1 code implementation • 28 May 2025 • Zhenglun Kong, Zheng Zhan, Shiyue Hou, Yifan Gong, Xin Meng, Pengwei Sui, Peiyan Dong, Xuan Shen, Zifeng Wang, Pu Zhao, Hao Tang, Stratis Ioannidis, Yanzhi Wang
To address these issues, we propose a framework that adaptively selects and aggregates knowledge from diverse LLMs to build a single, stronger model, avoiding the high memory overhead of ensemble and inflexible weight merging.
no code implementations • 22 May 2025 • Zifeng Wang, Benjamin Danek, Jimeng Sun
We propose BioDSA-1K as a foundation for building and evaluating generalizable, trustworthy AI agents for biomedical discovery.
no code implementations • 22 May 2025 • Zifeng Wang, Qiao Jin, Jiacheng Lin, Junyi Gao, Jathurshan Pradeepkumar, Pengcheng Jiang, Benjamin Danek, Zhiyong Lu, Jimeng Sun
This structured and ontology-grounded design enables TrialPanorama to serve as a unified, extensible resource for a wide range of clinical trial tasks, including trial planning, design, and summarization.
1 code implementation • 20 May 2025 • Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin, Jinfeng Xiao, Zifeng Wang, Jimeng Sun, Jiawei Han
Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference.
no code implementations • 11 Mar 2025 • Zifeng Wang, Xiaoning Jin
In this paper, we propose a Spatial-Terminal Iterative Learning Control (STILC) method integrated with PID control to iteratively learn and reduce registration error cycle-by-cycle, converging it to zero.
no code implementations • 11 Mar 2025 • Zhen Tan, Jun Yan, I-Hung Hsu, Rujun Han, Zifeng Wang, Long T. Le, Yiwen Song, Yanfei Chen, Hamid Palangi, George Lee, Anand Iyer, Tianlong Chen, Huan Liu, Chen-Yu Lee, Tomas Pfister
Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information from long-term interactions limits their effectiveness in applications requiring sustained personalization.
no code implementations • 10 Mar 2025 • Fan Yin, Zifeng Wang, I-Hung Hsu, Jun Yan, Ke Jiang, Yanfei Chen, Jindong Gu, Long T. Le, Kai-Wei Chang, Chen-Yu Lee, Hamid Palangi, Tomas Pfister
To address this, we propose Magnet, a principled framework for synthesizing high-quality training trajectories to enhance the function calling capability of large language model agents in multi-turn conversations with humans.
1 code implementation • 28 Feb 2025 • Pengcheng Jiang, Jiacheng Lin, Lang Cao, Runchu Tian, SeongKu Kang, Zifeng Wang, Jimeng Sun, Jiawei Han
We introduce DeepRetrieval, a reinforcement learning (RL) approach that trains LLMs for query generation through trial and error without supervised data (reference query).
no code implementations • 22 Feb 2025 • Mihir Parmar, Xin Liu, Palash Goyal, Yanfei Chen, Long Le, Swaroop Mishra, Hossein Mobahi, Jindong Gu, Zifeng Wang, Hootan Nakhost, Chitta Baral, Chen-Yu Lee, Tomas Pfister, Hamid Palangi
Recent agent frameworks and inference-time algorithms often struggle with complex planning problems due to limitations in verifying generated plans or reasoning and varying complexity of instances within a single task.
no code implementations • 12 Feb 2025 • Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Jeevesh Juneja, Zifeng Wang, Chen-Yu Lee, Pradeep Shenoy, Rina Panigrahy, Aditya Krishna Menon, Sanjiv Kumar
Large language models' significant advances in capabilities are accompanied by significant increases in inference costs.
no code implementations • 6 Feb 2025 • Shangbin Feng, Wenxuan Ding, Alisa Liu, Zifeng Wang, Weijia Shi, Yike Wang, Zejiang Shen, Xiaochuang Han, Hunter Lang, Chen-Yu Lee, Tomas Pfister, Yejin Choi, Yulia Tsvetkov
This position paper argues that in many realistic (i. e., complex, contextualized, subjective) scenarios, one LLM is not enough to produce a reliable output.
no code implementations • 6 Feb 2025 • Shangbin Feng, Zifeng Wang, Palash Goyal, Yike Wang, Weijia Shi, Huang Xia, Hamid Palangi, Luke Zettlemoyer, Yulia Tsvetkov, Chen-Yu Lee, Tomas Pfister
We propose Heterogeneous Swarms, an algorithm to design multi-LLM systems by jointly optimizing model roles and weights.
