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Peter E. Latham
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2020 – today
- 2024
- [c13]Yedi Zhang, Peter E. Latham, Andrew M. Saxe:
Understanding Unimodal Bias in Multimodal Deep Linear Networks. ICML 2024 - [i6]Yedi Zhang, Andrew M. Saxe, Peter E. Latham:
When Are Bias-Free ReLU Networks Like Linear Networks? CoRR abs/2406.12615 (2024) - [i5]Will Dorrell, Kyle Hsu, Luke Hollingsworth, Jin Hwa Lee, Jiajun Wu, Chelsea Finn, Peter E. Latham, Timothy Edward John Behrens, James C. R. Whittington:
Don't Cut Corners: Exact Conditions for Modularity in Biologically Inspired Representations. CoRR abs/2410.06232 (2024) - 2023
- [c12]Will Dorrell, Peter E. Latham, Tim E. J. Behrens, James C. R. Whittington:
Actionable Neural Representations: Grid Cells from Minimal Constraints. ICLR 2023 - [c11]Will Dorrell, Maria Yuffa, Peter E. Latham:
Meta-Learning the Inductive Bias of Simple Neural Circuits. ICML 2023: 8389-8402 - [i4]Yedi Zhang, Peter E. Latham, Andrew M. Saxe:
A Theory of Unimodal Bias in Multimodal Learning. CoRR abs/2312.00935 (2023) - 2022
- [j15]Sina Tootoonian
, Andreas T. Schaefer
Sparse connectivity for MAP inference in linear models using sister mitral cells. PLoS Comput. Biol. 18(1) (2022) - [c10]Naoki Hiratani, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham:
On the Stability and Scalability of Node Perturbation Learning. NeurIPS 2022 - 2021
- [c9]Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham:
Towards Biologically Plausible Convolutional Networks. NeurIPS 2021: 13924-13936 - [c8]Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh:
Powerpropagation: A sparsity inducing weight reparameterisation. NeurIPS 2021: 28889-28903 - [i3]Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham:
Towards Biologically Plausible Convolutional Networks. CoRR abs/2106.13031 (2021) - [i2]Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh:
Powerpropagation: A sparsity inducing weight reparameterisation. CoRR abs/2110.00296 (2021) - 2020
- [c7]Roman Pogodin, Peter E. Latham:
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks. NeurIPS 2020 - [i1]Roman Pogodin, Peter E. Latham:
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks. CoRR abs/2006.07123 (2020)
2010 – 2019
- 2019
- [j14]Christopher Brian Currin
Think: Theory for Africa. PLoS Comput. Biol. 15(7) (2019) - 2017
- [j13]Joel Zylberberg, Alexandre Pouget
Robust information propagation through noisy neural circuits. PLoS Comput. Biol. 13(4) (2017) - 2016
- [j12]Laurence Aitchison, Nicola Corradi, Peter E. Latham
Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables. PLoS Comput. Biol. 12(12) (2016) - 2015
- [j11]Laurence Aitchison, Dan Bang
Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making. PLoS Comput. Biol. 11(10) (2015) - 2014
- [j10]Agnieszka Grabska-Barwinska, Peter E. Latham:
How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes? J. Comput. Neurosci. 36(3): 469-481 (2014) - 2013
- [j9]Jakob H. Macke
Estimation Bias in Maximum Entropy Models. Entropy 15(8): 3109-3119 (2013) - [j8]Edward W. J. Wallace
Randomly Connected Networks Have Short Temporal Memory. Neural Comput. 25(6): 1408-1439 (2013) - [c6]Dan Bang, Riccardo Fusaroli, Kristian Tylén, Karsten Olsen, Peter E. Latham, Jennifer Lau, Andreas Roepstorff, Geraint Rees, Chris D. Frith, Bahador Bahrami:
Does interaction matter? Testing whether fast and frugal heuristics can replace interaction in collective decision-making. CogSci 2013 - [c5]Agnieszka Grabska-Barwinska, Jeffrey M. Beck, Alexandre Pouget, Peter E. Latham:
Demixing odors - fast inference in olfaction. NIPS 2013: 1968-1976 - 2011
- [c4]Jakob H. Macke, Iain Murray, Peter E. Latham:
How biased are maximum entropy models? NIPS 2011: 2034-2042
2000 – 2009
- 2009
- [j7]Yasser Roudi
Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't. PLoS Comput. Biol. 5(5) (2009) - [j6]Peter E. Latham, Yasser Roudi
Mutual information. Scholarpedia 4(1): 1658 (2009) - 2007
- [j5]Yasser Roudi
A Balanced Memory Network. PLoS Comput. Biol. 3(9) (2007) - [c3]Jonathan W. Pillow, Peter E. Latham:
Neural characterization in partially observed populations of spiking neurons. NIPS 2007: 1161-1168 - 2004
- [j4]Peter E. Latham, Sheila Nirenberg:
Computing and Stability in Cortical Networks. Neural Comput. 16(7): 1385-1412 (2004) - 2003
- [j3]Nicolas Brunel, Peter E. Latham:
Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron. Neural Comput. 15(10): 2281-2306 (2003) - 2001
- [c2]Peter E. Latham:
Associative memory in realistic neuronal networks. NIPS 2001: 237-244
1990 – 1999
- 1999
- [j2]Alexandre Pouget
Narrow vs Wide Tuning Curves: What's Best for a Population Code? Neural Comput. 11(1): 85-90 (1999) - 1998
- [j1]Alexandre Pouget
Statistically Efficient Estimation Using Population Coding. Neural Comput. 10(2): 373-401 (1998) - [c1]Sophie Denève, Alexandre Pouget, Peter E. Latham:
Divisive Normalization, Line Attractor Networks and Ideal Observers. NIPS 1998: 104-110
Coauthor Index
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