1 code implementation • 2 Feb 2024 • William Jongwon Han, Diana Gomez, Avi Alok, Chaojing Duan, Michael A. Rosenberg, Douglas Weber, Emerson Liu, Ding Zhao
Understanding the irregular electrical activity of atrial fibrillation (AFib) has been a key challenge in electrocardiography.
1 code implementation • 16 Jan 2024 • Zhepeng Cen, Zuxin Liu, Zitong Wang, Yihang Yao, Henry Lam, Ding Zhao
Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment.
no code implementations • 23 Dec 2023 • Yihang Yao, Zuxin Liu, Zhepeng Cen, Peide Huang, Tingnan Zhang, Wenhao Yu, Ding Zhao
Leveraging insights from this framework and recognizing the significance of \textit{redundant} and \textit{conflicting} constraint conditions, we introduce the Gradient Shaping (GradS) method for general Lagrangian-based safe RL algorithms to improve the training efficiency in terms of both reward and constraint satisfaction.
no code implementations • 19 Dec 2023 • Wenhao Ding, Yulong Cao, Ding Zhao, Chaowei Xiao, Marco Pavone
Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the potential risks associated with real-world testing.
no code implementations • 31 Oct 2023 • Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu, Jiacheng Zhu, Yuming Niu, Ding Zhao
However, maintaining safety in diverse safety-critical scenarios remains a significant challenge due to long-tailed and unforeseen scenarios absent from offline datasets.
no code implementations • 31 Oct 2023 • Aditya Wadaskar, Ding Zhao, Ibrahim Pehlivan, Danijela Cabric
Wideband millimeter-wave and terahertz (THz) systems can facilitate simultaneous data communication with multiple spatially separated users.
no code implementations • 19 Oct 2023 • Mengdi Xu, Peide Huang, Wenhao Yu, Shiqi Liu, Xilun Zhang, Yaru Niu, Tingnan Zhang, Fei Xia, Jie Tan, Ding Zhao
This paper investigates the feasibility of imbuing robots with the ability to creatively use tools in tasks that involve implicit physical constraints and long-term planning.
no code implementations • 10 Oct 2023 • Fan Yang, Wenxuan Zhou, Zuxin Liu, Ding Zhao, David Held
This work introduces a novel approach that combines RL with trajectory optimization to manage this trade-off effectively.
no code implementations • 9 Oct 2023 • Zuxin Liu, Jesse Zhang, Kavosh Asadi, Yao Liu, Ding Zhao, Shoham Sabach, Rasool Fakoor
Inspired by recent advancements in parameter-efficient fine-tuning in language domains, we explore efficient fine-tuning techniques -- e. g., Bottleneck Adapters, P-Tuning, and Low-Rank Adaptation (LoRA) -- in TAIL to adapt large pretrained models for new tasks with limited demonstration data.
no code implementations • 2 Oct 2023 • Yuyou Zhang, Yaru Niu, Xingyu Liu, Ding Zhao
Instead of perceiving the hyper-redundancy and flexibility of snake robots as mere challenges, there lies an unexplored potential in leveraging these traits to enhance robustness and generalizability at the control policy level.
no code implementations • 29 Sep 2023 • Weiran Wang, Zelin Wu, Diamantino Caseiro, Tsendsuren Munkhdalai, Khe Chai Sim, Pat Rondon, Golan Pundak, Gan Song, Rohit Prabhavalkar, Zhong Meng, Ding Zhao, Tara Sainath, Pedro Moreno Mengibar
Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scenarios.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 22 Sep 2023 • Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao
The current certification process for assessing robustness is costly and time-consuming due to the extensive number of image projections required for Monte Carlo sampling in the 3D camera motion space.
1 code implementation • NeurIPS 2023 • Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu
Regardless of the particular task we want them to perform in an environment, there are often shared safety constraints we want our agents to respect.
