4 citations · 6 across the 6 of their papers we have counts for
8 papers
Atelier: Learning Local Self-Supervised Features for CryoEM Volumes via Hypernetworks
Phillip Lo, Sudarshan Babu, Dari Kimanius +1
CryoEM map interpretation requires features that are spatially localized, consistent across samples, and informative across spatial scales. Most deep learning methods for map annot…
Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models
Giovanni Palla, Sudarshan Babu, Payam Dibaeinia +5
Computational modeling of single-cell gene expression is crucial for understanding cellular processes, but generating realistic expression profiles remains a major challenge. This…
HyperDiffusionFields (HyDiF): Diffusion-Guided Hypernetworks for Learning Implicit Molecular Neural Fields
Sudarshan Babu, Phillip Lo, Xiao Zhang +5
We introduce HyperDiffusionFields (HyDiF), a framework that models 3D molecular conformers as continuous fields rather than discrete atomic coordinates or graphs. At the core of ou…
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures
Sudarshan Babu
The ability to transfer knowledge from prior experiences to novel tasks stands as a pivotal capability of intelligent agents, including both humans and computational models. This p…
iSeg: Interactive 3D Segmentation via Interactive Attention
Itai Lang, Fei Xu, Dale Decatur +2
We present iSeg, a new interactive technique for segmenting 3D shapes. Previous works have focused mainly on leveraging pre-trained 2D foundation models for 3D segmentation based o…
HyperFields: Towards Zero-Shot Generation of NeRFs from Text
Sudarshan Babu, Richard Liu, Avery Zhou +3
We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and (optionally) some fine-tuning. Key to our approach…
Domain-independent Dominance of Adaptive Methods
Pedro Savarese, David McAllester, Sudarshan Babu +1
From a simplified analysis of adaptive methods, we derive AvaGrad, a new optimizer which outperforms SGD on vision tasks when its adaptability is properly tuned. We observe that th…
Scalable K-Medoids via True Error Bound and Familywise Bandits
Aravindakshan Babu, Saurabh Agarwal, Sudarshan Babu +1
K-Medoids(KM) is a standard clustering method, used extensively on semi-metric data.Error analyses of KM have traditionally used an in-sample notion of error,which can be far from…