This paper provides a comprehensive review of the NTIRE 2024 challenge focusing on
efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this …

On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest since they are more cost-effective, latency-efficient, and privacy …

To parameter-efficiently fine-tune (PEFT) large language models (LLMs), the low-rank
adaptation (LoRA) method approximates the model changes $\Delta W\in\mathbb …

Parameter-efficient finetuning (PEFT) methods seek to adapt large neural models via
updates to a small number of weights. However, much prior interpretability work has shown …

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly
impacting molecule design, property prediction, and synthesis optimization. This review …

Large language models (LLMs) have garnered unprecedented advancements across
diverse fields, ranging from natural language processing to computer vision and beyond …

Diffusion models have demonstrated remarkable and robust abilities in both image and
video generation. To achieve greater control over generated results, researchers introduce …

This comprehensive study evaluates the performance of OpenAI's o1-preview large
language model across a diverse array of complex reasoning tasks, spanning multiple …

Reticular chemistry is the science of connecting molecular building units into crystalline
extended structures such as metal–organic frameworks and covalent organic frameworks …

Fine-tuning large-scale pretrained models is prohibitively expensive in terms of
computational and memory costs. LoRA, as one of the most popular Parameter-Efficient Fine …