Update papers.bib

Author: harishB97

_bibliography/papers.bib

@@ -11,7 +11,7 @@ @article{khurana2024hierarchical
 keywords = {Evolution, Diffusion Models, Hierarchical Conditioning, Generative AI, Computer Vision},
 preview = {phylo_diffusion_architecture.jpg},
 website = {https://imageomics.github.io/phylo-diffusion},
-arxiv = {2408.00160v1},
+pdf = {https://arxiv.org/abs/2408.00160v1},
 abs = {A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical Embeddings (HIER-Embeds). We also propose two new experiments for perturbing the embedding space of Phylo-Diffusion: trait masking and trait swapping, inspired by counterpart experiments of gene knockout and gene editing/swapping. Our work represents a novel methodological advance in generative modeling to structure the embedding space of diffusion models using tree-based knowledge. Our work also opens a new chapter of research in evolutionary biology by using generative models to visualize evolutionary changes directly from images. We empirically demonstrate the usefulness of Phylo-Diffusion in capturing meaningful trait variations for fishes and birds, revealing novel insights about the biological mechanisms of their evolution.}
 }
 
@@ -55,7 +55,9 @@ @inproceedings{elhamod2023discovering
   author={Elhamod, Mohannad and Khurana, Mridul and Manogaran, Harish Babu and Uyeda, Josef C and Balk, Meghan A and Dahdul, Wasila and Bakis, Yasin and Bart Jr, Henry L and Mabee, Paula M and Lapp, Hilmar and others},
   booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
   pages={3966--3978},
-  year={2023}
+  year={2023},
+  pdf={https://arxiv.org/abs/2306.03228},
+  preview = {phyloNN_toc.png},
 }
 
 @inproceedings{sawhney2023detecting,