A large benchmark finds traditional imputation methods for scRNA-seq data generally outperform deep learning ones, but numerical recovery does not reliably improve biological downstream analyses and no method wins across all settings.
ISBN 9781450342322
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 7representative citing papers
Users adapt existing workflow patterns to create custom features in an AI email system via conversation, turning the inbox into a user-shaped flexible data layer while managing risks like mis-specified behavior through ongoing oversight.
IDP-DSN separates positive and negative dynamics in signed networks using dedicated memories and disentangles static and dynamic features to boost inductive edge prediction performance.
Spline encodings for numerical features show task-dependent performance in tabular deep learning, with piecewise-linear encoding robust for classification and variable results for regression depending on spline family, knot strategy, and backbone.
SA-HGNN with contrastive learning improves power outage prediction by modeling spatial effects of extreme weather on infrastructure across multiple utility territories.
Greedy linear models without exploration consistently achieve top-tier performance in over 90% of offline dataset evaluations for linear bandit recommenders, with hyperparameter tuning favoring minimal exploration and exposing biases in these protocols.
A hybrid deep learning plus classical ML pipeline for waste image classification reaches up to 100% accuracy on TrashNet and a corrected household dataset while cutting feature dimensionality by over 95%.
citing papers explorer
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A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data
A large benchmark finds traditional imputation methods for scRNA-seq data generally outperform deep learning ones, but numerical recovery does not reliably improve biological downstream analyses and no method wins across all settings.
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Conversational Customization of Productivity Systems: A Design Probe of Malleable AI Interfaces
Users adapt existing workflow patterns to create custom features in an AI email system via conversation, turning the inbox into a user-shaped flexible data layer while managing risks like mis-specified behavior through ongoing oversight.
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Inductive Dual-Polarity Modeling via Static-Dynamic Disentanglement for Dynamic Signed Networks
IDP-DSN separates positive and negative dynamics in signed networks using dedicated memories and disentangles static and dynamic features to boost inductive edge prediction performance.
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From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning
Spline encodings for numerical features show task-dependent performance in tabular deep learning, with piecewise-linear encoding robust for classification and variable results for regression depending on spline family, knot strategy, and backbone.
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Empowering Power Outage Prediction with Spatially Aware Hybrid Graph Neural Networks and Contrastive Learning
SA-HGNN with contrastive learning improves power outage prediction by modeling spatial effects of extreme weather on infrastructure across multiple utility territories.
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Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation
Greedy linear models without exploration consistently achieve top-tier performance in over 90% of offline dataset evaluations for linear bandit recommenders, with hyperparameter tuning favoring minimal exploration and exposing biases in these protocols.
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Towards Accurate and Efficient Waste Image Classification: A Hybrid Deep Learning and Machine Learning Approach
A hybrid deep learning plus classical ML pipeline for waste image classification reaches up to 100% accuracy on TrashNet and a corrected household dataset while cutting feature dimensionality by over 95%.