains, reducing the cost of annotating new datasets.
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space
Previous studies attempted to introduce discrete or Gaussian-based continuous latent variables to address the one-to-many problem, but the diversity is limited.
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction
Most existing methods focus on the network architecture design of the CTR model for better accuracy and suffer from the data sparsity problem.
Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction
The insights from this subset reveal the user's decision-making process related to the candidate item, improving prediction accuracy.
Automatic extraction and 3D reconstruction of split wire from point cloud data based on improved DPC algorithm
In order to solve the problem of point cloud data splitting improved by DPC algorithm, a research on automatic separation and 3D reconstruction of point cloud data split lines is proposed.
Robust Safe Reinforcement Learning under Adversarial Disturbances
To address this challenge, this paper proposes a robust safe reinforcement learning framework that tackles worst-case disturbances.
STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation
Specifically, we construct subgraphs of spatial, temporal, spatial-temporal, and global views respectively to precisely characterize the user's interests in various contexts.
Deep Context Interest Network for Click-Through Rate Prediction
Many works focus on user behavior modeling to improve CTR prediction performance.
DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
Point-of-Interest (POI) recommendation plays a vital role in various location-aware services.
Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling
In Meituan, one of the largest e-commerce platform in China, an item is typically displayed with its image and whether a user clicks the item or not is usually influenced by its image, which implies that user's image behaviors are helpful for understanding user's visual preference and improving the accuracy of CTR prediction.
6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark
We propose a set of toy grocery objects, whose physical instantiations are readily available for purchase and are appropriately sized for robotic grasping and manipulation.
Continual Learning for CTR Prediction: A Hybrid Approach
To address this problem, we formulate CTR prediction as a continual learning task and propose COLF, a hybrid COntinual Learning Framework for CTR prediction, which has a memory-based modular architecture that is designed to adapt, learn and give predictions continuously when faced with non-stationary drifting click data streams.
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications.
Deep Position-wise Interaction Network for CTR Prediction
Click-through rate (CTR) prediction plays an important role in online advertising and recommender systems.
Camera-to-Robot Pose Estimation from a Single Image
We show experimental results for three different camera sensors, demonstrating that our approach is able to achieve accuracy with a single frame that is better than that of classic off-line hand-eye calibration using multiple frames.
Robotics