1 code implementation • 27 Jul 2023 • Lingdong Kong, Yaru Niu, Shaoyuan Xie, Hanjiang Hu, Lai Xing Ng, Benoit R. Cottereau, Ding Zhao, Liangjun Zhang, Hesheng Wang, Wei Tsang Ooi, Ruijie Zhu, Ziyang Song, Li Liu, Tianzhu Zhang, Jun Yu, Mohan Jing, Pengwei Li, Xiaohua Qi, Cheng Jin, Yingfeng Chen, Jie Hou, Jie Zhang, Zhen Kan, Qiang Ling, Liang Peng, Minglei Li, Di Xu, Changpeng Yang, Yuanqi Yao, Gang Wu, Jian Kuai, Xianming Liu, Junjun Jiang, Jiamian Huang, Baojun Li, Jiale Chen, Shuang Zhang, Sun Ao, Zhenyu Li, Runze Chen, Haiyong Luo, Fang Zhao, Jingze Yu
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
3 code implementations • 15 Jun 2023 • Zuxin Liu, Zijian Guo, Haohong Lin, Yihang Yao, Jiacheng Zhu, Zhepeng Cen, Hanjiang Hu, Wenhao Yu, Tingnan Zhang, Jie Tan, Ding Zhao
This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the training and deployment phases.
no code implementations • 15 Jun 2023 • Miao Li, Wenhao Ding, Ding Zhao
The prominence of embodied Artificial Intelligence (AI), which empowers robots to navigate, perceive, and engage within virtual environments, has attracted significant attention, owing to the remarkable advancements in computer vision and large language models.
1 code implementation • 7 Jun 2023 • JieLin Qiu, Jiacheng Zhu, William Han, Aditesh Kumar, Karthik Mittal, Claire Jin, Zhengyuan Yang, Linjie Li, JianFeng Wang, Ding Zhao, Bo Li, Lijuan Wang
To address these challenges and provide a comprehensive dataset for this new direction, we have meticulously curated the \textbf{MMSum} dataset.
no code implementations • 18 May 2023 • Wenhao Ding, Tong Che, Ding Zhao, Marco Pavone
Recently, reward-conditioned reinforcement learning (RCRL) has gained popularity due to its simplicity, flexibility, and off-policy nature.
no code implementations • 17 Apr 2023 • Mengdi Xu, Yuchen Lu, Yikang Shen, Shun Zhang, Ding Zhao, Chuang Gan
To address this challenge, we propose a new framework, called Hyper-Decision Transformer (HDT), that can generalize to novel tasks from a handful of demonstrations in a data- and parameter-efficient manner.
no code implementations • 16 Apr 2023 • JieLin Qiu, Peide Huang, Makiya Nakashima, Jaehyun Lee, Jiacheng Zhu, Wilson Tang, Pohao Chen, Christopher Nguyen, Byung-Hak Kim, Debbie Kwon, Douglas Weber, Ding Zhao, David Chen
Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare.
no code implementations • 13 Apr 2023 • JieLin Qiu, Jiacheng Zhu, Shiqi Liu, William Han, Jingqi Zhang, Chaojing Duan, Michael Rosenberg, Emerson Liu, Douglas Weber, Ding Zhao
Automated interpretation of electrocardiograms (ECG) has garnered significant attention with the advancements in machine learning methodologies.
no code implementations • 15 Mar 2023 • Steven M. Hernandez, Ding Zhao, Shaojin Ding, Antoine Bruguier, Rohit Prabhavalkar, Tara N. Sainath, Yanzhang He, Ian McGraw
Such a model allows us to achieve always-on ambient speech recognition on edge devices with low-memory neural processors.
no code implementations • 9 Mar 2023 • Yaru Niu, Shiyu Jin, Zeqing Zhang, Jiacheng Zhu, Ding Zhao, Liangjun Zhang
In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning.
