| People |
This Week |
Next Week |
Task Tracking (DeadLine)
|
| Dong Wang
|
- Two days of teaching, two days of training
- Refine NS paper
- College version of the AI hand book
|
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| Lantian Li
|
- BUPT Graduate matters
- Huawei projects (N&F)
- Kicking off the translation of ML book
|
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| Ying Shi
|
- Prepare some material about the Huang Danian Challenge final report.
|
|
|
| Zhenghai You
|
- Complete the final delivery of Huawei TSE
|
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|
| Junming Yuan
|
- Find bug and revise my ASR code.
- Review Middle-school AI practice handbook(4).
|
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|
| Xiaolou Li
|
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| Zehua Liu
|
- Data Audit and paper revise
- Reading paper about Qwen2.5-omni and maybe apply VTS to this model
- Data augmentation for AlignVSR
|
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|
| Pengqi Li
|
- Research progress: A large number of experiments and visualizations, but there is still confusion in "How to display the explanation".
- Teaching work: Prepare for the AI course at Tsinghua middle School and assist with the graduation ceremony.
|
- Summarize the work that has been completed and review similar papers.
|
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| Wan Lin
|
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| Tianhao Wang
|
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| Xiaoxue Luo
|
- prepare Huawei project meeting with tianhao
- the English version of the AI book
|
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| Zhenyu Zhou
|
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| Junhui Chen
|
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| Jiaying Wang
|
- train pure asr system use clean wsj0
- loss setting and data: remove zero_infinity parameter, use speech longer than 6s
- training: loss can converge at under 20(reasonable), LER 45%(higher than expectation)
- plan: change cnn settings, explore whether the performance is related to word length
- HongYan competition slides modification
|
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|
| Yu Zhang
|
- Profit analysis about Multi Agent System
- Reflection time range experiment
- Some Data analysis task in RoyalFlush (external)
|
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|
| Wenqiang Du
|
- New KWS model for AIbabel(30 words and speech enroll)
|
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| Yang Wei
|
- Finish AIbabel online KWS model encryption.
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| Turi
|
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| Yue Gu
|
- complete the introduction, method and experimental setup, result is in progress
|
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| Qi Qu
|
- Text-enroll KWS models routine work: finding ideal thresholds (word list = Mandarin Chinese 200 words, models = [dist-20250310, dist-20250520]).
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