103 citations · 310 across the 23 of their papers we have counts for
23 papers
PyPose v0.6: The Imperative Programming Interface for Robotics
Zitong Zhan, Xiangfu Li, Qihang Li +33
PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has…
Randomized algorithms for precise measurement of differentially-private, personalized recommendations
Allegra Laro, Yanqing Chen, Hao He +1
Personalized recommendations form an important part of today's internet ecosystem, helping artists and creators to reach interested users, and helping users to discover new and eng…
SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark
Liang Xu, Anqi Li, Lei Zhu +7
Large language models (LLMs) have shown the potential to be integrated into human daily lives. Therefore, user preference is the most critical criterion for assessing LLMs' perform…
Taxonomy-Structured Domain Adaptation
Tianyi Liu, Zihao Xu, Hao He +3
Domain adaptation aims to mitigate distribution shifts among different domains. However, traditional formulations are mostly limited to categorical domains, greatly simplifying nua…
ReTR: Modeling Rendering Via Transformer for Generalizable Neural Surface Reconstruction
Yixun Liang, Hao He, Ying-cong Chen
Generalizable neural surface reconstruction techniques have attracted great attention in recent years. However, they encounter limitations of low confidence depth distribution and…
Weakly Supervised AUC Optimization: A Unified Partial AUC Approach
Zheng Xie, Yu Liu, Hao-Yuan He +2
Since acquiring perfect supervision is usually difficult, real-world machine learning tasks often confront inaccurate, incomplete, or inexact supervision, collectively referred to…
Label Name is Mantra: Unifying Point Cloud Segmentation across Heterogeneous Datasets
Yixun Liang, Hao He, Shishi Xiao +2
Point cloud segmentation is a fundamental task in 3D vision that serves a wide range of applications. Although great progresses have been made these years, its practical usability…
Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks
Jierun Chen, Shiu-hong Kao, Hao He +4
To design fast neural networks, many works have been focusing on reducing the number of floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does no…
FedDAR: Federated Domain-Aware Representation Learning
Aoxiao Zhong, Hao He, Zhaolin Ren +2
Cross-silo Federated learning (FL) has become a promising tool in machine learning applications for healthcare. It allows hospitals/institutions to train models with sufficient dat…
PyPose: A Library for Robot Learning with Physics-based Optimization
Chen Wang, Dasong Gao, Kuan Xu +34
Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physic…
DETRs with Hybrid Matching
Ding Jia, Yuhui Yuan, Haodi He +6
One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to r…
RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation
Haodi He, Yuhui Yuan, Xiangyu Yue +1
The segmentation task has traditionally been formulated as a complete-label pixel classification task to predict a class for each pixel from a fixed number of predefined semantic c…
Controlling Directions Orthogonal to a Classifier
Yilun Xu, Hao He, Tianxiao Shen +1
We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer. While orthogonal decomposition is dire…
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party
Yimin Huang, Xinyu Feng, Wanwan Wang +3
Federated learning allows multiple participants to conduct joint modeling without disclosing their local data. Vertical federated learning (VFL) handles the situation where partici…
Mid-flight Forecasting for CPA Lines in Online Advertising
Hao He, Tian Zhou, Lihua Ren +2
For Verizon MediaDemand Side Platform(DSP), forecasting of ad campaign performance not only feeds key information to the optimization server to allow the system to operate on a hig…
An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Tian Zhou, Hao He, Shengjun Pan +9
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions. Due to the fundamental difference bet…
Improving Video Instance Segmentation via Temporal Pyramid Routing
Xiangtai Li, Hao He, Yibo Yang +5
Video Instance Segmentation (VIS) is a new and inherently multi-task problem, which aims to detect, segment, and track each instance in a video sequence. Existing approaches are ma…
BoundarySqueeze: Image Segmentation as Boundary Squeezing
Hao He, Xiangtai Li, Yibo Yang +5
This paper proposes a novel method for high-quality image segmentation of both objects and scenes. Inspired by the dilation and erosion operations in morphological image processing…
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
Xiangtai Li, Hao He, Xia Li +6
Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two…
Enhanced Boundary Learning for Glass-like Object Segmentation
Hao He, Xiangtai Li, Guangliang Cheng +5
Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. Howev…
Bid Shading by Win-Rate Estimation and Surplus Maximization
Shengjun Pan, Brendan Kitts, Tian Zhou +8
This paper describes a new win-rate based bid shading algorithm (WR) that does not rely on the minimum-bid-to-win feedback from a Sell-Side Platform (SSP). The method uses a modifi…
Truly Proximal Policy Optimization
Yuhui Wang, Hao He, Chao Wen +1
Proximal policy optimization (PPO) is one of the most successful deep reinforcement-learning methods, achieving state-of-the-art performance across a wide range of challenging task…
Trust Region-Guided Proximal Policy Optimization
Yuhui Wang, Hao He, Xiaoyang Tan +1
Proximal policy optimization (PPO) is one of the most popular deep reinforcement learning (RL) methods, achieving state-of-the-art performance across a wide range of challenging ta…