arXiv Machine Learning
Jun 8
MIST: Mutual Information Estimation Via Supervised Training
arXiv:2511. 18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators.
Quantile-Free Uncertainty Quantification in Graph Neural Networks
arXiv:2605. 04847v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) in graph neural networks (GNNs) is crucial in high-stakes domains but remains a significant challenge.
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arXiv Machine Learning
Jun 15
Generalizing GNNs with Tokenized Mixture of Experts
arXiv:2602. 09258v2 Announce Type: replace Abstract: Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize under distribution shifts, and remain stable to perturbations.
arXiv AI
Jul 15
Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty
arXiv:2607. 12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs.
arXiv AI
Jun 8
Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training
arXiv:2605. 25054v2 Announce Type: replace-cross Abstract: Deploying deep neural networks on resource-constrained 6G edge devices demands aggressive compression with minimal accuracy loss.
arXiv Machine Learning
Aug 10
MiCoPro: End-to-End Mixed Precision HW/SW Co-design with HW-aware Proxy Model
arXiv:2608. 06916v1 Announce Type: new Abstract: Quantized Neural Networks~(QNN) with low-bitwidth data have proven promising in efficient storage and computation on edge devices.
arXiv AI
6d ago
Quantization-Aware Neuromorphic Architecture for Skin Lesion Classification on Resource-Constrained Devices
arXiv:2507. 15958v5 Announce Type: replace-cross Abstract: On-device skin lesion analysis is constrained by the compute and energy cost of conventional CNN inference and by the need for lightweight calibration under clinical data shift.
arXiv Machine Learning
Jul 20
Conformal Graph Prediction with Z-Gromov-Wasserstein Distances
arXiv:2603. 02460v5 Announce Type: replace-cross Abstract: Supervised graph prediction addresses regression problems where the outputs are structured graphs.
arXiv Machine Learning
Jul 7
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
arXiv:2507. 08150v4 Announce Type: replace-cross Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling.
arXiv Machine Learning
1d ago
Prediction Inference of Time Series with Standard ReLU Deep Neural Networks
arXiv:2608. 15362v1 Announce Type: cross Abstract: We propose a methodology based on the standard ReLU Deep Neural Networks (DNN) to make predictions and quantify their uncertainty.
arXiv AI
Jun 4
Model-Preserving Adaptive Rounding
arXiv:2505. 22988v3 Announce Type: replace-cross Abstract: The goal of quantization is to produce a compressed model whose output distribution is as close to the original model's as possible.
arXiv Machine Learning
Jun 10
JGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks
arXiv:2606. 09964v1 Announce Type: cross Abstract: The NISQ era places stringent constraints on quantum computation, where noise and decoherence fundamentally limit performance.
arXiv Machine Learning
Jun 25
Efficient Analytic Uncertainty Quantification for Multi-Modal Regression
arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.