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陈凯

北京中关村学院导师

北京中关村学院导师、中关村人工智能研究院研究员、具身智能方向负责人,深度机智创始人。中科大少年班学院自动化专业学士,中科大-微软亚洲研究院联合培养博士,曾任微软亚洲研究院首席研究员、北京智源人工智能研究院研究员。在人工智能领域深耕十五年,主要研究方向涵盖生成式AI、多模态、分布式训练、大模型、科学智能等,在国际上率先将人工智能模型分布式训练规模扩展至百卡以上,多项核心技术成果转化进入Azure、Office、Windows等重要产品,服务全球用户,相关成果发表于Nature子刊、TASLP、NeurIPS、ICLR等顶级会议和期刊。致力于将人工智能带入物理世界,以人类第一视角数据切入,提升物理智能水平,解决具身智能通用性难题。

教育经历

2007.8 – 2011.6     中国科学技术大学 少年班学院

2011.9 – 2016.6     中国科学技术大学-微软亚洲研究院 联合培养博士

 

工作经历

2023.8-2024.12     北京智源人工智能研究院

2023.3-2023.8        独立开发者

2016.6-2023.3        微软亚洲研究院

 

代表性学术论文

[1] Yunfei Teng, Yuxuan Ren, Kai Chen*, Xi Chen, Zhaoming Chen, Qiwei Ye, "CryoGEN: Cryogenic Electron Tomography Reconstruction via Generative Energy-base Models", ICLR-2025

[2] Jingxuan Feng, Lili Wang, Xiaoya Zhai, Kai Chen, Wenming Wu, Ligang Liu, Xiao-Ming Fu, "Constructing boundary-identical microstructures via guided diffusion for fast multiscale topology optimization", Computer Methods in Applied Mechanics and Engineering 436, 117735

[3] Yukang Yang, Dongnan Gui, Yuhui Yuan, Haisong Ding, Han Hu, Kai Chen, "GlyphControl: Glyph Conditional Control for Visual Text Generation", NeurIPS-2024

[4] Dongnan Gui, Kai Chen*, Haisong Ding, Qiang Huo,"Zero-shot Generation of Training Data with Denoising Diffusion Probabilistic Model for Handwritten Chinese Character Recognition", ICDAR-2023

[5] Haisong Ding, Bozhi Luan, Dongnan Gui, Kai Chen*, Qiang Huo, "Improving Handwritten OCR with Training Samples Generated by Glyph Conditional Denoising Diffusion Probabilistic Model", ICDAR-2023

[6] Haisong Ding, Kai Chen*, Qiang Huo, "An Encoder-Decoder Approach to Handwritten Mathematical Expression Recognition with Multi-head Attention and Stacked Decoder", in Proc. ICDAR-2021,pp.602-616

[7]    Mingyang Guan, Haisong Ding, Kai Chen*, Qiang Huo, “Improving Handwritten OCR with Augmented Text Line Images Synthesized from Online Handwriting Samples by Style Conditioned GAN”, in Proc. ICFHR-2020, pp.151-156 (Best Student Paper).

[8]    Haisong Ding, Kai Chen*, Qiang Huo, “Improving Knowledge Distillation of CTC-Trained Acoustic Models with Alignment-Consistent Ensemble and Target Delay”, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 2561-2571, 2020.

[9]    Kai Chen, Haisong Ding, Qiang Huo, “Parallelizing Adam with Blockwise Model-Update Filtering”, in Proc. ICASSP-2020, pp.3027-3031

[10]    Haisong Ding, Kai Chen*, Qiang Huo, “Compression of CTC-Trained Acoustic Models by Dynamic Frame-Wise Distillation or Segment-Wise N-Best Hypotheses Imitation”, in Proc. INTERSPEECH-2019, pp.3218-3222.

[11]    Haisong Ding, Kai Chen*, Qiang Huo, “Compressing CNN-DBLSTM models for OCR with teacher-student learning and Tucker decomposition”, in Pattern Recognition, 2019, vol.96, 106957.

[12]    Haisong Ding, Kai Chen*, Wenping Hu, Meng Cai, Qiang Huo, “Building Compact CNNDBLSTM Based Character Models for Handwriting Recognition and OCR by Teacher Student Learning”, in Proc. ICFHR-2018, pp.139-144.

[13]    Kai Chen, Li Tian, Haisong Ding, Meng Cai, Lei Sun, Sen Liang, Qiang Huo, “A Compact CNNDBLSTM Based Character Model for Online Handwritten Chinese Text Recognition”, in Proc.ICDAR-2017, pp.1068-1073.

[14]    Haisong Ding, Kai Chen*, Ye Yuan, Meng Cai, Lei Sun, Sen Liang, Qiang Huo, “A Compact CNN-DBLSTM based character model for Offline Handwriting Recognition with Tucker Decomposition”,in Proc. ICDAR-2017, pp.507-512.

[15]    Meng Cai, Wenping Hu, Kai Chen, Lei Sun, Sen Liang, XiongJian Mo, Qiang Huo, “An open vocabulary OCR system with hybrid word-subword language models”, in ICDAR-2017, pp.519- 524

[16]    Kai Chen, Qiang Huo, “Training Deep Bidirectional LSTM Acoustic Model for LVCSR by a Context-Sensitive-Chunk BPTT Approach”, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.24, pp.1185-1193, 2016

[17]    Kai Chen, Zhi-Jie Yan, Qiang Huo, “Training Deep Bidirectional LSTM Acoustic Model for LVCSR by a Context-Sensitive-Chunk BPTT Approach” in INTERSPEECH-2015, pp.3600-3604

[18]    Wenping Hu, Meng Cai, Kai Chen, Haisong Ding, Lei Sun, Sen Liang, Xiongjian Mo, and Qiang Huo, “Sequence discriminative training for offline handwriting recognition by an interpolated CTC and lattice-free MMI objective function”, in Proc. ICDAR-2017, pp.519-524.

[19]    Kai Chen, Qiang Huo, “Scalable Training of Deep Learning Machines by Incremental Block Training with Intra-Block Parallel Optimization and Blockwise Model-Update Filtering”, in Proc. ICASSP-2016, pp.5880-5884.

[20]    Kai Chen, Zhi-Jie Yan, Qiang Huo, “A Context-Sensitive-Chunk BPTT Approach to Training Deep LSTM/BLSTM Recurrent Neural Networks for Offline Handwriting Recognition”, in Proc. ICDAR-2015, pp.411-415.

[21]    Jun Du, Qiang Huo, Kai Chen, “Designing Compact Classifiers for Rotation-Free Recognition of Large Vocabulary Online Handwritten Chinese Characters”, in Proc. ICASSP-2012, pp.1721- 1724.

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