| People |
This Week |
Next Week |
Task Tracking (DeadLine)
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| Dong Wang
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| Lantian Li
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- NDRC daily work
- MLA book (3/4)
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| Wenqiang Du
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- Baseline testing of multimodal models(ing)
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| Yang Wei
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- Train audio separation model for 3 class (speech, song, bird). Dealing with low volume output problem.
- Test streaming AVSR demo (CER: mix: 72%, offline_separation: 20%, streaming_separation: 42%).
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| Ying Shi
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| Yue Gu
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| Lily
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| Pengqi Li
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- Paper Draft Completion & Revision Plan
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| Junming Yuan
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- Preparing the materials for attending ICASSP
- ZH paper draft (need refine)
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| Yu Zhang
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- GPU Util: [1]
- Chain level experiments:
- After introducing the Metric Reward, the weights of correct edges converge faster compared to training with pure reinforcement learning alone.
- The worse the situation when the Metric Reward is introduced (i.e., the lower the weights of critical edges), the more significant the difference compared to not using the Metric Reward.
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| Junhui Chen
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- To strengthen the robustness of the conclusions, conducting additional experiments:
- Introduce a new baseline (AgentPrune).
- Add experiments on a new dataset (GSM8K).
- Reproduce the results on other LLM base models.
- Paper writing
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| Xiaoxue Luo
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- attractor visualization analysis [2]
- The accuracy of attractor counting is lower than expected, may be due to the mixed scenes are complex（2-5mix), retrain the 2-3mix model
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| Bochao Hu
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- meet the all requirements and hand over vts pipeline to Sun Chang, waiting for his test
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| Hongcheng Zhang
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- test MLLM for aibabel's project
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| Weiman Sun
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- supplement our audioset dataset for specific classes
- test large multimodal models
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| Ge Gao
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- reproduce spatialnet for speech separation
- write my graduation thesis
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| Shuailong Li
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- read some papers
- USE（Sepformer and BSRNN and TDN）
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