20 citations · 21 across the 4 of their papers we have counts for
6 papers · 1 filter
Evaluation metrics for behaviour modeling
Daniel Jiwoong Im, Iljung Kwak, Kristin Branson
A primary difficulty with unsupervised discovery of structure in large data sets is a lack of quantitative evaluation criteria. In this work, we propose and investigate several met…
Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging Data
Daniel Jiwoong Im, Sridhama Prakhya, Jinyao Yan +2
The Importance Weighted Auto Encoder (IWAE) objective has been shown to improve the training of generative models over the standard Variational Auto Encoder (VAE) objective. Here,…
Stochastic Neighbor Embedding under f-divergences
Daniel Jiwoong Im, Nakul Verma, Kristin Branson
The t-distributed Stochastic Neighbor Embedding (t-SNE) is a powerful and popular method for visualizing high-dimensional data. It minimizes the Kullback-Leibler (KL) divergence be…
Quantitatively Evaluating GANs With Divergences Proposed for Training
Daniel Jiwoong Im, He Ma, Graham Taylor +1
Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has be…
Network-size independent covering number bounds for deep networks
Mayank Kabra, Kristin Branson
We give a covering number bound for deep learning networks that is independent of the size of the network. The key for the simple analysis is that for linear classifiers, rotating…
Sample complexity of learning Mahalanobis distance metrics
Nakul Verma, Kristin Branson
Metric learning seeks a transformation of the feature space that enhances prediction quality for the given task at hand. In this work we provide PAC-style sample complexity rates f…