Recent works proposed amortizing the cost by learning generalized wave functions across different structures and compounds instead of solving each problem independently.

Overall, our method can generate high-fidelity, diverse, and multi-view consistent meshes from single-view wild images within 30 seconds, as shown in Figure 1. We conduct extensive experiments on various wild 2D images with different styles.

Intelligent agents must be generalists, capable of quickly adapting to various tasks.

Our findings on NoRa dataset reveal a prevalent vulnerability to such noise among current LLMs, with existing robust methods like self-correction and self-consistency showing limited efficacy.

However, it seems that including the dimension of time remains challenging.

Conformal prediction is a powerful tool for uncertainty qua ntification, but its application to time-series data is constrained by the violati on of the exchangeability assumption. Current solutions for time-series prediction typically operate in the output space and rely on manually selected weights to addres s distribution drift, leading to overly conservative predictions. To enable dyna mic weight learning in the semantically rich latent space, we introduce a novel a pproach called Con-formalized Time Series with Semantic Features (CT -SSF). CT -SSF utilizes the inductive bias in deep representation learning to dynamica lly adjust weights, prioritizing semantic features relevant to the current predic tion. Theoretically, we show that CT -SSF surpasses previous methods defined in the ou tput space. Experiments on synthetic and benchmark datasets demonstrate tha t CT -SSF significantly outperforms existing state-of-the-art (SOT A) conformal p rediction techniques in terms of prediction efficiency while maintaining a valid cov erage guarantee.

We present an novel framework for efficiently and effectively extending the powerful continuous diffusion processes to discrete modeling.

Wang et al., 2024) is a widely used technique for customizing large language models (LLMs) to handle