Jacob A. Zavatone-Veth

Orcid: 0000-0002-4060-1738

According to our database1, Jacob A. Zavatone-Veth authored at least 14 papers between 2020 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Neural networks learn to magnify areas near decision boundaries.
CoRR, 2023

Learning Curves for Deep Structured Gaussian Feature Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Long Sequence Hopfield Memory.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
On Neural Network Kernels and the Storage Capacity Problem.
Neural Comput., 2022

Contrasting random and learned features in deep Bayesian linear regression.
CoRR, 2022

Drifting neuronal representations: Bug or feature?
Biol. Cybern., 2022

Natural gradient enables fast sampling in spiking neural networks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Asymptotics of representation learning in finite Bayesian neural networks.
CoRR, 2021

Exact priors of finite neural networks.
CoRR, 2021

Exact marginal prior distributions of finite Bayesian neural networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Asymptotics of representation learning in finite Bayesian neural networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Depth induces scale-averaging in overparameterized linear Bayesian neural networks.
Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

2020
Activation function dependence of the storage capacity of treelike neural networks.
CoRR, 2020


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