1 code implementation • 27 Jan 2025 • Zifeng Wang, Lang Cao, Qiao Jin, Joey Chan, Nicholas Wan, Behdad Afzali, Hyun-Jin Cho, Chang-In Choi, Mehdi Emamverdi, Manjot K. Gill, Sun-Hyung Kim, Yijia Li, Yi Liu, Hanley Ong, Justin Rousseau, Irfan Sheikh, Jenny J. Wei, Ziyang Xu, Christopher M. Zallek, Kyungsang Kim, Yifan Peng, Zhiyong Lu, Jimeng Sun
Here, we present LEADS, an AI foundation model for study search, screening, and data extraction from medical literature.
no code implementations • 29 Nov 2024 • Justin Chih-Yao Chen, Zifeng Wang, Hamid Palangi, Rujun Han, Sayna Ebrahimi, Long Le, Vincent Perot, Swaroop Mishra, Mohit Bansal, Chen-Yu Lee, Tomas Pfister
Reverse thinking plays a crucial role in human reasoning.
no code implementations • 11 Nov 2024 • Trisha Das, Zifeng Wang, Afrah Shafquat, Mandis Beigi, Jason Mezey, Jimeng Sun
In this paper, we propose SynRL which leverages reinforcement learning to improve the performance of patient data generators by customizing the generated data to meet the user-specified requirements for synthetic data outcomes and endpoints.
no code implementations • 28 Oct 2024 • Zifeng Wang, Benjamin Danek, Ziwei Yang, Zheng Chen, Jimeng Sun
To address this gap, we developed a benchmark of data science coding tasks derived from the analyses of 39 published studies.
no code implementations • 28 Oct 2024 • Zifeng Wang, Hanyin Wang, Benjamin Danek, Ying Li, Christina Mack, Hoifung Poon, Yajuan Wang, Pranav Rajpurkar, Jimeng Sun
The integration of Large Language Models (LLMs) into medical applications has sparked widespread interest across the healthcare industry, from drug discovery and development to clinical decision support, assisting telemedicine, medical devices, and healthcare insurance applications.
1 code implementation • 24 Oct 2024 • Qiao Jin, Nicholas Wan, Robert Leaman, Shubo Tian, Zhizheng Wang, Yifan Yang, Zifeng Wang, Guangzhi Xiong, Po-Ting Lai, Qingqing Zhu, Benjamin Hou, Maame Sarfo-Gyamfi, Gongbo Zhang, Aidan Gilson, Balu Bhasuran, Zhe He, Aidong Zhang, Jimeng Sun, Chunhua Weng, Ronald M. Summers, Qingyu Chen, Yifan Peng, Zhiyong Lu
We then review the strategies, such as prompt engineering and fine-tuning, to adapt standard LLMs to specialized medical tasks.
no code implementations • 16 Oct 2024 • Lichang Chen, Hexiang Hu, Mingda Zhang, YiWen Chen, Zifeng Wang, Yandong Li, Pranav Shyam, Tianyi Zhou, Heng Huang, Ming-Hsuan Yang, Boqing Gong
To address this, OmnixR offers two evaluation variants: (1)synthetic subset: a synthetic dataset generated automatically by translating text into multiple modalities--audio, images, video, and hybrids (Omnify).
no code implementations • 15 Oct 2024 • Shangbin Feng, Zifeng Wang, Yike Wang, Sayna Ebrahimi, Hamid Palangi, Lesly Miculicich, Achin Kulshrestha, Nathalie Rauschmayr, Yejin Choi, Yulia Tsvetkov, Chen-Yu Lee, Tomas Pfister
Extensive experiments demonstrate that Model Swarms could flexibly adapt LLM experts to a single task, multi-task domains, reward models, as well as diverse human interests, improving over 12 model composition baselines by up to 21. 0% across tasks and contexts.
no code implementations • 15 Oct 2024 • Wenda Xu, Rujun Han, Zifeng Wang, Long T. Le, Dhruv Madeka, Lei LI, William Yang Wang, Rishabh Agarwal, Chen-Yu Lee, Tomas Pfister
To address these limitations, we introduce Speculative Knowledge Distillation (SKD), a novel approach that leverages cooperation between student and teacher models to generate high-quality training data on-the-fly while aligning with the student's inference-time distribution.
