A. Krogh

发表

Introduction to the theory of neural computation

R. Palmer, J. Hertz, A. Krogh, 1994, The advanced book program.

Introduction to the theory of neural computation

Anders Krogh, John A. Hertz, Richard G. Palmer, 1994, The advanced book program.

Generalization in a linear perceptron in the presence of noise

J. Hertz, A. Krogh, 1992 .

A Simple Weight Decay Can Improve Generalization

Anders Krogh, John A. Hertz, J. Hertz, 1991, NIPS.

Learning with noise in a linear perceptron

A. Krogh, 1992 .

Statistical mechanics of ensemble learning

A. Krogh, Peter Sollich, 1997 .

Neural Network Ensembles, Cross Validation, and Active Learning

Anders Krogh, Jesper Vedelsby, A. Krogh, 1994, NIPS.

A Cost Function for Internal Representations 733 A Cost Function for Internal Representations

A. Krogh, 1989 .

Phase transitions in simple learning

J. Hertz, A. Krogh, G. I. Thorbergsson, 1989 .

A Cost Function for Internal Representations

Anders Krogh, John A. Hertz, C. I. Thorbergsson, 1989, NIPS.

Bounds on approximate steepest descent for likelihood maximization in exponential families

Manfred K. Warmuth, Nicolò Cesa-Bianchi, Anders Krogh, 1994, IEEE Trans. Inf. Theory.

Learning with ensembles: How overfitting can be useful

Anders Krogh, Peter Sollich, A. Krogh, 1995, NIPS.

Finiteness Results for Sigmoidal "Neural" Networks

R. Palmer, A. Krogh, 1993 .

Mean-field analysis of hierarchical associative networks with 'magnetisation'

J. Hertz, A. Krogh, 1988 .

, Introduction to the Theory of Neural Computation 1

R. Palmer, J. Hertz, A. Krogh, 2007 .

Summary of Current Work on Active Learning

A. Krogh, R. Hwa, 2006 .

Hierarchical associative networks

Corinna Cortes, J. Hertz, A. Krogh, 1987 .

Subdomain Epoch % Improvement Subdomain Epoch % Improvement Substructure Generation in the 2-d Nite Element Mesh Shown in Fig. 2 Using Competitive Learning. Subdomain Epoch % Improvement Subdomain Epoch % Improvement

R. Palmer, A. Krogh, C Farhat, 1993 .

An Exercises Supplement to the Introduction To The Theory Of Neural Computation

R. Palmer, J. Hertz, A. Krogh, 2004 .

Extensions of the Hopfield Model

Anders Krogh, Richard G. Palmer, John Hertz, 2018 .

Nonlinear backpropagation: doing backpropagation without derivatives of the activation function

Torsten Lehmann, Benny Lautrup, Anders Krogh, 1997, IEEE Trans. Neural Networks.

The Deep Generative Decoder: MAP estimation of representations improves modelling of single-cell RNA data

A. Krogh, Viktoria Schuster, 2021, Bioinformatics.