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Found 9 papers, 3 papers with code
Adapting LLaMA Decoder to Vision Transformer
We first "LLaMAfy" a standard ViT step-by-step to align with LLaMA's architecture, and find that directly applying a casual mask to the self-attention brings an attention collapse issue, resulting in the failure to the network training.
LLaMA Pro: Progressive LLaMA with Block Expansion
Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e. g., from LLaMA to CodeLLaMA.
$π$-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation
Foundation models have achieved great advances in multi-task learning with a unified interface of unimodal and multimodal tasks.
Hierarchical Diffusion Autoencoders and Disentangled Image Manipulation
To mitigate those limitations, we propose Hierarchical Diffusion Autoencoders (HDAE) that exploit the fine-grained-to-abstract and lowlevel-to-high-level feature hierarchy for the latent space of diffusion models.
Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning
To address this problem, we propose a generative augmentation framework in swarm learning called SL-GAN, which augments the non-IID data by generating the synthetic data from participants.
An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data
We conclude that the use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning
We allow the effective combination of design experience from different sources, so as to create an effective search space containing a variety of TSF models to support different TSF tasks.
A 1D-0D-3D coupled model for simulating blood flow and transport processes in breast tissue
In this work, we present mixed dimensional models for simulating blood flow and transport processes in breast tissue and the vascular tree supplying it.
