We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models.
Thus, we open-source a new AI agent library, AgentLite, which simplifies this process by offering a lightweight, user-friendly platform for innovating LLM agent reasoning, architectures, and applications with ease.
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.
The rapid expansion of the open-source language model landscape presents an opportunity to merge the competencies of these model checkpoints by combining their parameters.
We adopt a two-stage training strategy for the diffusion model, effectively binding movements with specific appearances.
Evaluating outputs of large language models (LLMs) is challenging, requiring making -- and making sense of -- many responses.
We also observe that the initiation denoising timestep for noise blending is the key to identity preservation and layout.
Research in mechanistic interpretability seeks to explain behaviors of machine learning models in terms of their internal components.
Transformers have widely adopted attention networks for sequence mixing and MLPs for channel mixing, playing a pivotal role in achieving breakthroughs across domains.
Our results show that a multi-scale smaller model has comparable learning capacity to a larger model, and pre-training smaller models with S$^2$ can match or even exceed the advantage of larger models.