arXiv:2604. 04087v2 Announce Type: replace Abstract: We introduce ArrowFlow, a machine learning architecture that operates entirely in the space of permutations.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
BIRDNet is a neural network that mines Boolean implication relationships (BIRs) from tabular data and encodes them as a sparse, interpretable architecture where each hidden unit represents a mined rule connecting two features. The design yields a model that is at most 2/d of the weights active per layer and retains symbolic identities for each unit, allowing direct rule extraction without surrogate models. Experiments on six transcriptomic and proteomic datasets show BIRDNet achieves AUROC within 0.02 of the best dense baseline while using up to 95× fewer active parameters, and its first‑layer rules align with known biological signatures.
arXiv:2605.06240v2 Announce Type: replace-cross Abstract: Forward-Forward (FF) training lets each layer learn from a local goodness criterion. In cumulative-goodness variants, later layers can inheri...
The paper investigates four ways to grow a classifier—adding a tree level, a hidden unit, a leaf split, and a statistically significant split—under a fixed protocol for tree‑structured and constructive models. It shows that the most natural method of deepening a soft decision tree by duplicating a leaf’s class distribution leaves the gradient of new gates identically zero, preventing learning, and proposes a small random perturbation as a fix. The other three growth decisions each provide a distinct benefit: fitting a new hidden unit to residual error yields a smaller network, splitting the leaf with the largest expected error adds sparsity, and requiring statistical significance before splitting adds no value and reduces accuracy.
arXiv:2606. 17886v1 Announce Type: new Abstract: Monotonicity has been a long-running architectural inductive bias for neural networks, motivated by tabular, scientific, and economic settings where outputs are known to respond monotonically to certain inputs.
arXiv:2606. 10632v1 Announce Type: cross Abstract: Lipschitz-style individual fairness formalizes the idea that semantically similar examples should receive similar predictions, but its evaluation in multi-task learning (MTL) can be confounded by method-induced representation scales.
arXiv:2512. 22240v5 Announce Type: replace-cross Abstract: Machine learning models are primarily judged by predictive performance, especially in applied genomics, where explanations are read as biological findings.