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 Machine Learning
Sep 22
Conformalized Quantile Regression and Minimax Limits of Fixed-Score Calibration under Known Covariate Shift
arXiv:2609.24929v1 Announce Type: cross Abstract: In this paper, we study nonasymptotic $L^p$ error bounds for interval length and conditional coverage in split conformalized quantile regression (CQR...
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 Machine Learning
Sep 18
Quantum Graph Convolutional Networks: Implementation and Trainability Analysis
The paper implements two quantum graph neural network architectures—Simplified Graph Convolution (SGC) and Linear Graph Convolution (LGC)—and evaluates them on benchmark graph datasets for semi‑supervised learning using quantum simulation. It compares their predictive performance and optimization behavior to classical baselines, finding that the quantum models achieve competitive results with fewer parameters. Additionally, the study provides a cost‑gradient analysis to identify trainable tasks and a classical simulability investigation to determine regimes where the circuits remain robust during training.
arXiv Machine Learning
Sep 22
Inductive Graph Representation Learning with Quantum Graph Neural Networks
arXiv:2503.24111v4 Announce Type: replace-cross Abstract: Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classica...
arXiv AI
Sep 2
Towards Provable and Scalable Training of Quantized Neural Networks with Ising Optimization
The paper presents a Quadratic Constrained Binary Optimization (QCBO) framework that provides provable guarantees for training quantized neural networks. It characterizes the topology of zero‑loss level sets, compiles finite‑depth architectures into bounded QCBOs, and introduces a sample‑wise Decomposed Lower‑Bound Optimization (DLBO) to scale Ising‑based optimization. Experiments on a coherent Ising machine show high accuracy on binary Fashion‑MNIST at 1.1‑bit precision and validate the approach on multi‑class datasets.
arXiv AI
Aug 13
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 Statistics ML
Aug 25
GeoQ: Geometry-Aware Conditional Quantile Error Estimation for Scientific Surrogate Models
arXiv:2608.21652v1 Announce Type: cross Abstract: Neural-network surrogate models are increasingly used to accelerate scientific simulations, but their deployment in extrapolative and autoregressive...