1 code implementation • 7 Oct 2024 • Si-An Chen, Lesly Miculicich, Julian Martin Eisenschlos, Zifeng Wang, Zilong Wang, Yanfei Chen, Yasuhisa Fujii, Hsuan-Tien Lin, Chen-Yu Lee, Tomas Pfister
Recent advancements in language models (LMs) have notably enhanced their ability to reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables.
no code implementations • 11 Jul 2024 • Zilong Wang, Zifeng Wang, Long Le, Huaixiu Steven Zheng, Swaroop Mishra, Vincent Perot, Yuwei Zhang, Anush Mattapalli, Ankur Taly, Jingbo Shang, Chen-Yu Lee, Tomas Pfister
Retrieval augmented generation (RAG) combines the generative abilities of large language models (LLMs) with external knowledge sources to provide more accurate and up-to-date responses.
no code implementations • 25 Jun 2024 • Zifeng Wang, Lang Cao, Benjamin Danek, Qiao Jin, Zhiyong Lu, Jimeng Sun
Here, we introduce TrialMind, a generative artificial intelligence (AI) pipeline for facilitating human-AI collaboration in three crucial tasks for evidence synthesis: study search, screening, and data extraction.
1 code implementation • 25 Jun 2024 • Jiacheng Lin, Hanwen Xu, Zifeng Wang, Sheng Wang, Jimeng Sun
To address this challenge, we propose a clinical trial foundation model named Panacea, designed to handle multiple tasks, including trial search, trial summarization, trial design, and patient-trial matching.
no code implementations • 23 Jun 2024 • Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long T. Le, Abhishek Kumar, James Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister
Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input.
no code implementations • 8 Jun 2024 • I-Hung Hsu, Zifeng Wang, Long T. Le, Lesly Miculicich, Nanyun Peng, Chen-Yu Lee, Tomas Pfister
Grounded generation aims to equip language models (LMs) with the ability to produce more credible and accountable responses by accurately citing verifiable sources.
2 code implementations • 25 Apr 2024 • Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang, Zifeng Wang, Sayna Ebrahimi, Hao Wang
In this survey, we provide a comprehensive overview of the current research progress on LLMs within the context of CL.
no code implementations • 8 Apr 2024 • Zifeng Wang, Chun-Liang Li, Vincent Perot, Long T. Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
To this end, we introduce CodecLM, a general framework for adaptively generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs.
1 code implementation • 19 Mar 2024 • Masih Eskandar, Tooba Imtiaz, Zifeng Wang, Jennifer Dy
The performance of deep models, including Vision Transformers, is known to be vulnerable to adversarial attacks.
no code implementations • 15 Mar 2024 • Pengcheng Jiang, Cao Xiao, Zifeng Wang, Parminder Bhatia, Jimeng Sun, Jiawei Han
To overcome this, we introduce TriSum, a framework for distilling LLMs' text summarization abilities into a compact, local model.
1 code implementation • 16 Feb 2024 • Pengcheng Jiang, Jiacheng Lin, Zifeng Wang, Jimeng Sun, Jiawei Han
The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs).
no code implementations • 28 Jan 2024 • Lang Cao, Zifeng Wang, Cao Xiao, Jimeng Sun
We demonstrate the importance of accurately identifying precedent cases and mitigating the temporal shift when making predictions for case law, as our method shows a significant improvement over the prior methods that focus on civil law case outcome predictions.
2 code implementations • 9 Jan 2024 • Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, Tomas Pfister
We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts.
