The computational and memory overheads associated with expanding the context window of LLMs severely limit their scalability. A noteworthy solution is vision-te...
Large language models are increasingly adapted to downstream tasks through fine-tuning. Full supervised fine-tuning (SFT) and parameter-efficient fine-tuning (P...
LLMs (Large Language Models) are increasingly used in text processing pipelines to intelligently respond to a variety of inputs and generation tasks. This raise...
Working memory enables the brain to integrate transient information for rapid decision-making. Artificial networks typically replicate this via recurrent or par...
Article URL: https://research.roundtable.ai/capabilities-humanness/ Comments URL: https://news.ycombinator.com/item?id=46302262 Points: 15 Comments: 8...
Here's how to detect point anomalies within each series, and identify anomalous signals across the whole bank The post A Practical Toolkit for Time Series Anoma...
The success of large language models for code relies on vast amounts of code data, including public open-source repositories, such as GitHub, and private, confi...
The use of large language models like ChatGPT in code review offers promising efficiency gains but also raises concerns about correctness and safety. Existing e...
We present LLMQ, an end-to-end CUDA/C++ implementation for medium-sized language-model training, e.g. 3B to 32B parameters, on affordable, commodity GPUs. These...
Introduction Data annotation is a foundational process in artificial intelligence that enables machines to learn from real‑world data. It involves adding meani...
Data-driven evolutionary algorithms has shown surprising results in addressing expensive optimization problems through robust surrogate modeling. Though promisi...
An AI background remover may feel like magic at first glance. You upload an image, click a button, and the background disappears. Behind that simple interaction...
