Search Results for author:
Found 7 papers, 3 papers with code
Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
We conduct a theoretical analysis of the learning process for the weights in the linear component, revealing how group-wise properties of training data influence them.
F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving
We take the first step in addressing this challenge and introduce a baseline, F2BEV, to generate discretized BEV height maps and BEV semantic segmentation maps from fisheye images.
Causal Incremental Graph Convolution for Recommender System Retraining
Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution.
Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series
Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models.
Cirrus: A Long-range Bi-pattern LiDAR Dataset
Point density varies significantly across such a long range, and different scanning patterns further diversify object representation in LiDAR.
Range Adaptation for 3D Object Detection in LiDAR
To the best of our knowledge, this paper is the first attempt to study cross-range LiDAR adaptation for object detection in point clouds.
Towards Recovery of Conditional Vectors from Conditional Generative Adversarial Networks
In this work, we show that it is possible to recover both latent and conditional vectors from generated images given the generator of a conditional generative adversarial network.