Ranked #4 on
Table-based Fact Verification
on TabFact
1 code implementation • 5 Oct 2023 • Zifeng Wang, Zichen Wang, Balasubramaniam Srinivasan, Vassilis N. Ioannidis, Huzefa Rangwala, Rishita Anubhai
Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.
no code implementations • 5 Oct 2023 • Ruiyu Wang, Zifeng Wang, Jimeng Sun
Specifically, we train a single LLM on an aggregation of 169 tabular datasets with diverse targets and compare its performance against baselines that are trained on each dataset separately.
no code implementations • 4 Oct 2023 • Tao Feng, Zifeng Wang, Jimeng Sun
Specifically, we employ a teacher LLM to create a curriculum for instruction tuning of the student LLM, namely Curriculum Instruction TunING (CITING).
no code implementations • 19 Sep 2023 • Vincent Perot, Kai Kang, Florian Luisier, Guolong Su, Xiaoyu Sun, Ramya Sree Boppana, Zilong Wang, Zifeng Wang, Jiaqi Mu, Hao Zhang, Chen-Yu Lee, Nan Hua
The main obstacles to adopting LLMs for this task include the absence of layout encoding within LLMs, which is critical for high quality extraction, and the lack of a grounding mechanism to localize the predicted entities within the document.
1 code implementation • 17 Aug 2023 • Yilin Wen, Zifeng Wang, Jimeng Sun
Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks.
1 code implementation • 27 Jul 2023 • Qiao Jin, Zifeng Wang, Charalampos S. Floudas, Fangyuan Chen, Changlin Gong, Dara Bracken-Clarke, Elisabetta Xue, Yifan Yang, Jimeng Sun, Zhiyong Lu
The TrialGPT-Ranking scores are highly correlated with human judgments and outperform the best-competing models by 43. 8% in ranking and excluding trials.
1 code implementation • 6 Jun 2023 • Zifeng Wang, Brandon Theodorou, Tianfan Fu, Cao Xiao, Jimeng Sun
The code is available at https://github. com/RyanWangZf/PyTrial.
no code implementations • 26 May 2023 • Ruoxi Sun, Sercan Ö. Arik, Alex Muzio, Lesly Miculicich, Satya Gundabathula, Pengcheng Yin, Hanjun Dai, Hootan Nakhost, Rajarishi Sinha, Zifeng Wang, Tomas Pfister
Text-to-SQL, the process of translating natural language into Structured Query Language (SQL), represents a transformative application of large language models (LLMs), potentially revolutionizing how humans interact with data.
1 code implementation • 20 May 2023 • Zifeng Wang, Chufan Gao, Cao Xiao, Jimeng Sun
Tabular data prediction has been employed in medical applications such as patient health risk prediction.
no code implementations • 19 May 2023 • Zifeng Wang, Cao Xiao, Jimeng Sun
Clinical trials are critical for drug development.
no code implementations • 30 Apr 2023 • Zifeng Wang, Zheng Zhan, Yifan Gong, Yucai Shao, Stratis Ioannidis, Yanzhi Wang, Jennifer Dy
Rehearsal-based approaches are a mainstay of continual learning (CL).
no code implementations • 7 Apr 2023 • Zifeng Wang, Cao Xiao, Jimeng Sun
Accurate trial outcome prediction based on historical trial data promises better trial investment decisions and more trial success.
no code implementations • 30 Jan 2023 • Penghao Jiang, Xin Ke, Zifeng Wang, Chunxi Li
However, learning such a model is not possible in standard machine learning frameworks as the distribution of the test data is unknown.
no code implementations • 26 Jan 2023 • Penghao Jiang, Ke Xin, Zifeng Wang, Chunxi Li
Modern deep learning techniques have illustrated their excellent capabilities in many areas, but relies on large training data.
no code implementations • 14 Dec 2022 • Tooba Imtiaz, Morgan Kohler, Jared Miller, Zifeng Wang, Masih Eskander, Mario Sznaier, Octavia Camps, Jennifer Dy
Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal.
no code implementations • 14 Nov 2022 • Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee, Guolong Su, Hao Zhang, Jennifer Dy, Vincent Perot, Tomas Pfister
Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities.
1 code implementation • 18 Oct 2022 • Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, Jimeng Sun
Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction.
1 code implementation • 11 Oct 2022 • Zifeng Wang, Jimeng Sun
Accessing longitudinal multimodal Electronic Healthcare Records (EHRs) is challenging due to privacy concerns, which hinders the use of ML for healthcare applications.
1 code implementation • 9 Oct 2022 • Tong Jian, Zifeng Wang, Yanzhi Wang, Jennifer Dy, Stratis Ioannidis
Adversarial pruning compresses models while preserving robustness.
