Search Results for author:
Found 12 papers, 3 papers with code
Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
Machine learning algorithms minimizing average risk are susceptible to distributional shifts.
CVSNet: A Computer Implementation for Central Visual System of The Brain
In computer vision, different basic blocks are created around different matrix operations, and models based on different basic blocks have achieved good results.
A Survey of Diffusion Models in Natural Language Processing
In NLP, diffusion models have been used in a variety of applications, such as natural language generation, sentiment analysis, topic modeling, and machine translation.
Exploring and Exploiting Data Heterogeneity in Recommendation
In this work, we focus on exploring two representative categories of heterogeneity in recommendation data that is the heterogeneity of prediction mechanism and covariate distribution and propose an algorithm that explores the heterogeneity through a bilevel clustering method.
Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization
Yet the current definition of flatness discussed in SAM and its follow-ups are limited to the zeroth-order flatness (i. e., the worst-case loss within a perturbation radius).
Improving Question Answering with Generation of NQ-like Questions
We demonstrate that in a low resource setting, using the generated data improves the QA performance over the baseline system on both NQ and QB data.
CausPref: Causal Preference Learning for Out-of-Distribution Recommendation
In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios, leading to the sharp decline of performance in testing environments.
Up-to-Down Network: Fusing Multi-Scale Context for 3D Semantic Scene Completion
An efficient 3D scene perception algorithm is a vital component for autonomous driving and robotics systems.
Ranked #6 on
3D Semantic Scene Completion
on SemanticKITTI
Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds
Specifically, the network takes a raw point cloud as input, and merges the features from the segmentation branch into the completion branch hierarchically to provide semantic information.
Ranked #4 on
3D Semantic Scene Completion
on SemanticKITTI
RFNet: Recurrent Forward Network for Dense Point Cloud Completion
The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline.
Counterfactual Prediction for Bundle Treatment
Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields.
F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking
A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates.
