Peter E. Latham

40 papers A* 9B 1Journal 25Unranked 5
YearRankTypeTitle / Venue / Authors
2026 J jnl
Optimal Learning Rate Schedule for Balancing Effort and Performance.
CoRR
Valentina Njaradi, Rodrigo Carrasco-Davis, Peter E. Latham, Andrew M. Saxe
2025 A* conf
Range, not Independence, Drives Modularity in Biologically Inspired Representations.
ICLR
Will Dorrell, Kyle Hsu, Luke Hollingsworth, Jin Hwa Lee, Jiajun Wu, Chelsea Finn, Peter E. Latham, Timothy Edward John Behrens, James C. R. Whittington
2025 J jnl
Saddle-to-Saddle Dynamics Explains A Simplicity Bias Across Neural Network Architectures.
CoRR
Yedi Zhang, Andrew M. Saxe, Peter E. Latham
2025 A* conf
Training Dynamics of In-Context Learning in Linear Attention.
ICML
Yedi Zhang, Aaditya K. Singh, Peter E. Latham, Andrew M. Saxe
2025 J jnl
Training Dynamics of In-Context Learning in Linear Attention.
CoRR
Yedi Zhang, Aaditya K. Singh, Peter E. Latham, Andrew M. Saxe
2025 J jnl
When Are Bias-Free ReLU Networks Effectively Linear Networks?
Trans. Mach. Learn. Res.
Yedi Zhang, Andrew M. Saxe, Peter E. Latham
2024 J jnl
Don't Cut Corners: Exact Conditions for Modularity in Biologically Inspired Representations.
CoRR
Will Dorrell, Kyle Hsu, Luke Hollingsworth, Jin Hwa Lee, Jiajun Wu, Chelsea Finn, Peter E. Latham, Timothy Edward John Behrens, James C. R. Whittington
2024 A* conf
Understanding Unimodal Bias in Multimodal Deep Linear Networks.
ICML
Yedi Zhang, Peter E. Latham, Andrew M. Saxe
2024 J jnl
When Are Bias-Free ReLU Networks Like Linear Networks?
CoRR
Yedi Zhang, Andrew M. Saxe, Peter E. Latham
2023 J jnl
A Theory of Unimodal Bias in Multimodal Learning.
CoRR
Yedi Zhang, Peter E. Latham, Andrew M. Saxe
2023 A* conf
Actionable Neural Representations: Grid Cells from Minimal Constraints.
ICLR
Will Dorrell, Peter E. Latham, Tim E. J. Behrens, James C. R. Whittington
2023 A* conf
Meta-Learning the Inductive Bias of Simple Neural Circuits.
ICML
Will Dorrell, Maria Yuffa, Peter E. Latham
2022 A* conf
On the Stability and Scalability of Node Perturbation Learning.
NeurIPS
Naoki Hiratani, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham
2022 J jnl
Sparse connectivity for MAP inference in linear models using sister mitral cells.
PLoS Comput. Biol.
Sina Tootoonian, Andreas T. Schaefer, Peter E. Latham
2021 A* conf
Powerpropagation: A sparsity inducing weight reparameterisation.
NeurIPS
Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh
2021 J jnl
Powerpropagation: A sparsity inducing weight reparameterisation.
CoRR
Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh
2021 A* conf
Towards Biologically Plausible Convolutional Networks.
NeurIPS
Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham
2021 J jnl
Towards Biologically Plausible Convolutional Networks.
CoRR
Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham
2020 A* conf
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks.
NeurIPS
Roman Pogodin, Peter E. Latham
2020 J jnl
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks.
CoRR
Roman Pogodin, Peter E. Latham
2019 J jnl
Think: Theory for Africa.
PLoS Comput. Biol.
Christopher Brian Currin, Phumlani N. Khoza, Alexander D. Antrobus, Peter E. Latham, Tim P. Vogels, Joseph Valentino Raimondo
2017 J jnl
Robust information propagation through noisy neural circuits.
PLoS Comput. Biol.
Joel Zylberberg, Alexandre Pouget, Peter E. Latham, Eric Shea-Brown
2016 J jnl
Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables.
PLoS Comput. Biol.
Laurence Aitchison, Nicola Corradi, Peter E. Latham
2015 J jnl
Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making.
PLoS Comput. Biol.
Laurence Aitchison, Dan Bang, Bahador Bahrami, Peter E. Latham
2014 J jnl
How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?
J. Comput. Neurosci.
Agnieszka Grabska-Barwinska, Peter E. Latham
2013 — conf
Demixing odors - fast inference in olfaction.
NIPS
Agnieszka Grabska-Barwinska, Jeffrey M. Beck, Alexandre Pouget, Peter E. Latham
2013 B conf
Does interaction matter? Testing whether fast and frugal heuristics can replace interaction in collective decision-making.
CogSci
Dan Bang, Riccardo Fusaroli, Kristian Tylén, Karsten Olsen, Peter E. Latham, Jennifer Lau, Andreas Roepstorff, Geraint Rees, Chris D. Frith, Bahador Bahrami
2013 J jnl
Estimation Bias in Maximum Entropy Models.
Entropy
Jakob H. Macke, Iain Murray, Peter E. Latham
2013 J jnl
Randomly Connected Networks Have Short Temporal Memory.
Neural Comput.
Edward W. J. Wallace, Hamid Reza Maei, Peter E. Latham
2011 — conf
How biased are maximum entropy models?
NIPS
Jakob H. Macke, Iain Murray, Peter E. Latham
2009 J jnl
Mutual information.
Scholarpedia
Peter E. Latham, Yasser Roudi
2009 J jnl
Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't.
PLoS Comput. Biol.
Yasser Roudi, Sheila Nirenberg, Peter E. Latham
2007 J jnl
A Balanced Memory Network.
PLoS Comput. Biol.
Yasser Roudi, Peter E. Latham
2007 — conf
Neural characterization in partially observed populations of spiking neurons.
NIPS
Jonathan W. Pillow, Peter E. Latham
2004 J jnl
Computing and Stability in Cortical Networks.
Neural Comput.
Peter E. Latham, Sheila Nirenberg
2003 J jnl
Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron.
Neural Comput.
Nicolas Brunel, Peter E. Latham
2001 — conf
Associative memory in realistic neuronal networks.
NIPS
Peter E. Latham
1999 J jnl
Narrow vs Wide Tuning Curves: What's Best for a Population Code?
Neural Comput.
Alexandre Pouget, Sophie Denève, Jean-Christophe Ducom, Peter E. Latham
1998 — conf
Divisive Normalization, Line Attractor Networks and Ideal Observers.
NIPS
Sophie Denève, Alexandre Pouget, Peter E. Latham
1998 J jnl
Statistically Efficient Estimation Using Population Coding.
Neural Comput.
Alexandre Pouget, Kechen Zhang, Sophie Denève, Peter E. Latham