Peter E. Latham

According to our database, Peter E. Latham authored at least 30 papers between 1998 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

When Are Bias-Free ReLU Networks Like Linear Networks?

Yedi Zhang, Andrew M. Saxe, Peter E. Latham

CoRR, 2024

Understanding Unimodal Bias in Multimodal Deep Linear Networks.

Yedi Zhang, Peter E. Latham, Andrew M. Saxe

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

A Theory of Unimodal Bias in Multimodal Learning.

Yedi Zhang, Peter E. Latham, Andrew M. Saxe

CoRR, 2023

Meta-Learning the Inductive Bias of Simple Neural Circuits.

Will Dorrell, Maria Yuffa, Peter E. Latham

Proceedings of the International Conference on Machine Learning, 2023

Actionable Neural Representations: Grid Cells from Minimal Constraints.

Will Dorrell, Peter E. Latham, Tim E. J. Behrens, James C. R. Whittington

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Sparse connectivity for MAP inference in linear models using sister mitral cells.

Sina Tootoonian, Andreas T. Schaefer, Peter E. Latham

PLoS Comput. Biol., 2022

On the Stability and Scalability of Node Perturbation Learning.

Naoki Hiratani, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Powerpropagation: A sparsity inducing weight reparameterisation.

Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Towards Biologically Plausible Convolutional Networks.

Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks.

Roman Pogodin, Peter E. Latham

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Think: Theory for Africa.

Christopher Brian Currin, Phumlani N. Khoza, Alexander D. Antrobus, Peter E. Latham, Tim P. Vogels, Joseph Valentino Raimondo

PLoS Comput. Biol., 2019

2017

Robust information propagation through noisy neural circuits.

Joel Zylberberg, Alexandre Pouget, Peter E. Latham, Eric Shea-Brown

PLoS Comput. Biol., 2017

2016

Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables.

Laurence Aitchison, Nicola Corradi, Peter E. Latham

PLoS Comput. Biol., 2016

2015

Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making.

Laurence Aitchison, Dan Bang, Bahador Bahrami, Peter E. Latham

PLoS Comput. Biol., 2015

2014

How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?

Agnieszka Grabska-Barwinska, Peter E. Latham

J. Comput. Neurosci., 2014

2013

Randomly Connected Networks Have Short Temporal Memory.

Edward W. J. Wallace, Hamid Reza Maei, Peter E. Latham

Neural Comput., 2013

Estimation Bias in Maximum Entropy Models.

Jakob H. Macke, Iain Murray, Peter E. Latham

Entropy, 2013

Demixing odors - fast inference in olfaction.

Agnieszka Grabska-Barwinska, Jeffrey M. Beck, Alexandre Pouget, Peter E. Latham

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Does interaction matter? Testing whether fast and frugal heuristics can replace interaction in collective decision-making.

Dan Bang, Riccardo Fusaroli, Kristian Tylén, Karsten Olsen, Peter E. Latham, Jennifer Lau, Andreas Roepstorff, Geraint Rees, Chris D. Frith, Bahador Bahrami

Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

2011

How biased are maximum entropy models?

Jakob H. Macke, Iain Murray, Peter E. Latham

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2009

Mutual information.

Peter E. Latham, Yasser Roudi

Scholarpedia, 2009

Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't.

Yasser Roudi, Sheila Nirenberg, Peter E. Latham

PLoS Comput. Biol., 2009

2007

A Balanced Memory Network.

Yasser Roudi, Peter E. Latham

PLoS Comput. Biol., 2007

Neural characterization in partially observed populations of spiking neurons.

Jonathan W. Pillow, Peter E. Latham

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2004

Computing and Stability in Cortical Networks.

Peter E. Latham, Sheila Nirenberg

Neural Comput., 2004

2003

Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron.

Nicolas Brunel, Peter E. Latham

Neural Comput., 2003

2001

Associative memory in realistic neuronal networks.

Peter E. Latham

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

1999

Narrow vs Wide Tuning Curves: What's Best for a Population Code?

Alexandre Pouget, Sophie Denève, Jean-Christophe Ducom, Peter E. Latham

Neural Comput., 1999

1998

Statistically Efficient Estimation Using Population Coding.

Alexandre Pouget, Kechen Zhang, Sophie Denève, Peter E. Latham

Neural Comput., 1998

Divisive Normalization, Line Attractor Networks and Ideal Observers.

Sophie Denève, Alexandre Pouget, Peter E. Latham

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998