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20112023
most citedLearning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle

200 citations · 675 across the 45 of their papers we have counts for

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Showing 2013Show all

6 papers · 1 filter

cs.IT2013

Information Loss and Anti-Aliasing Filters in Multirate Systems

Bernhard C. Geiger, Gernot Kubin

This work investigates the information loss in a decimation system, i.e., in a downsampler preceded by an anti-aliasing filter. It is shown that, without a specific signal model in…

cs.IT2013★ 47 cited

Optimal Quantization for Distribution Synthesis

Georg Böcherer, Bernhard C. Geiger

Finite precision approximations of discrete probability distributions are considered, applicable for distribution synthesis, e.g., probabilistic shaping. Two algorithms are present…

cs.IT2013

On the Rate of Information Loss in Memoryless Systems

Bernhard C. Geiger, Gernot Kubin

In this work we present results about the rate of (relative) information loss induced by passing a real-valued, stationary stochastic process through a memoryless system. We show t…

cs.IT2013★ 1 cited

Information Measures for Deterministic Input-Output Systems

Bernhard C. Geiger, Gernot Kubin

In this work the information loss in deterministic, memoryless systems is investigated by evaluating the conditional entropy of the input random variable given the output random va…

eess.SY2013★ 18 cited

Optimal Kullback-Leibler Aggregation via Information Bottleneck

Bernhard C. Geiger, Tatjana Petrov, Gernot Kubin +1

In this paper, we present a method for reducing a regular, discrete-time Markov chain (DTMC) to another DTMC with a given, typically much smaller number of states. The cost of redu…

cs.IT2013★ 2 cited

Information-Preserving Markov Aggregation

Bernhard C. Geiger, Christoph Temmel

We present a sufficient condition for a non-injective function of a Markov chain to be a second-order Markov chain with the same entropy rate as the original chain. This permits an…