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2020 – today
- 2022
[j11]Dino Oglic
, Zoran Cvetkovic, Peter Sollich, Steve Renals, Bin Yu:
Towards Robust Waveform-Based Acoustic Models. IEEE ACM Trans. Audio Speech Lang. Process. 30: 1977-1992 (2022)- 2021
- [j10]Dino Oglic, Zoran Cvetkovic, Peter Sollich:
Learning Waveform-Based Acoustic Models Using Deep Variational Convolutional Neural Networks. IEEE ACM Trans. Audio Speech Lang. Process. 29: 2850-2863 (2021) - [i10]Dino Oglic, Zoran Cvetkovic, Peter Sollich, Steve Renals, Bin Yu:
Towards Robust Waveform-Based Acoustic Models. CoRR abs/2110.08634 (2021) - 2020
- [i9]Edgar Herrera-Delgado, James Briscoe, Peter Sollich:
Nonlinear memory functions capture and explain dynamical behaviours. CoRR abs/2005.04751 (2020)
2010 – 2019
- 2019
- [i8]Dino Oglic, Zoran Cvetkovic, Peter Sollich:
Parzen Filters for Spectral Decomposition of Signals. CoRR abs/1906.09526 (2019) - 2018
- [j9]Edgar Herrera-Delgado, Ruben Perez-Carrasco, James Briscoe, Peter Sollich:
Memory functions reveal structural properties of gene regulatory networks. PLoS Comput. Biol. 14(2) (2018) - 2017
- [i7]Adriano Barra, Giuseppe Genovese, Daniele Tantari, Peter Sollich:
Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield Networks with Arbitrary Priors. CoRR abs/1702.05882 (2017) - [i6]Robin Nicole, Peter Sollich:
Dynamical selection of Nash equilibria using Experience Weighted Attraction Learning: emergence of heterogeneous mixed equilibria. CoRR abs/1706.09763 (2017) - 2016
- [i5]Adriano Barra, Giuseppe Genovese, Peter Sollich, Daniele Tantari:
Phase transitions in Restricted Boltzmann Machines with generic priors. CoRR abs/1612.03132 (2016) - 2014
- [i4]Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich, Matthew Ager:
A Subband-Based SVM Front-End for Robust ASR. CoRR abs/1401.3322 (2014) - 2013
- [j8]Matthew Urry, Peter Sollich:
Random walk kernels and learning curves for Gaussian process regression on random graphs. J. Mach. Learn. Res. 14(1): 1801-1835 (2013) - [c30]Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich:
Effects of domain-specific SVM kernel design on the robustness of automatic speech recognition. DSP 2013: 1-7 - [i3]Matthew Ager, Zoran Cvetkovic, Peter Sollich:
Phoneme Classification in High-Dimensional Linear Feature Domains. CoRR abs/1312.6849 (2013) - 2012
- [c29]Peter Sollich, Simon R. F. Ashton:
Learning curves for multi-task Gaussian process regression. NIPS 2012: 1790-1798 - [i2]Simon R. F. Ashton, Peter Sollich:
Learning curves for multi-task Gaussian process regression. CoRR abs/1211.0439 (2012) - [i1]Matthew Urry, Peter Sollich:
Random walk kernels and learning curves for Gaussian process regression on random graphs. CoRR abs/1211.1328 (2012) - 2011
- [j7]Jibran Yousafzai, Peter Sollich, Zoran Cvetkovic, Bin Yu:
Combined Features and Kernel Design for Noise Robust Phoneme Classification Using Support Vector Machines. IEEE Trans. Speech Audio Process. 19(5): 1396-1407 (2011) - [c28]Matthew Ager, Zoran Cvetkovic, Peter Sollich:
Combined waveform-cepstral representation for robust speech recognition. ISIT 2011: 864-868 - 2010
- [c27]Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich:
Towards robust phoneme classification with hybrid features. ISIT 2010: 1643-1647 - [c26]Matthew Ager, Zoran Cvetkovic, Peter Sollich:
High-dimensional linear representations for robust speech recognition. ITA 2010: 75-79 - [c25]Matthew Urry, Peter Sollich:
Exact learning curves for Gaussian process regression on large random graphs. NIPS 2010: 2316-2324 - [c24]Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich:
Subband acoustic waveform front-end for robust speech recognition using support vector machines. SLT 2010: 253-258
2000 – 2009
- 2009
- [c23]Matthew Ager, Zoran Cvetkovic, Peter Sollich:
Robust phoneme classification: Exploiting the adaptability of acoustic waveform models. EUSIPCO 2009: 530-534 - [c22]Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich:
