Zobrazeno 1 - 10
of 216
pro vyhledávání: '"Trần Cương"'
Autor:
Lê Văn Hưởng, Trần Cương
Publikováno v:
Tạp chí Khoa học Đại học Mở Thành phố Hồ Chí Minh - Kinh tế và Quản trị kinh doanh, Vol 17, Iss 5, Pp 80-95 (2022)
This paper examines the impact of public governance on enterprise performance in Vietnam. Using data from the Vietnamese enterprises white book and Provincial Competitiveness Index (PCI) in the period 2016 - 2019, applied Feasible Generalized Least S
Externí odkaz:
https://doaj.org/article/e0ee2b89ea404da6a6de6288ef816058
Autor:
Trần Cương, Phạm Duy Anh
Publikováno v:
Tạp chí Khoa học Đại học Cần Thơ, Vol 57, Iss 5 (2021)
Mục tiêu của nghiên cứu là xác định tác động của hiệu quả quản trị nhà nước (bao gồm: ổn định chính trị, kiểm soát tham nhũng, pháp quyền, chất lượng điều hành, quyền phát ngôn và trách n
Externí odkaz:
https://doaj.org/article/7417cc45ddd7439fb6d89649236b80b9
The principle of data minimization aims to reduce the amount of data collected, processed or retained to minimize the potential for misuse, unauthorized access, or data breaches. Rooted in privacy-by-design principles, data minimization has been endo
Externí odkaz:
http://arxiv.org/abs/2405.19471
Autor:
Das, Saswat, Romanelli, Marco, Tran, Cuong, Reza, Zarreen, Kailkhura, Bhavya, Fioretto, Ferdinando
Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models (LLMs) due to their reduced computational and memory requirements. This paper investigates the effectiveness of these methods in capturing the s
Externí odkaz:
http://arxiv.org/abs/2405.18572
Autor:
Nelaturu, Sree Harsha, Ravichandran, Nishaanth Kanna, Tran, Cuong, Hooker, Sara, Fioretto, Ferdinando
In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data. This oversight is particularly problematic in contexts like ML-as-a-service platforms, where users often
Externí odkaz:
http://arxiv.org/abs/2312.03886
Autor:
Tran, Cuong, Fioretto, Ferdinando
In domains with high stakes such as law, recruitment, and healthcare, learning models frequently rely on sensitive user data for inference, necessitating the complete set of features. This not only poses significant privacy risks for individuals but
Externí odkaz:
http://arxiv.org/abs/2305.17593
Autor:
Tran, Cuong, Fioretto, Ferdinando
The Private Aggregation of Teacher Ensembles (PATE) is a machine learning framework that enables the creation of private models through the combination of multiple "teacher" models and a "student" model. The student model learns to predict an output
Externí odkaz:
http://arxiv.org/abs/2305.11807
Autor:
Tran, Cuong, Fioretto, Ferdinando
A number of learning models used in consequential domains, such as to assist in legal, banking, hiring, and healthcare decisions, make use of potentially sensitive users' information to carry out inference. Further, the complete set of features is ty
Externí odkaz:
http://arxiv.org/abs/2302.00077
The remarkable performance of deep learning models and their applications in consequential domains (e.g., facial recognition) introduces important challenges at the intersection of equity and security. Fairness and robustness are two desired notions
Externí odkaz:
http://arxiv.org/abs/2211.11835
Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy. This paper shows that pruning may create or exacerbate disparate impacts. The paper sheds light on
Externí odkaz:
http://arxiv.org/abs/2205.13574