1 code implementation • 14 Feb 2023 • Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao
Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment.
no code implementations • 4 Feb 2023 • Jiacheng Zhu, JieLin Qiu, Aritra Guha, Zhuolin Yang, XuanLong Nguyen, Bo Li, Ding Zhao
Our work provides a new perspective of model robustness through the lens of Wasserstein geodesic-based interpolation with a practical off-the-shelf strategy that can be combined with existing robust training methods.
no code implementations • 23 Jan 2023 • Wenhao Ding, Nathalie Majcherczyk, Mohit Deshpande, Xuewei Qi, Ding Zhao, Rajasimman Madhivanan, Arnie Sen
Active perception describes a broad class of techniques that couple planning and perception systems to move the robot in a way to give the robot more information about the environment.
no code implementations • 21 Jan 2023 • JieLin Qiu, William Han, Jiacheng Zhu, Mengdi Xu, Michael Rosenberg, Emerson Liu, Douglas Weber, Ding Zhao
The learned embeddings are evaluated on two downstream tasks: (1) automatic ECG diagnosis report generation, and (2) zero-shot cardiovascular disease detection.
no code implementations • 15 Dec 2022 • JieLin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao, Bo Li, Mu Li
Multimodal image-text models have shown remarkable performance in the past few years.
no code implementations • 8 Dec 2022 • Wenhao Yu, Chengxiang Zhao, Jiaxin Liu, Yingkai Yang, Xiaohan Ma, Jun Li, Weida Wang, Hong Wang, Ding Zhao, Xiaosong Hu
To address these challenges, this paper aims to digitize traffic law comprehensively and provide an application for online monitoring of legal driving behavior for autonomous vehicles.
no code implementations • 21 Oct 2022 • Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, JieLin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators.
no code implementations • 21 Oct 2022 • Shiqi Liu, Mengdi Xu, Piede Huang, Yongkang Liu, Kentaro Oguchi, Ding Zhao
Continual reinforcement learning aims to sequentially learn a variety of tasks, retaining the ability to perform previously encountered tasks while simultaneously developing new policies for novel tasks.
1 code implementation • 18 Oct 2022 • Peide Huang, Mengdi Xu, Jiacheng Zhu, Laixi Shi, Fei Fang, Ding Zhao
Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks.
no code implementations • 12 Oct 2022 • JieLin Qiu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Ding Zhao, Hailin Jin
Livestream videos have become a significant part of online learning, where design, digital marketing, creative painting, and other skills are taught by experienced experts in the sessions, making them valuable materials.
no code implementations • 10 Oct 2022 • JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin
Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding.
1 code implementation • 4 Oct 2022 • Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao
To this end, we study the robustness of the visual perception model under camera motion perturbations to investigate the influence of camera motion on robotic perception.
no code implementations • 16 Sep 2022 • Mengdi Xu, Zuxin Liu, Peide Huang, Wenhao Ding, Zhepeng Cen, Bo Li, Ding Zhao
A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} constraints to avoid catastrophic failures, and {generalizing} to unseen scenarios during deployments.
no code implementations • 14 Sep 2022 • Rongmei Lin, Yonghui Xiao, Tien-Ju Yang, Ding Zhao, Li Xiong, Giovanni Motta, Françoise Beaufays
Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 8 Sep 2022 • Chulin Xie, Zhong Cao, Yunhui Long, Diange Yang, Ding Zhao, Bo Li
However, training AVs usually requires a large amount of training data collected from different driving environments (e. g., cities) as well as different types of personal information (e. g., working hours and routes).
1 code implementation • 10 Aug 2022 • William Han, JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Douglas Weber, Bo Li, Ding Zhao
In addition, we provide interpretations of the performance improvement: (1) feature distribution shows the effectiveness of the alignment module for discovering and encoding the relationship between EEG and language; (2) alignment weights show the influence of different language semantics as well as EEG frequency features; (3) brain topographical maps provide an intuitive demonstration of the connectivity in the brain regions.
no code implementations • 2 Aug 2022 • Jiacheng Zhu, JieLin Qiu, Zhuolin Yang, Douglas Weber, Michael A. Rosenberg, Emerson Liu, Bo Li, Ding Zhao
In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals.
