Search Results for author:
Found 15 papers, 3 papers with code
SUDO: Enhancing Text-to-Image Diffusion Models with Self-Supervised Direct Preference Optimization
In this paper, we introduce Self-sUpervised Direct preference Optimization (SUDO), a novel paradigm that optimizes both fine-grained details at the pixel level and global image quality.
Human Cognition Inspired RAG with Knowledge Graph for Complex Problem Solving
Large language models (LLMs) have demonstrated transformative potential across various domains, yet they face significant challenges in knowledge integration and complex problem reasoning, often leading to hallucinations and unreliable outputs.
GCA-3D: Towards Generalized and Consistent Domain Adaptation of 3D Generators
To enhance the pose and identity consistency, we further propose a hierarchical spatial consistency loss to align the spatial structure between the generated images in the source and target domain.
Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph
The rapid growth of social media platforms has raised significant concerns regarding online content toxicity.
Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification
Cloth-changing person re-identification is a subject closer to the real world, which focuses on solving the problem of person re-identification after pedestrians change clothes.
CHECKWHY: Causal Fact Verification via Argument Structure
In this paper, we introduce CheckWhy, a challenging dataset tailored to a novel causal fact verification task: checking the truthfulness of the causal relation within claims through rigorous reasoning steps.
Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses
Leveraging this rich dataset, we further formulate the crash event feature learning as a novel text reasoning problem and further fine-tune various large language models (LLMs) to predict detailed accident outcomes, such as crash types, severity and number of injuries, based on contextual and environmental factors.
Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs
The upper layer is called Speed Planner, and is a centralized optimal control algorithm.
Local Conditional Controlling for Text-to-Image Diffusion Models
It focuses on controlling specific local region according to user-defined image conditions, while the remaining regions are only conditioned by the original text prompt.
EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification
Experimental results show that the proposed approach outperforms the SOTA baselines and can generate linguistically diverse counterfactual data without disrupting their logical relationships.
Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers
This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular attention to accurate energy values for strongly unsteady driving profiles; and sparse Lagrangian controls via automated vehicles, with a focus on controls that can be executed via existing technology such as adaptive cruise control systems.
The Twelvefold Way of Non-Sequential Lossless Compression
Many information sources are not just sequences of distinguishable symbols but rather have invariances governed by alternative counting paradigms such as permutations, combinations, and partitions.
Information Theory Information Theory
A Lightweight Algorithm to Uncover Deep Relationships in Data Tables
The key to our approach is a computationally lightweight forward addition algorithm that we developed to recursively extract the functional dependencies between table columns that are scalable to tables with many columns.
