71 citations · 214 across the 20 of their papers we have counts for
22 papers
Deep Autoregressive Models as Causal Inference Engines
Daniel Jiwoong Im, Kevin Zhang, Nakul Verma +1
Existing causal inference (CI) models are often restricted to data with low-dimensional confounders and singleton actions. We propose an autoregressive (AR) CI framework capable of…
Onchain Sports Betting using UBET Automated Market Maker
Daniel Jiwoong Im, Alexander Kondratskiy, Vincent Harvey +1
The paper underscores how decentralization in sports betting addresses the drawbacks of traditional centralized platforms, ensuring transparency, security, and lower fees. Non-cust…
Active and Passive Causal Inference Learning
Daniel Jiwoong Im, Kyunghyun Cho
This paper serves as a starting point for machine learning researchers, engineers and students who are interested in but not yet familiar with causal inference. We start by laying…
UAMM: Price-oracle based Automated Market Maker
Daniel Jiwoong Im, Alexander Kondratskiy, Vincent Harvey +1
Automated market makers (AMMs) are pricing mechanisms utilized by decentralized exchanges (DEX). Traditional AMM approaches are constrained by pricing solely based on their own liq…
Causal Effect Variational Autoencoder with Uniform Treatment
Daniel Jiwoong Im, Kyunghyun Cho, Narges Razavian
Domain adaptation and covariate shift are big issues in deep learning and they ultimately affect any causal inference algorithms that rely on deep neural networks. Causal effect va…
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…
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…
Neural Machine Translation with Gumbel-Greedy Decoding
Jiatao Gu, Daniel Jiwoong Im, Victor O. K. Li
Previous neural machine translation models used some heuristic search algorithms (e.g., beam search) in order to avoid solving the maximum a posteriori problem over translation sen…
Generative Adversarial Parallelization
Daniel Jiwoong Im, He Ma, Chris Dongjoo Kim +1
Generative Adversarial Networks have become one of the most studied frameworks for unsupervised learning due to their intuitive formulation. They have also been shown to be capable…
An empirical analysis of the optimization of deep network loss surfaces
Daniel Jiwoong Im, Michael Tao, Kristin Branson
The success of deep neural networks hinges on our ability to accurately and efficiently optimize high-dimensional, non-convex functions. In this paper, we empirically investigate t…
Learning a metric for class-conditional KNN
Daniel Jiwoong Im, Graham W. Taylor
Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitiv…
Generating images with recurrent adversarial networks
Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang +1
Gatys et al. (2015) showed that optimizing pixels to match features in a convolutional network with respect reference image features is a way to render images of high visual qualit…
Denoising Criterion for Variational Auto-Encoding Framework
Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic +1
Denoising autoencoders (DAE) are trained to reconstruct their clean inputs with noise injected at the input level, while variational autoencoders (VAE) are trained with noise injec…
Conservativeness of untied auto-encoders
Daniel Jiwoong Im, Mohamed Ishmael Diwan Belghazi, Roland Memisevic
We discuss necessary and sufficient conditions for an auto-encoder to define a conservative vector field, in which case it is associated with an energy function akin to the unnorma…
Neural Network Regularization via Robust Weight Factorization
Jan Rudy, Weiguang Ding, Daniel Jiwoong Im +1
Regularization is essential when training large neural networks. As deep neural networks can be mathematically interpreted as universal function approximators, they are effective a…
Understanding Minimum Probability Flow for RBMs Under Various Kinds of Dynamics
Daniel Jiwoong Im, Ethan Buchman, Graham W. Taylor
Energy-based models are popular in machine learning due to the elegance of their formulation and their relationship to statistical physics. Among these, the Restricted Boltzmann Ma…
Scoring and Classifying with Gated Auto-encoders
Daniel Jiwoong Im, Graham W. Taylor
Auto-encoders are perhaps the best-known non-probabilistic methods for representation learning. They are conceptually simple and easy to train. Recent theoretical work has shed lig…