| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
|
- Training for Tsinghua Primary School
- Training for ZhongLv group
- Refine AIGE book for college students
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| Lantian Li
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| Ying Shi
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| Zhenghai You
|
- Some tests of Huawei project(emotion & targetless)
- Revised Huawei report
|
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| Junming Yuan
|
- Extended ASR finetuning exp[1]
- Review Middle-school AI practice handbook(9/20).
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| Xiaolou Li
|
- Audit video data
- Improve the speed of inference
- Paper reading
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| Zehua Liu
|
- Audio video data check
- help xiaolou to transfer web code to GA server
- Paper reading
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| Pengqi Li
|
- Organize a pre-experiment to explore the following questions:
- How to evaluate reliability(some experiment shows reliability)
- How to visualize (but still thinking about how to display the "explanation").
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| Wan Lin
|
- multi-scale feature exp: no improvement
- reading paper
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| Tianhao Wang
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| Xiaoxue Luo
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- Use some metrics in audio generation task to evaluate the performance of CLAPSep and our model
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| Zhenyu Zhou
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| Junhui Chen
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- reading paper
- weekly report
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| Jiaying Wang
|
- ASR training
- clean data ctc loss converge at 200 (higher than expactation)
- LER have abnormal result：predicted labels tend to concentrate on a few letters, debuging
- Modify the PPT for the hongyan campus competition.
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| Yu Zhang
|
- For news and sentiment analysis, use reflection as "condition" instead of as "reference" [2]
- Increase news sources (the abuse of reflection is often found when there is a lack of useful news on that day)
- Adding more benchmark (implementing rule based benchmark in TradingAgents (MACD RSI ... ))
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- Detail Analysis of current backtesting result and check the paper plan with Teacher Li
|
| Wenqiang Du
|
- Business trip to Changzhou
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| Yang Wei
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- Finish license check module for AIBabel ASR gRPC service.
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| Turi
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| Yue Gu
|
- fix a bug about a reproduced method and retrain the model
- polish my introduction according to some suggestions
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| Qi Qu
|
- On-device text-enroll KWS demo.
- Problem analysis: performance drop caused by precision loss in quantization. [3]
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