1 code implementation • 19 Jul 2022 • Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
As a pivotal component to attaining generalizable solutions in human intelligence, reasoning provides great potential for reinforcement learning (RL) agents' generalization towards varied goals by summarizing part-to-whole arguments and discovering cause-and-effect relations.
no code implementations • 27 Jun 2022 • Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Joshua B. Tenenbaum, Chuang Gan
Humans can leverage prior experience and learn novel tasks from a handful of demonstrations.
1 code implementation • 29 May 2022 • Zuxin Liu, Zijian Guo, Zhepeng Cen, huan zhang, Jie Tan, Bo Li, Ding Zhao
One interesting and counter-intuitive finding is that the maximum reward attack is strong, as it can both induce unsafe behaviors and make the attack stealthy by maintaining the reward.
no code implementations • 13 Apr 2022 • Shaojin Ding, Weiran Wang, Ding Zhao, Tara N. Sainath, Yanzhang He, Robert David, Rami Botros, Xin Wang, Rina Panigrahy, Qiao Liang, Dongseong Hwang, Ian McGraw, Rohit Prabhavalkar, Trevor Strohman
In this paper, we propose a dynamic cascaded encoder Automatic Speech Recognition (ASR) model, which unifies models for different deployment scenarios.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 7 Apr 2022 • JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin
Multimedia summarization with multimodal output can play an essential role in real-world applications, i. e., automatically generating cover images and titles for news articles or providing introductions to online videos.
no code implementations • 4 Apr 2022 • Mansur Arief, Zhepeng Cen, Zhenyuan Liu, Zhiyuang Huang, Henry Lam, Bo Li, Ding Zhao
In this work, we present Deep Importance Sampling (Deep IS) framework that utilizes a deep neural network to obtain an efficient IS that is on par with the state-of-the-art, capable of reducing the required sample size 43 times smaller than the naive sampling method to achieve 10% relative error and while producing an estimate that is much less conservative.
1 code implementation • 19 Mar 2022 • Jiacheng Zhu, Gregory Darnell, Agni Kumar, Ding Zhao, Bo Li, XuanLong Nguyen, Shirley You Ren
The proposed method learns an individual-specific predictive model from heterogeneous observations, and enables estimation of an optimal transport map that yields a push forward operation onto the demographic features for each task.
1 code implementation • ICLR 2022 • Fan Wu, Linyi Li, Chejian Xu, huan zhang, Bhavya Kailkhura, Krishnaram Kenthapadi, Ding Zhao, Bo Li
We leverage COPA to certify three RL environments trained with different algorithms and conclude: (1) The proposed robust aggregation protocols such as temporal aggregation can significantly improve the certifications; (2) Our certification for both per-state action stability and cumulative reward bound are efficient and tight; (3) The certification for different training algorithms and environments are different, implying their intrinsic robustness properties.
no code implementations • 19 Feb 2022 • Peide Huang, Mengdi Xu, Fei Fang, Ding Zhao
In this paper, we introduce a novel hierarchical formulation of robust RL - a general-sum Stackelberg game model called RRL-Stack - to formalize the sequential nature and provide extra flexibility for robust training.
no code implementations • 1 Feb 2022 • Zhao Zhang, Ding Zhao, Xianfeng Terry Yang
Full-field traffic state information (i. e., flow, speed, and density) is critical for the successful operation of Intelligent Transportation Systems (ITS) on freeways.
2 code implementations • 28 Jan 2022 • Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao
Safe reinforcement learning (RL) aims to learn policies that satisfy certain constraints before deploying them to safety-critical applications.
no code implementations • 25 Jan 2022 • JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Peide Huang, Michael Rosenberg, Douglas Weber, Emerson Liu, Ding Zhao
In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection.
no code implementations • 26 Nov 2021 • Xianxin Song, Ding Zhao, Haocheng Hua, Tony Xiao Han, Xun Yang, Jie Xu
This paper studies an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system, in which one IRS is deployed to not only assist the wireless communication from a multi-antenna base station (BS) to a single-antenna communication user (CU), but also create virtual line-of-sight (LoS) links for sensing targets at areas with LoS links blocked.
