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Daniel Kunin
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2020 – today
- 2024
- [j1]Daniel Kunin, Javier Sagastuy-Breña, Lauren Gillespie
, Eshed Margalit, Hidenori Tanaka, Surya Ganguli, Daniel L. K. Yamins:
The Limiting Dynamics of SGD: Modified Loss, Phase-Space Oscillations, and Anomalous Diffusion. Neural Comput. 36(1): 151-174 (2024) - [i9]Daniel Kunin, Allan Raventós, Clémentine Dominé, Feng Chen, David Klindt, Andrew M. Saxe, Surya Ganguli:
Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning. CoRR abs/2406.06158 (2024) - 2023
- [c7]Daniel Kunin, Atsushi Yamamura, Chao Ma, Surya Ganguli:
The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks. ICLR 2023 - [c6]Feng Chen, Daniel Kunin, Atsushi Yamamura, Surya Ganguli:
Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks. NeurIPS 2023 - [i8]Feng Chen, Daniel Kunin, Atsushi Yamamura, Surya Ganguli:
Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks. CoRR abs/2306.04251 (2023) - 2022
- [i7]Daniel Kunin, Atsushi Yamamura, Chao Ma, Surya Ganguli:
The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks. CoRR abs/2210.03820 (2022) - 2021
- [c5]Daniel Kunin, Javier Sagastuy-Breña, Surya Ganguli, Daniel L. K. Yamins, Hidenori Tanaka:
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics. ICLR 2021 - [c4]Hidenori Tanaka, Daniel Kunin:
Noether's Learning Dynamics: Role of Symmetry Breaking in Neural Networks. NeurIPS 2021: 25646-25660 - [i6]Hidenori Tanaka, Daniel Kunin:
Noether's Learning Dynamics: The Role of Kinetic Symmetry Breaking in Deep Learning. CoRR abs/2105.02716 (2021) - [i5]Daniel Kunin, Javier Sagastuy-Breña, Lauren Gillespie, Eshed Margalit, Hidenori Tanaka, Surya Ganguli, Daniel L. K. Yamins:
Rethinking the limiting dynamics of SGD: modified loss, phase space oscillations, and anomalous diffusion. CoRR abs/2107.09133 (2021) - 2020
- [c3]Daniel Kunin, Aran Nayebi, Javier Sagastuy-Breña, Surya Ganguli, Jonathan M. Bloom, Daniel Yamins:
Two Routes to Scalable Credit Assignment without Weight Symmetry. ICML 2020: 5511-5521 - [c2]Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, Surya Ganguli:
Pruning neural networks without any data by iteratively conserving synaptic flow. NeurIPS 2020 - [i4]Daniel Kunin, Aran Nayebi, Javier Sagastuy-Breña, Surya Ganguli, Jonathan M. Bloom, Daniel L. K. Yamins:
Two Routes to Scalable Credit Assignment without Weight Symmetry. CoRR abs/2003.01513 (2020) - [i3]Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, Surya Ganguli:
Pruning neural networks without any data by iteratively conserving synaptic flow. CoRR abs/2006.05467 (2020) - [i2]Daniel Kunin, Javier Sagastuy-Breña, Surya Ganguli, Daniel L. K. Yamins, Hidenori Tanaka:
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics. CoRR abs/2012.04728 (2020)
2010 – 2019
- 2019
- [c1]Daniel Kunin, Jonathan M. Bloom, Aleksandrina Goeva, Cotton Seed:
Loss Landscapes of Regularized Linear Autoencoders. ICML 2019: 3560-3569 - [i1]Daniel Kunin, Jonathan M. Bloom, Aleksandrina Goeva, Cotton Seed:
Loss Landscapes of Regularized Linear Autoencoders. CoRR abs/1901.08168 (2019)
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