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Found 10 papers, 3 papers with code
Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models
Diffusion models (DMs) embark a new era of generative modeling and offer more opportunities for efficient generating high-quality and realistic data samples.
TransGPT: Multi-modal Generative Pre-trained Transformer for Transportation
TransGPT-MM is finetuned on a multi-modal Transportation dataset (MTD) that we manually collected from three areas of the transportation domain: driving tests, traffic signs, and landmarks.
Multi-Network Constrained Operational Optimization in Community Integrated Energy Systems: A Safe Reinforcement Learning Approach
The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources.
DoDo-Code: a Deep Levenshtein Distance Embedding-based Code for IDS Channel and DNA Storage
In this paper, we propose an innovative approach that utilizes deep Levenshtein distance embedding to bypass these mathematical challenges.
Levenshtein Distance Embedding with Poisson Regression for DNA Storage
Under this embedding dimension, the Poisson regression is introduced by assuming the Levenshtein distance between sequences of fixed length following a Poisson distribution, which naturally aligns with the definition of Levenshtein distance.
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction
In order to construct or extend entity-centric and event-centric knowledge graphs (KG and EKG), the information extraction (IE) annotation toolkit is essential.
Prompt What You Need: Enhancing Segmentation in Rainy Scenes with Anchor-based Prompting
Semantic segmentation in rainy scenes is a challenging task due to the complex environment, class distribution imbalance, and limited annotated data.
Zero-Shot Information Extraction via Chatting with ChatGPT
Zero-shot information extraction (IE) aims to build IE systems from the unannotated text.
LMSA: Low-relation Mutil-head Self-Attention Mechanism in Visual Transformer
By appropriately compressing the dimensions of the self-attention relationship variables, the Transformer network can be more efficient and even perform better.
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Despite being impactful on a variety of problems and applications, the generative adversarial nets (GANs) are remarkably difficult to train.