1 code implementation • 3 Nov 2021 • Mansur Arief, Yuanlu Bai, Wenhao Ding, Shengyi He, Zhiyuan Huang, Henry Lam, Ding Zhao
Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events.
no code implementations • 26 Oct 2021 • Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an effective way to evaluate the robustness of autonomous driving systems.
1 code implementation • 19 Jun 2021 • Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations.
2 code implementations • ICLR 2022 • Fan Wu, Linyi Li, Zijian Huang, Yevgeniy Vorobeychik, Ding Zhao, Bo Li
We then develop a local smoothing algorithm for policies derived from Q-functions to guarantee the robustness of actions taken along the trajectory; we also develop a global smoothing algorithm for certifying the lower bound of a finite-horizon cumulative reward, as well as a novel local smoothing algorithm to perform adaptive search in order to obtain tighter reward certification.
no code implementations • 8 Jun 2021 • Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
Generating adversarial scenarios, which have the potential to fail autonomous driving systems, provides an effective way to improve robustness.
1 code implementation • CVPR 2022 • Hanjiang Hu, Zuxin Liu, Sharad Chitlangia, Akhil Agnihotri, Ding Zhao
To this end, we introduce an easy-to-compute information-theoretic surrogate metric to quantitatively and fast evaluate LiDAR placement for 3D detection of different types of objects.
no code implementations • 28 Apr 2021 • Rajeev Rikhye, Quan Wang, Qiao Liang, Yanzhang He, Ding Zhao, Yiteng, Huang, Arun Narayanan, Ian McGraw
In this paper, we introduce a streaming keyphrase detection system that can be easily customized to accurately detect any phrase composed of words from a large vocabulary.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
1 code implementation • 7 Feb 2021 • Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, XuanLong Nguyen, Ding Zhao
We introduce a formulation of optimal transport problem for distributions on function spaces, where the stochastic map between functional domains can be partially represented in terms of an (infinite-dimensional) Hilbert-Schmidt operator mapping a Hilbert space of functions to another.
no code implementations • 2 Jan 2021 • Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding, Ding Zhao
The algorithm is evaluated in realistic safety-critical environments with non-stationary disturbances.
1 code implementation • 9 Nov 2020 • Hanjiang Hu, Baoquan Yang, Zhijian Qiao, Shiqi Liu, Jiacheng Zhu, Zuxin Liu, Wenhao Ding, Ding Zhao, Hesheng Wang
Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving, and the generalization of learning-based algorithms on different environments is still an open problem.
1 code implementation • 15 Oct 2020 • Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, Ding Zhao
We propose a model-based approach to enable RL agents to effectively explore the environment with unknown system dynamics and environment constraints given a significantly small number of violation budgets.
Model-based Reinforcement Learning Model Predictive Control +3
no code implementations • 10 Oct 2020 • Yuanlu Bai, Zhiyuan Huang, Henry Lam, Ding Zhao
We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests.
no code implementations • 16 Sep 2020 • Wenhao Ding, Baiming Chen, Bo Li, Kim Ji Eun, Ding Zhao
Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance.
no code implementations • 30 Jul 2020 • Zuxin Liu, Baiming Chen, Hongyi Zhou, Guru Koushik, Martial Hebert, Ding Zhao
Multi-agent navigation in dynamic environments is of great industrial value when deploying a large scale fleet of robot to real-world applications.
2 code implementations • 28 Jun 2020 • Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, Ding Zhao
Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications.
