arXiv Machine Learning
Neural Petri flows for chemical reactions
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arXiv Machine Learning
Oct 5
Hyperparameter selection for equation learning with biologically-informed neural networks
Biologically-informed neural networks (BINNs) are a subclass of physics-informed neural networks tailored for learning partial differential equations in biological systems, where equations are nonlinear and data are sparse. The paper introduces a diagnostic workflow for selecting hyperparameters—network capacity, training epochs, and term stability—without requiring known ground-truth equations. By applying this workflow to synthetic systems, the authors show that validation loss tracks true error and provide practical guidelines and starting values for BINN hyperparameter tuning.
arXiv Machine Learning
Jul 1
ReactionAtlas: Ab origine exploration of chemical reaction networks with machine learning
arXiv:2606. 30778v1 Announce Type: new Abstract: Mapping a chemical reaction network, the graph of minima and transition states (TS) and the elementary reactions connecting them, is the natural language of chemistry, from catalysis to combustion to the origin of life.
arXiv AI
Oct 28
KPPax: preprocess differentiable kinetic ODE for machine learning in atmospheric chemistry
KPPax is a new kinetic preprocessor that converts atmospheric chemistry mechanisms written in the Kinetic PreProcessor (KPP) language into differentiable, just‑in‑time compiled, vectorisable code in JAX. It evaluates rate laws in log space to maintain differentiability and integrates stiff systems using implicit solvers with forward‑ and reverse‑mode adjoints from the diffrax library, enabling exact sensitivities without hand‑derived adjoint code. Demonstrations with the POLLU benchmark show that KPPax can predict which rate coefficients are identifiable and improve neural‑network corrections of emission inventories, reducing errors by up to a factor of five.
arXiv AI
Aug 19
Neuro-symbolic learning over OWL 2 DL via consequence-based compilation to differentiable circuits
Baobab compiles an OWL 2 DL (ΣROIQ) ontology with a finite ABox into a Sentential Decision Diagram (SDD), saturating a propositional core and instantiating remaining DL features over the active domain. The resulting evidence‑conditioned weighted model count trains a perception network to recognize real images under partial ABox supervision, enabling a CNN to recover latent ontology concepts that an independent perception would miss. When supervision allows multiple ontology‑consistent completions, Baobab’s mixture indexed by query justifications represents the calibrated posterior, achieving Bayes‑optimal performance on a real‑image MNIST task where single‑WMC and learned mixtures fail, thereby characterizing and mitigating reasoning shortcuts in a non‑Horn description logic.
arXiv Machine Learning
Oct 22
Fragment-Level Graph Encoding for Interpretable Molecular Property Prediction
The paper introduces a fragment‑level graph encoding for molecular property prediction, using BRICS decomposition to represent molecules as graphs of synthetically meaningful fragments. This change of input representation allows any message‑passing GNN and post‑hoc attribution method to produce explanations at the functional‑group level, matching atom‑level accuracy on smaller graphs. Experiments across multiple architectures, tasks, and explainers show that fragment‑level encoding recovers known pharmacophores more reliably than post‑hoc aggregation of atom‑level explanations.
arXiv Machine Learning
Jul 1
Diffusing Blame: Task-Dependent Credit Assignment in Biologically Plausible Dual-Stream Networks
arXiv:2606. 31700v1 Announce Type: new Abstract: Biological neural circuits obey Dale's principle: each neuron's synapses are uniformly excitatory or inhibitory.