Search Results for author:
Found 9 papers, 5 papers with code
Continual Learning of Large Language Models: A Comprehensive Survey
In this survey, we provide a comprehensive overview of the current research progress on LLMs within the context of CL.
PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steering
This prior information is encoded into the attention weights, which are then integrated into the self-attention layers of the generator to guide the synthesis process.
Behavior Generation with Latent Actions
Unlike language or image generation, decision making requires modeling actions - continuous-valued vectors that are multimodal in their distribution, potentially drawn from uncurated sources, where generation errors can compound in sequential prediction.
Enhancing Object Coherence in Layout-to-Image Synthesis
Layout-to-image synthesis is an emerging technique in conditional image generation.
High-fidelity Person-centric Subject-to-Image Synthesis
Specifically, we first develop two specialized pre-trained diffusion models, i. e., Text-driven Diffusion Model (TDM) and Subject-augmented Diffusion Model (SDM), for scene and person generation, respectively.
Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning
To tackle the challenges, we formulate the problem as a course representation learning task-based and develop an Information-aware Graph Representation Learning(IaGRL) for multi-view MOOC quality evaluation.
From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data
While large-scale sequence modeling from offline data has led to impressive performance gains in natural language and image generation, directly translating such ideas to robotics has been challenging.
A Gradient Mapping Guided Explainable Deep Neural Network for Extracapsular Extension Identification in 3D Head and Neck Cancer Computed Tomography Images
However, manual annotation of lymph node region is a required data preprocessing step in most of the current ML-based ECE diagnosis studies.
Rice Diseases Detection and Classification Using Attention Based Neural Network and Bayesian Optimization
In this research, an attention-based depthwise separable neural network with Bayesian optimization (ADSNN-BO) is proposed to detect and classify rice disease from rice leaf images.