1 code implementation • NeurIPS 2020 • Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao
We propose a transition prior to account for the temporal dependencies in streaming data and update the mixture online via sequential variational inference.
no code implementations • 18 Jun 2020 • Aritra Guha, Rayleigh Lei, Jiacheng Zhu, XuanLong Nguyen, Ding Zhao
These distance metrics can serve as an objective for assessing the stability of an interaction learning algorithm.
no code implementations • 16 May 2020 • Zhaofeng Wu, Ding Zhao, Qiao Liang, Jiahui Yu, Anmol Gulati, Ruoming Pang
In automatic speech recognition (ASR), model pruning is a widely adopted technique that reduces model size and latency to deploy neural network models on edge devices with resource constraints.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 11 May 2020 • Baiming Chen, Mengdi Xu, Liang Li, Ding Zhao
Action delays degrade the performance of reinforcement learning in many real-world systems.
1 code implementation • 11 May 2020 • Baiming Chen, Mengdi Xu, Zuxin Liu, Liang Li, Ding Zhao
We also test the proposed algorithm in traffic scenarios that require coordination of all autonomous vehicles to show the practical value of delay-awareness.
no code implementations • 28 Mar 2020 • Tara N. Sainath, Yanzhang He, Bo Li, Arun Narayanan, Ruoming Pang, Antoine Bruguier, Shuo-Yiin Chang, Wei Li, Raziel Alvarez, Zhifeng Chen, Chung-Cheng Chiu, David Garcia, Alex Gruenstein, Ke Hu, Minho Jin, Anjuli Kannan, Qiao Liang, Ian McGraw, Cal Peyser, Rohit Prabhavalkar, Golan Pundak, David Rybach, Yuan Shangguan, Yash Sheth, Trevor Strohman, Mirko Visontai, Yonghui Wu, Yu Zhang, Ding Zhao
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.
no code implementations • 2 Mar 2020 • Wenhao Ding, Baiming Chen, Minjun Xu, Ding Zhao
We then train the generative model as an agent (or a generator) to investigate the risky distribution parameters for a given driving algorithm being evaluated.
no code implementations • 17 Oct 2019 • Shenghao Qin, Jiacheng Zhu, Jimmy Qin, Wenshuo Wang, Ding Zhao
Neural processes (NPs) learn stochastic processes and predict the distribution of target output adaptively conditioned on a context set of observed input-output pairs.
no code implementations • 17 Oct 2019 • Jiacheng Zhu, Shenghao Qin, Wenshuo Wang, Ding Zhao
Constructed by incorporating NPs with recurrent neural networks (RNNs), the ARNP model predicts the distribution of a target vehicle trajectory conditioned on the observed long-term sequential data of all surrounding vehicles.
no code implementations • 8 Oct 2019 • Weiyang Zhang, Wenshuo Wang, Ding Zhao
The experimental results demonstrate that our proposed method can generate a bunch of human-like multi-vehicle interaction trajectories that can fit different road conditions remaining the key interaction patterns of agents in the provided scenarios, which is import to the development of autonomous vehicles.
1 code implementation • 17 Sep 2019 • Wenhao Ding, Mengdi Xu, Ding Zhao
However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms.
no code implementations • 14 Sep 2019 • Rui Chen, Wenshuo Wang, Zirui Zhao, Ding Zhao
One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution.
1 code implementation • 17 Jul 2019 • Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang, Ding Zhao
Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions.
2 code implementations • 25 Jun 2019 • Yaohui Guo, Vinay Varma Kalidindi, Mansur Arief, Wenshuo Wang, Jiacheng Zhu, Huei Peng, Ding Zhao
We then use this model to reproduce the high-dimensional driving scenarios in a finitely tractable form.
no code implementations • 19 Apr 2019 • Zhiyuan Huang, Mansur Arief, Henry Lam, Ding Zhao
These Monte Carlo samples are generated from stochastic input models constructed based on real-world data.
