29 citations · 80 across the 24 of their papers we have counts for
12 papers · 1 filter
Inferring Relational Potentials in Interacting Systems
Armand Comas-Massagué, Yilun Du, Christian Fernandez +4
Systems consisting of interacting agents are prevalent in the world, ranging from dynamical systems in physics to complex biological networks. To build systems which can interact r…
Geometry of Score Based Generative Models
Sandesh Ghimire, Jinyang Liu, Armand Comas +4
In this work, we look at Score-based generative models (also called diffusion generative models) from a geometric perspective. From a new view point, we prove that both the forward…
Boundary-Aware Uncertainty for Feature Attribution Explainers
Davin Hill, Aria Masoomi, Max Torop +2
Post-hoc explanation methods have become a critical tool for understanding black-box classifiers in high-stakes applications. However, high-performing classifiers are often highly…
Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space
Sandesh Ghimire, Aria Masoomi, Jennifer Dy
Estimating Kullback Leibler (KL) divergence from samples of two distributions is essential in many machine learning problems. Variational methods using neural network discriminator…
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization
Prashnna Kumar Gyawali, Sandesh Ghimire, Linwei Wang
The success of deep learning relies on the availability of large-scale annotated data sets, the acquisition of which can be costly, requiring expert domain knowledge. Semi-supervis…
Semi-supervised Medical Image Classification with Global Latent Mixing
Prashnna Kumar Gyawali, Sandesh Ghimire, Pradeep Bajracharya +2
Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates…
Analysis of Discriminator in RKHS Function Space for Kullback-Leibler Divergence Estimation
Sandesh Ghimire, Prashnna K Gyawali, Linwei Wang
Several scalable sample-based methods to compute the Kullback Leibler (KL) divergence between two distributions have been proposed and applied in large-scale machine learning model…
Improving Disentangled Representation Learning with the Beta Bernoulli Process
Prashnna Kumar Gyawali, Zhiyuan Li, Cameron Knight +4
To improve the ability of VAE to disentangle in the latent space, existing works mostly focus on enforcing independence among the learned latent factors. However, the ability of th…
Semi-Supervised Learning by Disentangling and Self-Ensembling Over Stochastic Latent Space
Prashnna Kumar Gyawali, Zhiyuan Li, Sandesh Ghimire +1
The success of deep learning in medical imaging is mostly achieved at the cost of a large labeled data set. Semi-supervised learning (SSL) provides a promising solution by leveragi…
Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
Sandesh Ghimire, Prashnna Kumar Gyawali, Jwala Dhamala +3
Deep learning networks have shown state-of-the-art performance in many image reconstruction problems. However, it is not well understood what properties of representation and learn…
Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors
Prashnna K Gyawali, Cameron Knight, Sandesh Ghimire +3
While deep representation learning has become increasingly capable of separating task-relevant representations from other confounding factors in the data, two significant challenge…
Improving Generalization of Sequence Encoder-Decoder Networks for Inverse Imaging of Cardiac Transmembrane Potential
Sandesh Ghimire, Prashnna Kumar Gyawali, John L Sapp +2
Deep learning models have shown state-of-the-art performance in many inverse reconstruction problems. However, it is not well understood what properties of the latent representatio…