arXiv Machine Learning
Jun 9
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.
Calibrating Generative Models to Feature Distributions with MMD Finetuning
arXiv:2606. 19496v1 Announce Type: new Abstract: Generative models can produce individually plausible samples while deviating substantially from a target set in the distribution of key features.
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arXiv AI
Jun 10
MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.
arXiv Machine Learning
Aug 17
Conditional Neural Optimal Transport for Predicting Cellular Phenotypes from Molecular Structure
arXiv:2608. 14293v1 Announce Type: cross Abstract: High-content microscopy enables systematic profiling of cellular responses to chemical perturbations, but the scale of the chemical space makes exhaustive phenotypic characterization experimentally infeasible.
arXiv Machine Learning
Jul 21
GeneSpeak-FP: Target and Compound Retrieval from Observed Cell-Level Perturbation Signatures
arXiv:2607. 17671v1 Announce Type: new Abstract: Large-scale single-cell perturbation atlases make it possible to ask an inverse question: given an observed transcriptional response, which annotated targets and compounds in a fixed library are most consistent with that response?
arXiv Machine Learning
Jul 1
Introduction to Stochastic Differential Equations for Generative Machine Learning: A Variational Perspective
arXiv:2606. 31576v1 Announce Type: new Abstract: The use of ordinary and stochastic differential equations has led to substantial progress in generative machine learning with applications to, for example, image, video and biomolecule generation.
arXiv Machine Learning
Jun 2
Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models
arXiv:2606. 01461v1 Announce Type: new Abstract: Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes.
arXiv Machine Learning
Aug 4
Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery
arXiv:2509. 26405v2 Announce Type: replace Abstract: We introduce InVirtuoGen, a discrete flow generative model for fragmented SMILES for de novo and fragment-constrained generation, and target-property/lead optimization of small molecules.
arXiv AI
Jun 2
Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
arXiv:2606. 01220v1 Announce Type: cross Abstract: Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD).
arXiv AI
Aug 11
Biologically Informed Representation Learning for Robust Cross-Center Generalization of MALDI-TOF Mass Spectrometry
arXiv:2608. 08182v1 Announce Type: cross Abstract: Machine learning models for MALDI-TOF mass spectrometry have shown considerable promise for clinical microbiology tasks such as microbial identification and antimicrobial resistance prediction.
arXiv AI
Aug 6
Interpreting GFlowNets for Drug Discovery: What probes can and cannot show
arXiv:2511. 19264v2 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) construct molecules through sequential decisions, but their internal policies remain opaque, limiting adoption in drug discovery, where chemists need interpretable rationales for proposed structures.
arXiv Machine Learning
1d ago
Generalization, memorization, and overfitting for diffusion models trained in the lazy high-dimensional regime
The paper investigates diffusion models trained in a lazy high‑dimensional regime, extending benign overfitting theory to generative settings. By analyzing denoising score matching in a vector‑valued RKHS with an inner‑product kernel, the authors derive exact risk trajectories under gradient flow when the number of samples scales proportionally with dimensionality. These trajectories reveal three distinct phases—spectral generalization, noise‑dominated interpolation, and empirical Bayes memorization—whose interplay shapes the distribution of generated samples.
arXiv AI
Jun 24
Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate
arXiv:2606. 23920v1 Announce Type: cross Abstract: The task of compositional generation involves using a conditional generative model, trained only on a subset of the possible conditions, to produce samples from compositionally-defined target distributions such as a geometric combination of the source distributions.