Correct paper for submission

Author: homerjed

paper/paper.bib

@@ -59,7 +59,7 @@ @misc{Feng2023
 }
 
 @misc{Feng2024,
-      title={Variational Bayesian Imaging with an Efficient Surrogate Score-based Prior}, 
+      title={{V}ariational {B}ayesian {I}maging with an {E}fficient {S}urrogate {S}core-based {P}rior}, 
       author={Berthy T. Feng and Katherine L. Bouman},
       year={2024},
       eprint={2309.01949},
@@ -159,7 +159,7 @@ @misc{ambientdiffusion
 }
 
 @article{emulating,
-   title={<scp>CosmoPower</scp>: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys},
+   title={CosmoPower: emulating cosmological power spectra for accelerated {B}ayesian inference from next-generation surveys},
    volume={511},
    ISSN={1365-2966},
    url={http://dx.doi.org/10.1093/mnras/stac064},
@@ -224,16 +224,6 @@ @misc{ldms
       url={https://arxiv.org/abs/2112.10752}, 
 }
 
-@misc{dits,
-      title={Scalable Diffusion Models with Transformers}, 
-      author={William Peebles and Saining Xie},
-      year={2023},
-      eprint={2212.09748},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2212.09748}, 
-}
-
 @misc{gans,
       title={Generative Adversarial Networks}, 
       author={Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron Courville and Yoshua Bengio},
@@ -245,7 +235,7 @@ @misc{gans
 }
 
 @misc{vaes,
-      title={Auto-Encoding Variational Bayes}, 
+      title={{A}uto-{E}ncoding {V}ariational {B}ayes}, 
       author={Diederik P Kingma and Max Welling},
       year={2022},
       eprint={1312.6114},

paper/paper.md

@@ -30,7 +30,7 @@ aas-journal: Astrophysical Journal <- The name of the AAS journal.
 
 # Summary
 
-Diffusion models [@diffusion; @ddpm; @sde] have emerged as the dominant paradigm for generative modeling based on performance in a variety of tasks [@ldms; @dits]. The advantages of accurate density estimation and high-quality samples of normalising flows [@flows; @ffjord], VAEs [@vaes] and GANs [@gans] are subsumed into this method. Significant limitations exist on implicit and neural network based likelihood models with respect to modeling normalised probability distributions and sampling speed. Score-matching diffusion models are more efficient than previous generative model algorithms for these tasks. The diffusion process is agnostic to the data representation meaning different types of data such as audio, point-clouds, videos and images can be modelled. 
+Diffusion models [@diffusion; @ddpm; @sde] have emerged as the dominant paradigm for generative modeling based on performance in a variety of tasks [@ldms; @dit]. The advantages of accurate density estimation and high-quality samples of normalising flows [@flows; @ffjord], VAEs [@vaes] and GANs [@gans] are subsumed into this method. Significant limitations exist on implicit and neural network based likelihood models with respect to modeling normalised probability distributions and sampling speed. Score-matching diffusion models are more efficient than previous generative model algorithms for these tasks. The diffusion process is agnostic to the data representation meaning different types of data such as audio, point-clouds, videos and images can be modelled. 
 
 # Statement of need