arXiv:2602.17493v2 Announce Type: replace-cross
Abstract: We develop a method for training neural networks on Boolean data in which the values at all nodes are strictly $\pm 1$, and the resulting mod...
By Veit Elser, Manish Krishan Lal
arXiv:2607. 09710v1 Announce Type: new Abstract: Tabular classification is often governed by local, condition-triggered rules rather than smooth global patterns.
By Tian Li, Lucy Robinson, Varun Ojha, Huizhi Liang
arXiv:2606. 13803v1 Announce Type: new Abstract: Enforcing functional inequality constraints such as monotonicity and convexity in neural networks is a fundamental challenge in many industrial and scientific applications.
By Ruben Wiedemann, Antoine Jacquier, Lukas Gonon
arXiv:2605.24437v2 Announce Type: replace
Abstract: We present a novel framework for embedding hard constraint satisfaction into neural network (NN) architectures, specifically feedforward neural net...
By Yang Zhao, Jungeun Lee, Jeong hwan Jeon, Sze Zheng Yong
arXiv:2603. 07606v2 Announce Type: replace Abstract: Interpretable machine learning is essential in high-stakes domains where decision-making requires accountability, transparency, and trust.
By Hans Farrell Soegeng, Sarthak Ketanbhai Modi, Thomas Peyrin
HUANet is a deep neural network architecture that unrolls the Alternating Direction Method of Multipliers (ADMM) into a trainable model for accelerating parametric constrained convex optimization. It embeds a hard‑constrained neural network in each ADMM iteration, using a differentiable correction stage to enforce affine equalities of the primal subproblem. The method also incorporates first‑order optimality conditions into a self‑supervised training loss, and numerical experiments on benchmark problems and a control application demonstrate its effectiveness in speeding up constrained convex optimization.
By Trinh Tran, Binh Nguyen, Truong X. Nghiem