2.4k citations · 12.3k across the 115 of their papers we have counts for
12 papers · 1 filter
Re-evaluating the Need for Multimodal Signals in Unsupervised Grammar Induction
Boyi Li, Rodolfo Corona, Karttikeya Mangalam +7
Are multimodal inputs necessary for grammar induction? Recent work has shown that multimodal training inputs can improve grammar induction. However, these improvements are based on…
Latent Diffusion for Language Generation
Justin Lovelace, Varsha Kishore, Chao Wan +2
Diffusion models have achieved great success in modeling continuous data modalities such as images, audio, and video, but have seen limited use in discrete domains such as language…
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning
Ruihan Wu, Xiangyu Chen, Chuan Guo +1
Gradient inversion attack enables recovery of training samples from model gradients in federated learning (FL), and constitutes a serious threat to data privacy. To mitigate this v…
Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs
Youya Xia, Josephine Monica, Wei-Lun Chao +3
A self-driving car must be able to reliably handle adverse weather conditions (e.g., snowy) to operate safely. In this paper, we investigate the idea of turning sensor inputs (i.e.…
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions
Carlos A. Diaz-Ruiz, Youya Xia, Yurong You +11
Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under n…
Differentially Private Multi-Party Data Release for Linear Regression
Ruihan Wu, Xin Yang, Yuanshun Yao +4
Differentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects. However the majority of prior work has focu…
Long-term Control for Dialogue Generation: Methods and Evaluation
Ramya Ramakrishnan, Hashan Buddhika Narangodage, Mauro Schilman +2
Current approaches for controlling dialogue response generation are primarily focused on high-level attributes like style, sentiment, or topic. In this work, we focus on constraine…
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
Felix Wu, Kwangyoun Kim, Shinji Watanabe +4
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete repres…
Learning to Detect Mobile Objects from LiDAR Scans Without Labels
Yurong You, Katie Z Luo, Cheng Perng Phoo +5
Current 3D object detectors for autonomous driving are almost entirely trained on human-annotated data. Although of high quality, the generation of such data is laborious and costl…
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
Yurong You, Katie Z Luo, Xiangyu Chen +6
Self-driving cars must detect vehicles, pedestrians, and other traffic participants accurately to operate safely. Small, far-away, or highly occluded objects are particularly chall…
Does Label Differential Privacy Prevent Label Inference Attacks?
Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger +1
Label differential privacy (label-DP) is a popular framework for training private ML models on datasets with public features and sensitive private labels. Despite its rigorous priv…
Language-driven Semantic Segmentation
Boyi Li, Kilian Q. Weinberger, Serge Belongie +2
We present LSeg, a novel model for language-driven semantic image segmentation. LSeg uses a text encoder to compute embeddings of descriptive input labels (e.g., "grass" or "buildi…