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Found 16 papers, 6 papers with code
Latent Space Disentanglement in Diffusion Transformers Enables Zero-shot Fine-grained Semantic Editing
Through our investigation of DiT's latent space, we have uncovered key findings that unlock the potential for zero-shot fine-grained semantic editing: (1) Both the text and image spaces in DiTs are inherently decomposable.
Cycle-Configuration: A Novel Graph-theoretic Descriptor Set for Molecular Inference
In this paper, we propose a novel family of descriptors of chemical graphs, named cycle-configuration (CC), that can be used in the standard "two-layered (2L) model" of mol-infer, a molecular inference framework based on mixed integer linear programming (MILP) and machine learning (ML).
CoSIGN: Few-Step Guidance of ConSIstency Model to Solve General INverse Problems
Within comparable NFEs, our method achieves new state-of-the-art in diffusion-based inverse problem solving, showcasing the significant potential of employing prior-based inverse problem solvers for real-world applications.
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models
It is a significant yet challenging task to achieve high spatial-temporal resolution in satellite imaging.
DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
To the best of our knowledge, we are the first to utilize a 3D-patch diffusion prior for 3D medical image reconstruction.
Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problems
This paper proposes a method to learn an efficient data prior for the entire image by training diffusion models only on patches of images.
The Role of Identification in Data-driven Policy Iteration: A System Theoretic Study
By casting the concurrent model identification and control design as a feedback interconnection between two algorithmic systems, we provide a closed-loop analysis that shows convergence and robustness properties for arbitrary levels of excitation in the data.
Predicting Ground Reaction Force from Inertial Sensors
Our aim in this paper is to determine if data collected with inertial measurement units (IMUs), that can be worn by athletes during outdoor runs, can be used to predict GRF with sufficient accuracy to allow the analysis of its derived biomechanical variables (e. g., contact time and loading rate).
Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback.
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
However, training diffusion models in the pixel space are both data-intensive and computationally demanding, which restricts their applicability as priors for high-dimensional real-world data such as medical images.
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones.
Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection
Thus, in this paper, we further propose a transfer framework to tackle the cross-domain fraud detection problem, which aims to transfer knowledge from existing domains (source domains) with enough and mature data to improve the performance in the new domain (target domain).
Neural Hierarchical Factorization Machines for User's Event Sequence Analysis
Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance.
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection
In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i. e., field value variations and field interactions simultaneously for fraud detection.
Adapted tree boosting for Transfer Learning
Secure online transaction is an essential task for e-commerce platforms.
Scalable String Reconciliation by Recursive Content-Dependent Shingling
We consider the problem of reconciling similar, but remote, strings with minimum communication complexity.
Information Theory Information Theory
