13 citations · 18 across the 5 of their papers we have counts for
9 papers
Online hyperparameter optimization by real-time recurrent learning
Daniel Jiwoong Im, Cristina Savin, Kyunghyun Cho
Conventional hyperparameter optimization methods are computationally intensive and hard to generalize to scenarios that require dynamically adapting hyperparameters, such as life-l…
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…
Are skip connections necessary for biologically plausible learning rules?
Daniel Jiwoong Im, Rutuja Patil, Kristin Branson
Backpropagation is the workhorse of deep learning, however, several other biologically-motivated learning rules have been introduced, such as random feedback alignment and differen…
Model-Agnostic Meta-Learning using Runge-Kutta Methods
Daniel Jiwoong Im, Yibo Jiang, Nakul Verma
Meta-learning has emerged as an important framework for learning new tasks from just a few examples. The success of any meta-learning model depends on (i) its fast adaptation to ne…
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…