Custom-designed SVM kernels for improved robustness of phoneme classification. EUSIPCO 2009: 1765-1769 - [c21]Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich:
Tuning support vector machines for robust phoneme classification with acoustic waveforms. INTERSPEECH 2009: 2391-2394 - [c20]Peter Sollich, Matthew Urry, Camille Coti:
Kernels and learning curves for Gaussian process regression on random graphs. NIPS 2009: 1723-1731 - 2008
- [c19]Matthew Ager, Zoran Cvetkovic, Peter Sollich, Bin Yu:
Towards robust phoneme classification: Augmentation of PLP models with acoustic waveforms. EUSIPCO 2008: 1-5 - [c18]Jibran Yousafzai, Zoran Cvetkovic, Peter Sollich, Bin Yu:
Combined PLP - Acoustic waveform classification for robust phoneme recognition using support vector machines. EUSIPCO 2008: 1-5 - 2007
- [c17]David Barber, Peter Sollich:
Stable Belief Propagation in Gaussian Dags. ICASSP (2) 2007: 409-412 - [c16]Nicholas Betteridge, Zoran Cvetkovic, Peter Sollich:
Phoneme Classification in Frequency Subbands using Ensemble Methods. DSP 2007: 511-514 - 2006
- [c15]Lena Khoo, Zoran Cvetkovic, Peter Sollich:
Robustness of phoneme classification in different representation spaces. EUSIPCO 2006: 1-5 - 2005
- [j6]Carl Gold, Alex Holub, Peter Sollich:
Bayesian approach to feature selection and parameter tuning for support vector machine classifiers. Neural Networks 18(5-6): 693-701 (2005) - 2004
- [c14]Peter Sollich:
Can Gaussian Process Regression Be Made Robust Against Model Mismatch? Deterministic and Statistical Methods in Machine Learning 2004: 199-210 - [c13]Peter Sollich, Christopher K. I. Williams:
Understanding Gaussian Process Regression Using the Equivalent Kernel. Deterministic and Statistical Methods in Machine Learning 2004: 211-228 - [c12]Peter Sollich, Christopher K. I. Williams:
Using the Equivalent Kernel to Understand Gaussian Process Regression. NIPS 2004: 1313-1320 - 2003
- [j5]Carl Gold, Peter Sollich:
Model selection for support vector machine classification. Neurocomputing 55(1-2): 221-249 (2003) - 2002
- [j4]Peter Sollich:
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities. Mach. Learn. 46(1-3): 21-52 (2002) - [j3]Peter Sollich, Anason S. Halees:
Learning Curves for Gaussian Process Regression: Approximations and Bounds. Neural Comput. 14(6): 1393-1428 (2002) - 2001
- [c11]Peter Sollich:
Gaussian Process Regression with Mismatched Models. NIPS 2001: 519-526
1990 – 1999
- 1999
- [c10]Peter Sollich:
Probabilistic Methods for Support Vector Machines. NIPS 1999: 349-355 - [c9]David Barber, Peter Sollich:
Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks. NIPS 1999: 393-399 - 1998
- [j2]Peter Sollich, David Barber:
Online Learning from Finite Training Sets and Robustness to Input Bias. Neural Comput. 10(8): 2201-2217 (1998) - [c8]H. C. Rae, Peter Sollich, Anthony C. C. Coolen:
On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General Theories. NIPS 1998: 316-322 - [c7]Peter Sollich:
Learning Curves for Gaussian Processes. NIPS 1998: 344-350 - 1997
- [c6]Peter Sollich, David Barber:
On-line Learning from Finite Training Sets in Nonlinear Networks. NIPS 1997: 357-363 - 1996
- [c5]Peter Sollich, David Barber:
Online Learning from Finite Training Sets: An Analytical Case Study. NIPS 1996: 274-280 - 1995
- [b1]Peter Sollich:
Asking intelligent questions : the statistical mechanics of query learning. University of Edinburgh, UK, 1995 - [j1]David Barber, David Saad, Peter Sollich:
Test Error Fluctuations in Finite Linear Perceptrons. Neural Comput. 7(4): 809-821 (1995) - [c4]Peter Sollich:
Minimum entropy queries for linear students learning nonlinear rules. ESANN 1995 - [c3]Peter Sollich, Anders Krogh:
Learning with ensembles: How overfitting can be useful. NIPS 1995: 190-196 - 1994
- [c2]Peter Sollich:
Learning in large linear perceptrons and why the thermodynamic limit is relevant to the real world. NIPS 1994: 207-214 - [c1]Peter Sollich, David Saad:
Learning from queries for maximum information gain in imperfectly learnable problems. NIPS 1994: 287-294
Coauthor Index
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