Peter Sollich - researchr alias

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Viewing Publication 1 - 42 from 42

2022

Towards Robust Waveform-Based Acoustic ModelsDino Oglic, Zoran Cvetkovic, Peter Sollich, Steve Renals, Bin Yu. taslp, 30:1977-1992, 2022. [doi]

2021

Learning Waveform-Based Acoustic Models Using Deep Variational Convolutional Neural NetworksDino Oglic, Zoran Cvetkovic, Peter Sollich. taslp, 29:2850-2863, 2021. [doi]

2018

Memory functions reveal structural properties of gene regulatory networksEdgar Herrera-Delgado, Ruben Perez-Carrasco, James Briscoe, Peter Sollich. ploscb, 14(2), 2018. [doi]

2013

Random walk kernels and learning curves for Gaussian process regression on random graphsMatthew Urry, Peter Sollich. jmlr, 14(1):1801-1835, 2013. [doi]

Effects of domain-specific SVM kernel design on the robustness of automatic speech recognitionJibran Yousafzai, Zoran Cvetkovic, Peter Sollich. icdsp 2013: 1-7 [doi]

2012

Learning curves for multi-task Gaussian process regressionPeter Sollich, Simon R. F. Ashton. nips 2012: 1790-1798 [doi]

2011

Combined waveform-cepstral representation for robust speech recognitionMatthew Ager, Zoran Cvetkovic, Peter Sollich. isit 2011: 864-868 [doi]

Combined Features and Kernel Design for Noise Robust Phoneme Classification Using Support Vector MachinesJibran Yousafzai, Peter Sollich, Zoran Cvetkovic, B. Yu. taslp, 19(5):1396-1407, 2011. [doi]

2010

2009

2008

Towards robust phoneme classification: Augmentation of PLP models with acoustic waveformsMatthew Ager, Zoran Cvetkovic, Peter Sollich, Bin Yu. eusipco 2008: 1-5 [doi]

Combined PLP - Acoustic waveform classification for robust phoneme recognition using support vector machinesJibran Yousafzai, Zoran Cvetkovic, Peter Sollich, Bin Yu. eusipco 2008: 1-5 [doi]

2007

Stable Belief Propagation in Gaussian DagsDavid Barber, Peter Sollich. icassp 2007: 409-412 [doi]

Phoneme Classification in Frequency Subbands using Ensemble MethodsNicholas Betteridge, Zoran Cvetkovic, Peter Sollich. icdsp 2007: 511-514 [doi]

2006

Robustness of phoneme classification in different representation spacesLena Khoo, Zoran Cvetkovic, Peter Sollich. eusipco 2006: 1-5 [doi]

2005

Bayesian approach to feature selection and parameter tuning for support vector machine classifiersCarl Gold, Alex Holub, Peter Sollich. NN, 18(5-6):693-701, 2005. [doi]

2004

Using the Equivalent Kernel to Understand Gaussian Process RegressionPeter Sollich, Christopher K. I. Williams. nips 2004: 1313-1320 [doi]

Understanding Gaussian Process Regression Using the Equivalent KernelPeter Sollich, Christopher K. I. Williams. dsmml 2005: 211-228 [doi]

Can Gaussian Process Regression Be Made Robust Against Model Mismatch?Peter Sollich. dsmml 2005: 199-210 [doi]

2003

Model selection for support vector machine classificationCarl Gold, Peter Sollich. ijon, 55(1-2):221-249, 2003. [doi]

2002

Learning Curves for Gaussian Process Regression: Approximations and BoundsPeter Sollich, Anason Halees. neco, 14(6):1393-1428, 2002. [doi]

Bayesian Methods for Support Vector Machines: Evidence and Predictive Class ProbabilitiesPeter Sollich. ml, 46(1-3):21-52, 2002.

2001

Gaussian Process Regression with Mismatched ModelsPeter Sollich. nips 2001: 519-526 [doi]

1999

Gaussian Fields for Approximate Inference in Layered Sigmoid Belief NetworksDavid Barber, Peter Sollich. nips 2000: 393-399 [doi]

Probabilistic Methods for Support Vector MachinesPeter Sollich. nips 2000: 349-355 [doi]

1998

On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General TheoriesH. C. Rae, Peter Sollich, Anthony C. C. Coolen. nips 1999: 316-322 [doi]

Learning Curves for Gaussian ProcessesPeter Sollich. nips 1999: 344-350 [doi]

Online Learning from Finite Training Sets and Robustness to Input BiasPeter Sollich, David Barber. neco, 10(8):2201-2217, 1998.

1997

On-line Learning from Finite Training Sets in Nonlinear NetworksPeter Sollich, David Barber. nips 1998: 357-363

1996

Online Learning from Finite Training Sets: An Analytical Case StudyPeter Sollich, David Barber. nips 1997: 274-280 [doi]

1995

Test Error Fluctuations in Finite Linear PerceptronsDavid Barber, David Saad, Peter Sollich. neco, 7(4):809-821, 1995. [doi]

Minimum entropy queries for linear students learning nonlinear rulesPeter Sollich. esann 1995: [doi]

Asking intelligent questions : the statistical mechanics of query learningPeter Sollich. PhD thesis, University of Edinburgh, UK, 1995. [doi]

Learning with ensembles: How overfitting can be usefulPeter Sollich, Anders Krogh. nips 1996: 190-196 [doi]

1994

Learning in large linear perceptrons and why the thermodynamic limit is relevant to the real worldPeter Sollich. nips 1995: 207-214 [doi]

Learning from queries for maximum information gain in imperfectly learnable problemsPeter Sollich, David Saad. nips 1995: 287-294 [doi]

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