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Nikhil Ghosh
Nikhil Ghosh
Verified email at berkeley.edu
Title
Cited by
Cited by
Year
Lora+: Efficient low rank adaptation of large models
S Hayou, N Ghosh, B Yu
arXiv preprint arXiv:2402.12354, 2024
1022024
More is better in modern machine learning: when infinite overparameterization is optimal and overfitting is obligatory
JB Simon, D Karkada, N Ghosh, M Belkin
arXiv preprint arXiv:2311.14646, 2023
24*2023
Deconstructing distributions: A pointwise framework of learning
G Kaplun, N Ghosh, S Garg, B Barak, P Nakkiran
arXiv preprint arXiv:2202.09931, 2022
232022
The Three Stages of Learning Dynamics in High-dimensional Kernel Methods
N Ghosh, S Mei, B Yu
arXiv preprint arXiv:2111.07167, 2021
232021
On the benefits of learning to route in mixture-of-experts models
N Dikkala, N Ghosh, R Meka, R Panigrahy, N Vyas, X Wang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language …, 2023
172023
Landmark ordinal embedding
N Ghosh, Y Chen, Y Yue
Advances in neural information processing systems 32, 2019
132019
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors
N Ghosh, M Belkin
SIAM Journal on Mathematics of Data Science 5 (4), 977-1004, 2023
92023
The Impact of Initialization on LoRA Finetuning Dynamics
S Hayou, N Ghosh, B Yu
arXiv preprint arXiv:2406.08447, 2024
62024
Alternating updates for efficient transformers
C Baykal, D Cutler, N Dikkala, N Ghosh, R Panigrahy, X Wang
Advances in Neural Information Processing Systems 36, 2024
52024
The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning
N Ghosh, S Frei, W Ha, B Yu
arXiv preprint arXiv:2308.03215, 2023
12023
Theoretical Foundations of Deep Learning: Optimization, Generalization, and Scaling
N Ghosh
University of California, Berkeley, 2024
2024
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