Psoriasis

Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems

Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptual quality yield sharp but less faithful images. Existing approaches either commit to a single operating point on this distortion perception (DP) frontier or require paired-data supervision, auxiliary models, or hyperparameter tuning of the sampler to access...

Starter-Iterator Neural Operator: A Unified Architecture for High-Fidelity Forward and Inverse PDE Problems

Operator learning is an emerging interdisciplinary field that integrates machine learning with scientific computing. By mapping infinite-dimensional function spaces, this approach provides an efficient surrogate modeling framework for high-dimensional partial differential equations (PDEs). Compared to traditional numerical solvers, it achieves a superior trade-off between computational complexity ...

Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining

Inverse design of heterogeneous catalysts remains challenging because catalyst surfaces exhibit substantial structural complexity with coupled surface...

Root-Selecting Fixed-Point Inversion for Rectified Flows via Trajectory Straightness

Finding the initial noise that generates a given data sample, known as inversion, is a key component for downstream applications such as training-free...

Blind Recovery of Latent Domains via Unsupervised Symmetry Discovery

Primary motivation in blind inverse problems is to recover signals of interest from corrupted observations without knowing the obfuscating mechanism. ...

Variance Reduction for Non-Log-Concave Sampling with Applications to Inverse Problems

Sampling from high-dimensional, non-log-concave distributions with unnormalized densities is a fundamental challenge in machine learning, particularly...

Learning Urban Access Costs from Origin-Destination Flows via Inverse Optimal Transport

Cities deliver basic services through mixed public-private facility networks, including schools, clinics, transit providers, and subsidized service po...

MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics

Binding kinetics are crucial for antibody function, shaping pharmacokinetics and in vivo efficacy beyond what equilibrium affinity captures. We presen...

ANNet: A first-principles neural network for forward and inverse dynamics

Biological and robotic systems must solve two related computations to move: inverse dynamics, which determines the forces or torques needed to produce...

Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation

Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of segmentation foundation models for medical imaging. However, most federated LoRA...

Neural networks learn forward dynamics when freed from numerical integration

Seamless interaction between humans and machines requires interfaces that remain robust to the variability inherent in biological signals and physical...

AI-Guided Structure-Aware Modeling and Thermal Proteomics Reveal Direct Demethylzeylasteral-ACLY Interaction

Identifying the direct molecular targets of bioactive natural products remains a central challenge in chemical biology. Here we present an integrated ...

Triadic Dynamics Aware Diffusion Posterior Sampling for Inverse Problems: Optimizing Guidance and Stochasticity Schedules

Generative posterior sampling using diffusion models has emerged as a dominant paradigm for solving inverse problems in imaging, which usually consist...

Genetic code expansion enables programmable covalent protein design

Covalent chemistry has transformed small-molecule drug discovery, yet analogous strategies for proteins remain largely inaccessible because covalent w...

Physics-Informed Neural Networks for Parameter Recovery in the Repressilator Oscillatory Model

Parameter estimation in nonlinear biological dynamical systems is a difficult inverse problem because the governing equations are often stiff or oscil...

Separating Intrinsic Ambiguity from Estimation Uncertainty in Deep Generative Models for Linear Inverse Problems

Recently, deep generative models have been used for posterior inference in inverse problems, including high-stakes applications in medical imaging and...

Principled Design of Diffusion-based Optimizers for Inverse Problems

Score-based diffusion models achieve state-of-the-art performance for inverse problems, but their practical deployment is hindered by long inference t...

Human face perception reflects inverse-generative and naturalistic discriminative objectives

The perceptual representations supporting our ability to recognize faces remain a computational mystery. Deep neural networks offer mechanistic hypoth...

Quantifying Potential Observation Missingness in Inverse Reinforcement Learning

Inverse reinforcement learning (IRL), which infers reward functions from demonstrations, is a valuable tool for modeling and understanding decision-ma...

A Stability Benchmark of Generative Regularizers for Inverse Problems

Generative (diffusion) priors demonstrate remarkable performance in addressing inverse problems in imaging. Yet, for scientific and medical imaging, i...