In this work, we empirically investigate the impact of neural parameter symmetries by introducing new neural network architectures that have reduced parameter space symmetries.

Tokenizer, serving as a translator to map the intricate visual data into a compact latent space, lies at the core of visual generative models.

As Large Language Models (LLMs) are integrated into critical real-world applications, their strategic and logical reasoning abilities are increasingly crucial.

Recently, various strategies for distributed training of large language models (LLMs) have been proposed.

GPT can handle core motion comprehension and generation tasks, including text-to-motion, motion-to-text, music-to-dance, dance-to-music, motion prediction, and motion in-between.

Can Transformers predict new syllogisms by composing established ones?