MELT is the first behavioral trace dataset for high-risk memecoin launch detection on Solana, providing 122 features, risk annotations, and ML benchmarks that reduce investment loss when used for selection.
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8 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
Variability modeling from software engineering enables systematic sampling, measurement, and prediction of LLM inference configurations for energy, latency, and accuracy trade-offs.
Post-processing fairness methods (ROC and EqOdds) provide the most stable fairness-utility trade-offs when training classifiers on DP synthetic tabular data, outperforming pre- and in-processing interventions.
Using a 61-feature readability model, LLM code matches or slightly exceeds human code in readability score, shows distinct issue patterns, and prompt design has limited influence.
A privacy-aware semantic encoding framework for GPS data in mobile stress recognition maintains performance comparable to non-private baselines while improving privacy by 2-3 times on the GeoLife dataset via LOSO validation.
Empirical study of real NISQ order-finding data identifies dominant verified mass fraction as the strongest predictor of whether standard post-processing recovers the true order.
SSR uses static random filters and iterative competitive sparse mechanisms to explicitly enforce sparsity in recommendation models, outperforming dense baselines on public and billion-scale industrial datasets.
The sum of verifier warnings adds no useful predictive power for code comprehensibility beyond syntactic and developer features.
citing papers explorer
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MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection
MELT is the first behavioral trace dataset for high-risk memecoin launch detection on Solana, providing 122 features, risk annotations, and ML benchmarks that reduce investment loss when used for selection.
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Pimp My LLM: Leveraging Variability Modeling to Tune Inference Hyperparameters
Variability modeling from software engineering enables systematic sampling, measurement, and prediction of LLM inference configurations for energy, latency, and accuracy trade-offs.
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Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data
Post-processing fairness methods (ROC and EqOdds) provide the most stable fairness-utility trade-offs when training classifiers on DP synthetic tabular data, outperforming pre- and in-processing interventions.
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Characterizing Readability Issue Patterns and the Role of Prompt Design in LLM-Generated Code
Using a 61-feature readability model, LLM code matches or slightly exceeds human code in readability score, shows distinct issue patterns, and prompt design has limited influence.
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From Coordinates to Context: An LLM-Bootstrapped Semantic Encoding Framework for Privacy-Preserving Mobile Sensing Stress Recognition
A privacy-aware semantic encoding framework for GPS data in mobile stress recognition maintains performance comparable to non-private baselines while improving privacy by 2-3 times on the GeoLife dataset via LOSO validation.
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When Noisy Quantum Order Finding Remains Recoverable for Shor's Algorithm
Empirical study of real NISQ order-finding data identifies dominant verified mass fraction as the strongest predictor of whether standard post-processing recovers the true order.
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Beyond Dense Connectivity: Explicit Sparsity for Scalable Recommendation
SSR uses static random filters and iterative competitive sparse mechanisms to explicitly enforce sparsity in recommendation models, outperforming dense baselines on public and billion-scale industrial datasets.
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Verifier Warnings Do Not Improve Comprehensibility Prediction
The sum of verifier warnings adds no useful predictive power for code comprehensibility beyond syntactic and developer features.