arXiv Machine Learning
PhyloGFN: Phylogenetic inference with generative flow networks
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arXiv Machine Learning
Aug 31
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
The paper introduces JSP-GFN, a Generative Flow Network that jointly infers the structure and parameters of a Bayesian Network. It sequentially generates a directed acyclic graph edge by edge and then samples the corresponding conditional probability parameters once the full structure is known. Experiments on simulated and real data show that JSP‑GFN accurately approximates the joint posterior and outperforms existing methods.
arXiv Machine Learning
Aug 17
Generative Modeling with Bayesian Sample Inference
arXiv:2502. 07580v4 Announce Type: replace Abstract: We present a novel view of diffusion-like generative modeling from the perspective of iterative Gaussian posterior inference.
arXiv Machine Learning
Aug 7
Phylogenetic Tree Inference with Tropical Axial Attention
arXiv:2605. 13894v2 Announce Type: replace-cross Abstract: In this work, we introduce a Tropical Axial Attention neural reasoning architecture that replaces vanilla softmax dot-product attention with max-plus operators, inducing a piecewise-linear structure aligned with dynamic programming formulations.
arXiv Machine Learning
Sep 24
Variational Bayesian Flow Network for Graph Generation
The paper introduces Variational Bayesian Flow Network (VBFN), a graph generation model that lifts Bayesian updates to a joint Gaussian belief family with structured precisions, enabling coupled node and edge updates in a single fusion step. By constructing sample‑agnostic sparse precisions from a representation‑induced dependency graph, VBFN avoids label leakage while enforcing node‑edge consistency. Experiments on synthetic and molecular graph datasets show that VBFN improves fidelity and diversity over baseline methods.
arXiv Machine Learning
Sep 1
Amortizing intractable inference in large language models
arXiv:2310.04363v3 Announce Type: replace Abstract: Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits t...
arXiv AI
Jul 22
Approximating SPR Distance Between Phylogenetic Trees with Graph Neural Networks
arXiv:2607. 18311v1 Announce Type: cross Abstract: Comparing phylogenetic tree topologies is essential for understanding epidemic dynamics, yet biologically meaningful distances such as the Subtree Prune and Regraft (SPR) distance are NP-hard to compute and intractable on large datasets.