Suchprotocols could verify the amount and kind of data and compute used to train the model, including whether it was trained on specific harmful or beneficial data sources. We explore efficient verification strategies for Proof-of-Training-Data that are compatible with most current large-model training procedures.

Topic models, which have the ability to uncover the hidden semantic structure in a text corpus, have been widely applied to text analysis.

This work proposes abranch and bound (BB) scheme, inwhich atailored Lagrangian relaxation method proposed in the 1970s is used to provide alower boundateachBBnode.

A wide array of control applications, ranging from medical to engineering, fundamentally deals with critical systems, i.e., systems of vital importance where the control actions have to guarantee no harm to the system functionality. Examples include managing nuclear fusion [Degrave et al., 2022], performing robotic surgeries [Datta et al., 2021], and devising patient treatment strategies [Komorowski et al., 2018].

NDRalsofurther refines thecamera poses in a global optimization manner. Experiments on public datasets and our collected dataset demonstrate that NDR outperforms existing monocular dynamic reconstructionmethods.

We propose a new Stein self-repulsive dynamics for obtaining diversified samples from intractable un-normalized distributions.

To adapt this to our setting, we need to scale some terms properly.