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Publication search results
found 66 matches
- 2024
- Paulo Cavalin, Pedro Henrique Domingues, Claudio S. Pinhanez, Julio Nogima:
Fixing Rogue Memorization in Many-to-One Multilingual Translators of Extremely-Low-Resource Languages by Rephrasing Training Samples. NAACL-HLT 2024: 4503-4514 - Branislav Pecher, Ivan Srba, Mária Bieliková:
Fine-Tuning, Prompting, In-Context Learning and Instruction-Tuning: How Many Labelled Samples Do We Need? CoRR abs/2402.12819 (2024) - 2023
- Albert Selebea Lutakamale, Yona Zakaria Manyesela:
Machine Learning-Based Fingerprinting Positioning in Massive MIMO Networks: Analysis on the Impact of Small Training Sample Size to the Positioning Performance. SN Comput. Sci. 4(3): 286 (2023) - Yang Su, Michael Chesser, Yansong Gao, Alanson P. Sample, Damith C. Ranasinghe:
Wisecr: Secure Simultaneous Code Dissemination to Many Batteryless Computational RFID Devices. IEEE Trans. Dependable Secur. Comput. 20(3): 2188-2207 (2023) - Ravsehaj Singh Puri, Swaroop Mishra, Mihir Parmar, Chitta Baral:
How Many Data Samples is an Additional Instruction Worth? EACL (Findings) 2023: 1012-1027 - Vivien Cabannes, Stefano Vigogna:
How many samples are needed to leverage smoothness? NeurIPS 2023 - Idio Guarino, Chao Wang, Alessandro Finamore, Antonio Pescapè, Dario Rossi:
Many or Few Samples?: Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification. TMA 2023: 1-10 - Idio Guarino, Chao Wang, Alessandro Finamore, Antonio Pescapè, Dario Rossi:
Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification. CoRR abs/2305.12432 (2023) - Vivien Cabannes, Stefano Vigogna:
How many samples are needed to leverage smoothness? CoRR abs/2305.16014 (2023) - 2022
- Shaoheng Liang, Jason Willis, Jinzhuang Dou, Vakul Mohanty, Yuefan Huang, Eduardo Vilar, Ken Chen:
Sensei: how many samples to tell a change in cell type abundance? BMC Bioinform. 23(1): 2 (2022) - Martin Bicher, Matthias Wastian, Dominik Brunmeir, Niki Popper:
Review on Monte Carlo Simulation Stopping Rules: How Many Samples Are Really Enough? Simul. Notes Eur. 32(1): 1-8 (2022) - Pallavi Venugopal Minimol, Deepak Mishra, Rama Krishna Sai Subrahmanyam Gorthi:
Guided MDNet tracker with guided samples. Vis. Comput. 38(3): 1135-1149 (2022) - Ian Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár:
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. AAMAS 2022: 507-515 - Chaoning Zhang, Kang Zhang, Trung X. Pham, Axi Niu, Zhinan Qiao, Chang D. Yoo, In So Kweon:
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo. CVPR 2022: 14421-14430 - Chuhan Wu, Fangzhao Wu, Yongfeng Huang:
Rethinking InfoNCE: How Many Negative Samples Do You Need? IJCAI 2022: 2509-2515 - Ravsehaj Singh Puri, Swaroop Mishra, Mihir Parmar, Chitta Baral:
How Many Data Samples is an Additional Instruction Worth? CoRR abs/2203.09161 (2022) - Chaoning Zhang, Kang Zhang, Trung X. Pham, Axi Niu, Zhinan Qiao, Chang D. Yoo, In So Kweon:
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo. CoRR abs/2203.17248 (2022) - Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua Gubler, Christopher Michael Rytting, David Wingate:
Out of One, Many: Using Language Models to Simulate Human Samples. CoRR abs/2209.06899 (2022) - 2021
- Yi Zhao, Jianchao Zeng, Ying Tan:
Neighborhood samples and surrogate assisted multi-objective evolutionary algorithm for expensive many-objective optimization problems. Appl. Soft Comput. 105: 107268 (2021) - Manyu Zhao, Zhengxin Wang, Jun Ye:
Non-fragile quasi-synchronization of delayed heterogeneous dynamical networks with memory sampled-data control. Trans. Inst. Meas. Control 43(10) (2021) - Jon Fagerström, Sebastian J. Schlecht, Vesa Välimäki:
One-to-Many Conversion for Percussive Samples. DAFx 2021: 129-135 - Chih-Ting Liu, Man-Yu Lee, Tsai-Shien Chen, Shao-Yi Chien:
Hard Samples Rectification for Unsupervised Cross-Domain Person Re-Identification. ICIP 2021: 429-433 - Yang Su, Michael Chesser, Yansong Gao, Alanson P. Sample, Damith C. Ranasinghe:
Wisecr: Secure Simultaneous Code Disseminationto Many Batteryless Computational RFID Devices. CoRR abs/2103.10671 (2021) - Chuhan Wu, Fangzhao Wu, Yongfeng Huang:
Rethinking InfoNCE: How Many Negative Samples Do You Need? CoRR abs/2105.13003 (2021) - Ian Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár:
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. CoRR abs/2106.01285 (2021) - Chih-Ting Liu, Man-Yu Lee, Tsai-Shien Chen, Shao-Yi Chien:
Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification. CoRR abs/2106.07204 (2021) - 2020
- Saleh Shahinfar, Paul D. Meek, Gregory Falzon:
"How many images do I need?" Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring. Ecol. Informatics 57: 101085 (2020) - Lida Asgharian, Hossein Ebrahimnezhad:
How many sample points are sufficient for 3D model surface representation and accurate mesh simplification? Multim. Tools Appl. 79(39-40): 29595-29620 (2020) - Anurag Anshu, Srinivasan Arunachalam, Tomotaka Kuwahara, Mehdi Soleimanifar:
Sample-efficient learning of quantum many-body systems. FOCS 2020: 685-691 - Andrew Bassilakis, Andrew Drucker, Mika Göös, Lunjia Hu, Weiyun Ma, Li-Yang Tan:
The Power of Many Samples in Query Complexity. ICALP 2020: 9:1-9:18
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