NDProp learns decision heuristics to compute stable models for ASP and shows improved accuracy and scalability in neuro-symbolic benchmarks compared to solver-dependent approaches.
Anonymity is not privacy: Technical perspective,
6 Pith papers cite this work, alongside 924 external citations. Polarity classification is still indexing.
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A cycle-counting-ratio estimator for the β-model achieves minimax-optimal MSE and consistency under the weak conditions θ_max→0 and θ_t‖θ‖₁→∞, even at network densities near log n/n.
DNS over CoAP with packet length equalization, block-wise transfer, header and payload compression reduces DNS identification accuracy to 77-86% in constrained IoT scenarios, outperforming DNS over HTTPS.
ASPEn integrates ASP stable-model semantics with energy-based models for joint discrete-continuous optimisation and end-to-end training on visual reasoning and multi-object tracking.
An ASP-based implementation of CARCASS abstractions is created and evaluated for RL on two domains.
Norms (obligation, permission, prohibition) are encoded as preemptible global constraints in ASP, and the paper claims this resolves classic deontic paradoxes including Chisholm's contrary-to-duty paradox.
citing papers explorer
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Neural Decision-Propagation for Answer Set Programming
NDProp learns decision heuristics to compute stable models for ASP and shows improved accuracy and scalability in neuro-symbolic benchmarks compared to solver-dependent approaches.
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Subgraph counting estimation for the $\beta$-model in sparse networks
A cycle-counting-ratio estimator for the β-model achieves minimax-optimal MSE and consistency under the weak conditions θ_max→0 and θ_t‖θ‖₁→∞, even at network densities near log n/n.
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Secrets Best Not Shared: DNS Privacy Enhancements for the Constrained IoT
DNS over CoAP with packet length equalization, block-wise transfer, header and payload compression reduces DNS identification accuracy to 77-86% in constrained IoT scenarios, outperforming DNS over HTTPS.
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Answer Set Programming Energised! End-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models
ASPEn integrates ASP stable-model semantics with energy-based models for joint discrete-continuous optimisation and end-to-end training on visual reasoning and multi-object tracking.
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Answer-Set-Programming-based Abstractions for Reinforcement Learning
An ASP-based implementation of CARCASS abstractions is created and evaluated for RL on two domains.
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Modeling Deontic Modal Logic in ASP
Norms (obligation, permission, prohibition) are encoded as preemptible global constraints in ASP, and the paper claims this resolves classic deontic paradoxes including Chisholm's contrary-to-duty paradox.