2 code implementations • 15 Nov 2018 • Yanzhang He, Tara N. Sainath, Rohit Prabhavalkar, Ian McGraw, Raziel Alvarez, Ding Zhao, David Rybach, Anjuli Kannan, Yonghui Wu, Ruoming Pang, Qiao Liang, Deepti Bhatia, Yuan Shangguan, Bo Li, Golan Pundak, Khe Chai Sim, Tom Bagby, Shuo-Yiin Chang, Kanishka Rao, Alexander Gruenstein
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition.
no code implementations • 15 Sep 2018 • Wenhao Ding, Wenshuo Wang, Ding Zhao
Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing.
no code implementations • 7 Aug 2018 • Golan Pundak, Tara N. Sainath, Rohit Prabhavalkar, Anjuli Kannan, Ding Zhao
Our approach, which we re- fer to as Contextual Listen, Attend and Spell (CLAS) jointly- optimizes the ASR components along with embeddings of the context n-grams.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 27 Jul 2018 • Wenshuo Wang, Weiyang Zhang, Ding Zhao
Semantically understanding complex drivers' encountering behavior, wherein two or multiple vehicles are spatially close to each other, does potentially benefit autonomous car's decision-making design.
no code implementations • 13 May 2018 • Jiacheng Zhu, Wenshuo Wang, Ding Zhao
A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV).
no code implementations • 5 May 2018 • Mansur Arief, Peter Glynn, Ding Zhao
Various automobile and mobility companies, for instance Ford, Uber and Waymo, are currently testing their pre-produced autonomous vehicle (AV) fleets on the public roads.
no code implementations • 28 Feb 2018 • Sisi Li, Wenshuo Wang, Zhaobin Mo, Ding Zhao
Learning knowledge from driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with nearby vehicles engaged.
no code implementations • 27 Feb 2018 • Zhaobin Mo, Sisi Li, Diange Yang, Ding Zhao
To overcome this problem, we extract naturalistic V2V encounters data from the database, and then separate the primary vehicle encounter category by clustering.
no code implementations • 11 Jan 2018 • Wenshuo Wang, Junqiang Xi, Ding Zhao
A learning-based inference method, using onboard data from CAN-Bus, radar and cameras as explanatory variables, is introduced to infer drivers' braking decisions by combining a Gaussian mixture model (GMM) with a hidden Markov model (HMM).
no code implementations • 1 Oct 2017 • Zhiyuan Huang, Yaohui Guo, Henry Lam, Ding Zhao
The distribution used in sampling is pivotal to the performance of the method, but building a suitable distribution requires case-by-case analysis.
no code implementations • 11 Sep 2017 • Wenshuo Wang, Ding Zhao
Developing an automated vehicle, that can handle complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and understand driving environment, oftentimes, based on analyzing massive amounts of naturalistic driving data.
no code implementations • 16 Aug 2017 • Wenshuo Wang, Junqiang Xi, Ding Zhao
In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number of these patterns.
no code implementations • 23 Jun 2017 • Wenshuo Wang, Chang Liu, Ding Zhao
For projects that cost millions of dollars, it is critical to determine the right amount of data needed.
no code implementations • 1 May 2017 • Macheng Shen, Jing Sun, Ding Zhao
It has been shown, in our previous work, that the GNSS error can be reduced from several meters to sub-meter level by matching the biased GNSS positioning to a digital map with road constraints.
Systems and Control
no code implementations • 26 Mar 2017 • Macheng Shen, Jing Sun, Ding Zhao
Cooperative map matching (CMM) uses the Global Navigation Satellite System (GNSS) positioning of a group of vehicles to improve the standalone localization accuracy.
Systems and Control
no code implementations • 19 Feb 2017 • Macheng Shen, Ding Zhao, Jing Sun, Huei Peng
A Rao-Blackwellized particle filter (RBPF) is used to jointly estimate the common biases of the pseudo-ranges and the vehicle positions.
Systems and Control
no code implementations • 4 Feb 2017 • Wenshuo Wang, Ding Zhao, Junqiang Xi, Wei Han
Second, based on this model, we develop an online model-based prediction algorithm to predict the forthcoming vehicle trajectory and judge whether the driver will demonstrate an LDB or a DCB.