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pro vyhledávání: '"Chung, Clement"'
Autor:
Feng, Tiantian, Ramakrishna, Anil, Majmudar, Jimit, Peris, Charith, Wang, Jixuan, Chung, Clement, Zemel, Richard, Ziyadi, Morteza, Gupta, Rahul
Federated Learning (FL) is a popular algorithm to train machine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns. Typically, FL is trained with the assumption that no part of the user data
Externí odkaz:
http://arxiv.org/abs/2403.01615
Autor:
Good, Jack, Majmudar, Jimit, Dupuy, Christophe, Wang, Jixuan, Peris, Charith, Chung, Clement, Zemel, Richard, Gupta, Rahul
Continual Federated Learning (CFL) combines Federated Learning (FL), the decentralized learning of a central model on a number of client devices that may not communicate their data, and Continual Learning (CL), the learning of a model from a continua
Externí odkaz:
http://arxiv.org/abs/2310.15054
It is challenging to extract semantic meanings directly from audio signals in spoken language understanding (SLU), due to the lack of textual information. Popular end-to-end (E2E) SLU models utilize sequence-to-sequence automatic speech recognition (
Externí odkaz:
http://arxiv.org/abs/2305.02937
Autor:
Sharma, Rahul, Ramakrishna, Anil, MacLaughlin, Ansel, Rumshisky, Anna, Majmudar, Jimit, Chung, Clement, Avestimehr, Salman, Gupta, Rahul
Machine Learning (ML) systems are getting increasingly popular, and drive more and more applications and services in our daily life. This has led to growing concerns over user privacy, since human interaction data typically needs to be transmitted to
Externí odkaz:
http://arxiv.org/abs/2205.03092
Training mixed-domain translation models is a complex task that demands tailored architectures and costly data preparation techniques. In this work, we leverage federated learning (FL) in order to tackle the problem. Our investigation demonstrates th
Externí odkaz:
http://arxiv.org/abs/2205.01557
Autor:
Dupuy, Christophe, Roosta, Tanya G., Long, Leo, Chung, Clement, Gupta, Rahul, Avestimehr, Salman
Federated Learning (FL) applied to real world data may suffer from several idiosyncrasies. One such idiosyncrasy is the data distribution across devices. Data across devices could be distributed such that there are some "heavy devices" with large amo
Externí odkaz:
http://arxiv.org/abs/2202.03925
We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling. Specifically, we encode syntactic knowledge into the Transformer encoder by jointly training it to predict syntactic p
Externí odkaz:
http://arxiv.org/abs/2012.11689
Akademický článek
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Autor:
Chung, Clement1 (AUTHOR) clement_t_chung@yahoo.com, Umoru, Godsfavour2 (AUTHOR), Abboud, Karen2 (AUTHOR), Hobaugh, Eleanor2 (AUTHOR)
Publikováno v:
European Journal of Haematology. Jul2023, Vol. 111 Issue 1, p15-28. 14p.
Autor:
Chung, Clement
Tandem mass spectrometry (MS/MS) is the dominant approach for large-scale peptide sequencing in high-throughput proteomic profiling studies. The computational analysis of MS/MS spectra involves the identification of peptides from experimental spectra
Externí odkaz:
http://hdl.handle.net/1807/33965