2.4k citations · 12.3k across the 115 of their papers we have counts for
10 papers · 1 filter
Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps
Katie Z Luo, Xinshuo Weng, Yan Wang +5
Autonomous driving has traditionally relied heavily on costly and labor-intensive High Definition (HD) maps, hindering scalability. In contrast, Standard Definition (SD) maps are m…
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
Katie Z Luo, Zhenzhen Liu, Xiangyu Chen +7
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Alt…
Correction with Backtracking Reduces Hallucination in Summarization
Zhenzhen Liu, Chao Wan, Varsha Kishore +3
Abstractive summarization aims at generating natural language summaries of a source document that are succinct while preserving the important elements. Despite recent advances, neu…
Pre-Training LiDAR-Based 3D Object Detectors Through Colorization
Tai-Yu Pan, Chenyang Ma, Tianle Chen +7
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we in…
Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features
Travis Zhang, Katie Luo, Cheng Perng Phoo +5
The rapid development of 3D object detection systems for self-driving cars has significantly improved accuracy. However, these systems struggle to generalize across diverse driving…
On the Effectiveness of Offline RL for Dialogue Response Generation
Paloma Sodhi, Felix Wu, Ethan R. Elenberg +2
A common training technique for language models is teacher forcing (TF). TF attempts to match human language exactly, even though identical meanings can be expressed in different w…
IncDSI: Incrementally Updatable Document Retrieval
Varsha Kishore, Chao Wan, Justin Lovelace +2
Differentiable Search Index is a recently proposed paradigm for document retrieval, that encodes information about a corpus of documents within the parameters of a neural network a…
Learning Iterative Neural Optimizers for Image Steganography
Xiangyu Chen, Varsha Kishore, Kilian Q Weinberger
Image steganography is the process of concealing secret information in images through imperceptible changes. Recent work has formulated this task as a classic constrained optimizat…
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
Yurong You, Cheng Perng Phoo, Katie Z Luo +5
For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts. One potential solu…
Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
Zhenzhen Liu, Jin Peng Zhou, Yufan Wang +1
Unsupervised out-of-distribution detection (OOD) seeks to identify out-of-domain data by learning only from unlabeled in-domain data. We present a novel approach for this task - Li…