Fetching the paper…
Reading the bibliography…
In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Good debt or bad debt: Detecting semantic orientations in economic texts
P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala · 2014
Earlier work this paper cites.
Gaussian error linear units (gelus)
D. Hendrycks and K. Gimpel · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
Earlier work this paper cites.
Distributed deep learning networks among institutions for medical imaging
K. Chang, N. Balachandar, C. Lam, D. Yi, J. Brown, A. Beers, B. Rosen, D. L. Rubin, and J. Kalpathy-Cramer · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
Earlier work this paper cites.
Distributed learning of deep neural network over multiple agents
O. Gupta and R. Raskar · 2018
Earlier work this paper cites.
Parameter-efficient transfer learning for nlp
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly · 2019
Earlier work this paper cites.
Privacy-preserving federated brain tumour segmentation
W. Li, F. Milletarì, D. Xu, N. Rieke, J. Hancox, W. Zhu, M. Baust, Y. Cheng, S. Ourselin, M. J. Cardoso, et al · 2019
Earlier work this paper cites.
Hellaswag: Can a machine really finish your sentence?
R. Zellers, A. Holtzman, Y. Bisk, A. Farhadi, and Y. Choi · 2019
Earlier work this paper cites.
Piqa: Reasoning about physical commonsense in natural language
Y. Bisk, R. Zellers, J. Gao, Y. Choi, et al · 2020
Earlier work this paper cites.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Earlier work this paper cites.
The future of digital health with federated learning
N. Rieke, J. Hancox, W. Li, F. Milletari, H. R. Roth, S. Albarqouni, S. Bakas, M. N. Galtier, B. A. Landman, K. Maier-Hein, et al · 2020
Earlier work this paper cites.
Federated learning for breast density classification: A real-world implementation
H. R. Roth, K. Chang, P. Singh, N. Neumark, W. Li, V. Gupta, S. Gupta, L. Qu, A. Ihsani, B. C. Bizzo, et al · 2020
Earlier work this paper cites.
Federated learning with matched averaging
H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, and Y. Khazaeni · 2020
Cited alongside, same era.
Batchcrypt: Efficient homomorphic encryption for { \{ Cross-Silo } \} federated learning
C. Zhang, S. Li, J. Xia, W. Wang, F. Yan, and Y. Liu · 2020
Cited alongside, same era.
Flip: Benchmark tasks in fitness landscape inference for proteins
C. Dallago, J. Mou, K. E. Johnston, B. J. Wittmann, N. Bhattacharya, S. Goldman, A. Madani, and K. K. Yang · 2021
Cited alongside, same era.
Federated learning for predicting clinical outcomes in patients with covid-19
I. Dayan, H. R. Roth, A. Zhong, A. Harouni, A. Gentili, A. Z. Abidin, A. Liu, A. B. Costa, B. J. Wood, C.-S. Tsai, et al · 2021
Cited alongside, same era.
Towards a unified view of parameter-efficient transfer learning
J. He, C. Zhou, X. Ma, T. Berg-Kirkpatrick, and G. Neubig · 2021
S. Smith, M. Patwary, B. Norick, P. LeGresley, S. Rajbhandari, J. Casper, Z. Liu, S. Prabhumoye, G. Zerveas, V. Korthikanti, et al · 2022
Later among the works it cites.
Closing the generalization gap of cross-silo federated medical image segmentation
A. Xu, W. Li, P. Guo, D. Yang, H. R. Roth, A. Hatamizadeh, C. Zhao, D. Xu, H. Huang, and Z. Xu · 2022
Later among the works it cites.
Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023
M. Conover, M. Hayes, A. Mathur, J. Xie, J. Wan, S. Shah, A. Ghodsi, P. Wendell, M. Zaharia, and R. Xin · 2023
Later among the works it cites.
Parameter-efficient fine-tuning of large-scale pre-trained language models
N. Ding, Y. Qin, G. Yang, F. Wei, Z. Yang, Y. Su, S. Hu, Y. Chen, C.-M. Chan, W. Chen, et al · 2023
Later among the works it cites.
A framework for few-shot language model evaluation, 12 2023
L. Gao, J. Tow, B. Abbasi, S. Biderman, S. Black, A. DiPofi, C. Foster, L. Golding, J. Hsu, A. Le Noac’h, H. Li, K. McDonell, N. Muennighoff, C. Ociepa, J. Phang, L. Reynolds, H. Schoelkopf, A. Skowron, L. Sutawika, E. Tang, A. Thite, B. Wang, K. Wang, and A. Zou · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2021
Cited alongside, same era.
Language models enable zero-shot prediction of the effects of mutations on protein function
J. Meier, R. Rao, R. Verkuil, J. Liu, T. Sercu, and A. Rives · 2021
Cited alongside, same era.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
A. Rives, J. Meier, T. Sercu, S. Goyal, Z. Lin, J. Liu, D. Guo, M. Ott, C. L. Zitnick, J. Ma, et al · 2021
Cited alongside, same era.
Winogrande: An adversarial winograd schema challenge at scale
K. Sakaguchi, R. L. Bras, C. Bhagavatula, and Y. Choi · 2021
Cited alongside, same era.
Federated learning improves site performance in multicenter deep learning without data sharing
K. V. Sarma, S. Harmon, T. Sanford, H. R. Roth, Z. Xu, J. Tetreault, D. Xu, M. G. Flores, A. G. Raman, R. Kulkarni, et al · 2021
Cited alongside, same era.
Light attention predicts protein location from the language of life
H. Stärk, C. Dallago, M. Heinzinger, and B. Rost · 2021
Cited alongside, same era.
Swarm learning for decentralized and confidential clinical machine learning
S. Warnat-Herresthal, H. Schultze, K. L. Shastry, S. Manamohan, S. Mukherjee, V. Garg, R. Sarveswara, K. Händler, P. Pickkers, N. A. Aziz, et al · 2021
Cited alongside, same era.
Later among the works it cites.
Fair federated medical image segmentation via client contribution estimation
M. Jiang, H. R. Roth, W. Li, D. Yang, C. Zhao, V. Nath, D. Xu, Q. Dou, and Z. Xu · 2023
Later among the works it cites.
Openassistant conversations–democratizing large language model alignment
A. Köpf, Y. Kilcher, D. von Rütte, S. Anagnostidis, Z.-R. Tam, K. Stevens, A. Barhoum, N. M. Duc, O. Stanley, R. Nagyfi, et al · 2023
Later among the works it cites.
Gpt understands, too
X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang · 2023
Later among the works it cites.
Code llama: Open foundation models for code
B. Roziere, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin, et al · 2023
Later among the works it cites.
Communication-efficient vertical federated learning with limited overlapping samples
J. Sun, Z. Xu, D. Yang, V. Nath, W. Li, C. Zhao, D. Xu, Y. Chen, and H. R. Roth · 2023
Later among the works it cites.
Stanford alpaca: An instruction-following llama model
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, et al · 2023
Later among the works it cites.
Condistfl: Conditional distillation for federated learning from partially annotated data
P. Wang, C. Shen, W. Wang, M. Oda, C.-S. Fuh, K. Mori, and H. R. Roth · 2023
Later among the works it cites.
Cross section of an animal cell, August 24, 2026
Pixabay · 2026
Closest in time.