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ORCIDRadford M. Neal
Person information
- affiliation: University of Toronto, Canada
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2020 – today
- 2020
[i6]Radford M. Neal:
Non-reversibly updating a uniform [0, 1] value for Metropolis accept/reject decisions. CoRR abs/2001.11950 (2020)
2010 – 2019
- 2015
- [i5]Radford M. Neal:
Representing numeric data in 32 bits while preserving 64-bit precision. CoRR abs/1504.02914 (2015) - [i4]Radford M. Neal:
Fast exact summation using small and large superaccumulators. CoRR abs/1505.05571 (2015) - 2014
- [j16]Babak Shahbaba
, Shiwei Lan, Wesley O. Johnson, Radford M. Neal:
Split Hamiltonian Monte Carlo. Stat. Comput. 24(3): 339-349 (2014) - 2013
- [i3]Michael Harvey, Radford M. Neal:
Inference for Belief Networks Using Coupling From the Past. CoRR abs/1301.3861 (2013) - 2012
- [i2]Chunyi Wang, Radford M. Neal:
Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals. CoRR abs/1212.6246 (2012) - 2011
- [i1]Radford M. Neal:
On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note). CoRR abs/1106.0237 (2011)
2000 – 2009
- 2009
- [j15]Babak Shahbaba, Radford M. Neal:
Nonlinear Models Using Dirichlet Process Mixtures. J. Mach. Learn. Res. 10: 1829-1850 (2009) - 2007
- [j14]Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Peter Wong, Andrew Emili:
Difference detection in LC-MS data for protein biomarker discovery. Bioinform. 23(2): 198-204 (2007) - [j13]Radford M. Neal:
Pattern Recognition and Machine Learning. Technometrics 49(3): 366 (2007) - 2006
- [j12]Babak Shahbaba, Radford M. Neal:
Gene function classification using Bayesian models with hierarchy-based priors. BMC Bioinform. 7: 448 (2006) - [c8]Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Rachel Puckrin, Sean Cutler:
Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure. NIPS 2006: 905-912 - [c7]Edward Meeds, Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis:
Modeling Dyadic Data with Binary Latent Factors. NIPS 2006: 977-984 - [p3]Radford M. Neal, Jianguo Zhang:
High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees. Feature Extraction 2006: 265-296 - 2005
- [c6]Radford M. Neal:
Classification with Bayesian Neural Networks. MLCW 2005: 28-32 - 2004
- [c5]Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Andrew Emili:
Multiple Alignment of Continuous Time Series. NIPS 2004: 817-824 - 2003
- [c4]Radford M. Neal, Matthew J. Beal, Sam T. Roweis:
Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models. NIPS 2003: 401-408 - 2001
- [j11]Radford M. Neal:
Annealed importance sampling. Stat. Comput. 11(2): 125-139 (2001) - 2000
- [j10]Radford M. Neal:
On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note). J. Artif. Intell. Res. 12: 87-91 (2000) - [c3]Michael Harvey, Radford M. Neal:
Inference for Belief Networks Using Coupling From the Past. UAI 2000: 256-263
1990 – 1999
- 1998
- [j9]Alistair Moffat, Radford M. Neal, Ian H. Witten:
Arithmetic Coding Revisited. ACM Trans. Inf. Syst. 16(3): 256-294 (1998) - [p2]Radford M. Neal:
Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation. Learning in Graphical Models 1998: 205-228 - [p1]Radford M. Neal, Geoffrey E. Hinton:
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants. Learning in Graphical Models 1998: 355-368 - 1997
- [j8]Radford M. Neal, Peter Dayan:
Factor Analysis Using Delta-Rule Wake-Sleep Learning. Neural Comput. 9(8): 1781-1803 (1997) - 1996
- [j7]Radford M. Neal:
Sampling from multimodal distributions using tempered transitions. Stat. Comput. 6(4): 353-366 (1996) - 1995
- [b1]Radford M. Neal:
Bayesian learning for neural networks. University of Toronto, Canada, 1995 - [j6]Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel:
The Helmholtz machine. Neural Comput. 7(5): 889-904 (1995) - [c2]Alistair Moffat, Radford M. Neal, Ian H. Witten:
Arithmetic Coding Revisited. Data Compression Conference 1995: 202-211 - 1993
- [j5]Radford M. Neal:
Comments on 'A theoretical analysis of Monte Carlo algorithms for the simulation of Gibbs random field images'. IEEE Trans. Inf. Theory 39(1): 310-311 (1993) - 1992
- [j4]Radford M. Neal:
Connectionist Learning of Belief Networks. Artif. Intell. 56(1): 71-113 (1992) - [j3]Radford M. Neal:
Asymmetric Parallel Boltzmann Machines are Belief Networks. Neural Comput. 4(6): 832-834 (1992) - [c1]Radford M. Neal:
Bayesian Learning via Stochastic Dynamics. NIPS 1992: 475-482
1980 – 1989
- 1987
- [j2]Ian H. Witten, Radford M. Neal, John G. Cleary:
Arithmetic Coding for Data Compression. Commun. ACM 30(6): 520-540 (1987) - 1983
- [j1]Ian H. Witten, Graham M. Birtwistle, John G. Cleary, David R. Hill, Danny Levinson, Greg Lomow, Radford M. Neal, Murray Peterson, Brian W. Unger, Brian Wyvill:
Jade: A Distributed Software Prototyping Environment. ACM SIGOPS Oper. Syst. Rev. 17(3): 10-23 (1983)
Coauthor Index
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