arXiv:2607. 20153v1 Announce Type: new Abstract: We study Gaussian averaging as a smooth surrogate for quantized neural models.
arXiv:2505. 18113v2 Announce Type: replace Abstract: Training quantized neural networks requires addressing the non-differentiable and discrete nature of the underlying optimization problem.
The paper introduces Q-MINO, a Quantization-Aware Minimal-Norm Optimizer designed to improve training of ultra-low-bit neural networks. Q-MINO uses a temporal bundle method that incorporates gradient consensus, state-drift regularization, and an alignment constraint to produce stabilized, minimum-norm update directions. The authors solve the resulting constrained subproblem with a warm-started Frank–Wolfe procedure and provide theoretical convergence guarantees via a stochastic Lyapunov Kurdyka–Łojasiewicz framework, along with numerical experiments demonstrating its effectiveness across various quantization levels.
arXiv:2609.06430v1 Announce Type: new Abstract: We study the identity straight-through estimator (STE) for training a two-layer binary-activation network with hinge loss from the perspective of Stati...
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:2607. 03860v1 Announce Type: new Abstract: The Strong Lottery Ticket Hypothesis (SLTH) asserts that sufficiently overparameterized, randomly initialized neural networks contain sparse subnetworks that, even without any training, can match the performance of a small trained network on a given dataset.
arXiv:2606. 05599v1 Announce Type: new Abstract: This paper establishes a theoretical framework for the uniform convergence of smoothly activated deep neural network (DNN) estimators.
Layerwise Error Attribution for Fast and Robust Mixed-Precision Post-Training Quantization proposes a new allocation method that assigns bits to layers under a global memory budget using a small calibration set. The method derives a layerwise probabilistic analysis separating propagated error from local perturbations, enabling a simple, solver‑free allocation algorithm that is robust to corrupted calibration data. Experiments on DRUNet denoising tasks and quantized diffusion models show significant speed‑ups (up to 2,570×) and improved performance, with PSNR gains of up to 7.5 dB under corrupted calibration.
arXiv:2603. 10562v2 Announce Type: replace-cross Abstract: Monotone operator equilibrium networks are implicit-layer models whose output is the unique equilibrium of a monotone operator, guaranteeing existence, uniqueness, and convergence.
arXiv:2608. 03069v1 Announce Type: new Abstract: Deep Q-Networks (DQNs) learn value functions through bootstrapped temporal-difference updates, where future returns are approximated using a greedy maximization over next-state action values.
arXiv:2510.08999v2 Announce Type: replace-cross Abstract: Compressing large-scale neural networks is essential for deploying models on resource-constrained devices. Most existing methods adopt weight...
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:2607. 05000v1 Announce Type: cross Abstract: Canonical quantization provides a systematic procedure for constructing quantum models from classical Hamiltonians.