1 code implementation • 20 Sep 2022 • Zifeng Wang, Zheng Zhan, Yifan Gong, Geng Yuan, Wei Niu, Tong Jian, Bin Ren, Stratis Ioannidis, Yanzhi Wang, Jennifer Dy
SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity.
no code implementations • 16 Sep 2022 • Zifeng Wang, Chufan Gao, Lucas M. Glass, Jimeng Sun
In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials.
1 code implementation • 29 Jun 2022 • Zifeng Wang, Jimeng Sun
We propose a zero-shot clinical trial retrieval method, Trial2Vec, which learns through self-supervision without annotating similar clinical trials.
1 code implementation • 19 May 2022 • Zifeng Wang, Jimeng Sun
Can we leverage model pretraining on multiple distinct tables?
3 code implementations • 10 Apr 2022 • Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister
Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting.
1 code implementation • 12 Jan 2022 • Batool Salehi, Guillem Reus-Muns, Debashri Roy, Zifeng Wang, Tong Jian, Jennifer Dy, Stratis Ioannidis, Kaushik Chowdhury
Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed within short contact times.
5 code implementations • CVPR 2022 • Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister
The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge.
1 code implementation • 2 Oct 2021 • Zifeng Wang, Jimeng Sun
In medicine, survival analysis studies the time duration to events of interest such as mortality.
1 code implementation • ICLR 2022 • Zifeng Wang, Shao-Lun Huang, Ercan E. Kuruoglu, Jimeng Sun, Xi Chen, Yefeng Zheng
Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB.
1 code implementation • 13 Jun 2021 • Tingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer Dy
We develop a fully Bayesian inference framework for ULL with a novel end-to-end Deep Bayesian Unsupervised Lifelong Learning (DBULL) algorithm, which can progressively discover new clusters without forgetting the past with unlabelled data while learning latent representations.
1 code implementation • NeurIPS 2021 • Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer Dy
We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a regularizer for learning an adversarially robust deep neural network classifier.
1 code implementation • 27 Feb 2021 • Zifeng Wang, Yifan Yang, Rui Wen, Xi Chen, Shao-Lun Huang, Yefeng Zheng
Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i. e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks.
1 code implementation • 13 Dec 2020 • Zifeng Wang, Batool Salehi, Andrey Gritsenko, Kaushik Chowdhury, Stratis Ioannidis, Jennifer Dy
We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples.
1 code implementation • 13 Dec 2020 • Zifeng Wang, Tong Jian, Kaushik Chowdhury, Yanzhi Wang, Jennifer Dy, Stratis Ioannidis
In lifelong learning, we wish to maintain and update a model (e. g., a neural network classifier) in the presence of new classification tasks that arrive sequentially.
no code implementations • NeurIPS 2020 • Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi, Jennifer Dy
Moreover, the features that belong to each group, and the important feature groups may vary per sample.
no code implementations • COLING 2022 • Zifeng Wang, Rui Wen, Xi Chen, Shao-Lun Huang, Ningyu Zhang, Yefeng Zheng
Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise.
no code implementations • 6 Sep 2020 • Zifeng Wang, Rui Wen, Xi Chen, Shilei Cao, Shao-Lun Huang, Buyue Qian, Yefeng Zheng
We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs).
1 code implementation • NeurIPS 2020 • Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E. Kuruoglu, Yefeng Zheng
Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems.
no code implementations • 25 Jan 2020 • Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang
Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), which often causes large bias to the estimate of true outcomes risk.
1 code implementation • 3 Dec 2019 • Zifeng Wang, Hong Zhu, Zhenhua Dong, Xiuqiang He, Shao-Lun Huang
In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.
no code implementations • 7 Jun 2019 • Tingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer G. Dy
We develop a data driven approach to perform clustering and end-to-end feature learning simultaneously for streaming data that can adaptively detect novel clusters in emerging data.
no code implementations • ECCV 2018 • Liangliang Ren, Jiwen Lu, Zifeng Wang, Qi Tian, Jie zhou
To address this, we develop a deep prediction-decision network in our C-DRL, which simultaneously detects and predicts objects under a unified network via deep reinforcement learning.