430 research outputs found
Enhancing Evolutionary Conversion Rate Optimization via Multi-armed Bandit Algorithms
Conversion rate optimization means designing web interfaces such that more
visitors perform a desired action (such as register or purchase) on the site.
One promising approach, implemented in Sentient Ascend, is to optimize the
design using evolutionary algorithms, evaluating each candidate design online
with actual visitors. Because such evaluations are costly and noisy, several
challenges emerge: How can available visitor traffic be used most efficiently?
How can good solutions be identified most reliably? How can a high conversion
rate be maintained during optimization? This paper proposes a new technique to
address these issues. Traffic is allocated to candidate solutions using a
multi-armed bandit algorithm, using more traffic on those evaluations that are
most useful. In a best-arm identification mode, the best candidate can be
identified reliably at the end of evolution, and in a campaign mode, the
overall conversion rate can be optimized throughout the entire evolution
process. Multi-armed bandit algorithms thus improve performance and reliability
of machine discovery in noisy real-world environments.Comment: The Thirty-First Innovative Applications of Artificial Intelligence
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SARDSRN: A NEURAL NETWORK SHIFT-REDUCE PARSER
Simple Recurrent Networks (SRNs) have been widely used in natural language tasks. SARDSRN extends the SRN by
explicitly representing the input sequence in a SARDNET self-organizing map. The distributed SRN component leads to good generalization and robust cognitive properties, whereas the SARDNET map provides exact representations of the sentence constituents. This combination allows SARDSRN to learn to parse sentences with more complicated structure than can the SRN alone, and suggests that the approach could scale up to realistic natural language
Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex
RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the long-term development of the map are shown to result in tilt aftereffects over short time scales in the adult. The model permits simultaneous observation of large numbers of neurons and connections, making it possible to relate high-level phenomena to low-level events, which is difficult to do experimentally. The results give detailed computational support for the long-standing conjecture that the direct tilt aftereffect arises from adaptive lateral interactions between feature detectors. They also make a new prediction that the indirect effect results from the normalization of synaptic efficacies during this process. The model thus provides a unified computational explanation of self-organization and both the direct and indirect tilt aftereffect in the primary visual cortex
Hebbian Learning and Temporary Storage in the Convergence-Zone Model of Episodic Memory
The Convergence-Zone model shows how sparse, random memory patterns can lead to one-shot storage and high capacity in the hippocampal component of the episodic memory system. This paper presents a biologically more realistic version of the model, with continuously-weighted connections and storage through Hebbian learning and normalization. In contrast to the gradual weight adaptation in many neural network models, episodic memory turns out to require high learning rates. Normalization allows earlier patterns to be overwritten,
introducing time-dependent forgetting similar to the hippocampus
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