Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Li, Hai (Helen)"'
Autor:
Lin, Yueqian, Fu, Yuzhe, Zhang, Jingyang, Liu, Yudong, Zhang, Jianyi, Sun, Jingwei, Li, Hai "Helen", Chen, Yiran
We introduce Speech Information Retrieval (SIR), a new long-context task for Speech Large Language Models (Speech LLMs), and present SPIRAL, a 1,012-sample benchmark testing models' ability to extract critical details from approximately 90-second spo
Externí odkaz:
http://arxiv.org/abs/2412.12009
Autor:
Zhang, Junyao, Zhou, Guanglei, Cheng, Feng, Ku, Jonathan, Ding, Qi, Gu, Jiaqi, Wang, Hanrui, Li, Hai "Helen", Chen, Yiran
Noisy Intermediate-Scale Quantum (NISQ) computers are currently limited by their qubit numbers, which hampers progress towards fault-tolerant quantum computing. A major challenge in scaling these systems is crosstalk, which arises from unwanted inter
Externí odkaz:
http://arxiv.org/abs/2411.02447
Autor:
Tang, Minxue, Wang, Yitu, Zhang, Jingyang, DiValentin, Louis, Ding, Aolin, Hass, Amin, Chen, Yiran, Li, Hai "Helen"
Federated Learning (FL) provides a strong privacy guarantee by enabling local training across edge devices without training data sharing, and Federated Adversarial Training (FAT) further enhances the robustness against adversarial examples, promoting
Externí odkaz:
http://arxiv.org/abs/2409.08372
Autor:
Zhang, Junyao, Wang, Hanrui, Ding, Qi, Gu, Jiaqi, Assouly, Reouven, Oliver, William D., Han, Song, Brown, Kenneth R., Li, Hai "Helen", Chen, Yiran
Noisy Intermediate-Scale Quantum (NISQ) computers face a critical limitation in qubit numbers, hindering their progression towards large-scale and fault-tolerant quantum computing. A significant challenge impeding scaling is crosstalk, characterized
Externí odkaz:
http://arxiv.org/abs/2401.17450
Autor:
Wang, Yitu, Li, Shiyu, Zheng, Qilin, Song, Linghao, Li, Zongwang, Chang, Andrew, Li, Hai "Helen", Chen, Yiran
Approximate nearest neighbor search (ANNS) is a key retrieval technique for vector database and many data center applications, such as person re-identification and recommendation systems. It is also fundamental to retrieval augmented generation (RAG)
Externí odkaz:
http://arxiv.org/abs/2312.03141
Autor:
Du, Zhixu, Li, Shiyu, Wu, Yuhao, Jiang, Xiangyu, Sun, Jingwei, Zheng, Qilin, Wu, Yongkai, Li, Ang, Li, Hai "Helen", Chen, Yiran
Publikováno v:
Seventh Conference on Machine Learning and Systems, (2024)
Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era of large models due to its inherent advantage, i.e., enlarging model capacity without incurring notable computational overhead. Yet, the realization of such benefits often re
Externí odkaz:
http://arxiv.org/abs/2310.18859
Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. In this work, we explicitly uncover external covariate shift problem in FL, which is caused by the independent l
Externí odkaz:
http://arxiv.org/abs/2210.03277
Akademický článek
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Akademický článek
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Autor:
Chen, Yiran, Li, Hai (Helen), Wu, Chunpeng, Song, Chang, Li, Sicheng, Min, Chuhan, Cheng, Hsin-Pai, Wen, Wei, Liu, Xiaoxiao
Publikováno v:
In Integration March 2018 61:49-61
