219 citations · 287 across the 20 of their papers we have counts for
21 papers
Generative Artificial Intelligence Reproducibility and Consensus
Edward Kim, Isamu Isozaki, Naomi Sirkin +1
We performed a billion locality sensitive hash comparisons between artificially generated data samples to answer the critical question - can we reproduce the results of generative…
Multi-Agent Algorithmic Recourse
Andrew O'Brien, Edward Kim
The recent adoption of machine learning as a tool in real world decision making has spurred interest in understanding how these decisions are being made. Counterfactual Explanation…
Parallel and Multi-Objective Falsification with Scenic and VerifAI
Kesav Viswanadha, Edward Kim, Francis Indaheng +2
Falsification has emerged as an important tool for simulation-based verification of autonomous systems. In this paper, we present extensions to the Scenic scenario specification la…
ATRAS: Adversarially Trained Robust Architecture Search
Yigit Alparslan, Edward Kim
In this paper, we explore the effect of architecture completeness on adversarial robustness. We train models with different architectures on CIFAR-10 and MNIST dataset. For each mo…
Functional Protein Structure Annotation Using a Deep Convolutional Generative Adversarial Network
Ethan Moyer, Jeff Winchell, Isamu Isozaki +3
Identifying novel functional protein structures is at the heart of molecular engineering and molecular biology, requiring an often computationally exhaustive search. We introduce t…
Extreme Volatility Prediction in Stock Market: When GameStop meets Long Short-Term Memory Networks
Yigit Alparslan, Edward Kim
The beginning of 2021 saw a surge in volatility for certain stocks such as GameStop company stock (Ticker GME under NYSE). GameStop stock increased around 10 fold from its decade-l…
Robust SleepNets
Yigit Alparslan, Edward Kim
State-of-the-art convolutional neural networks excel in machine learning tasks such as face recognition, and object classification but suffer significantly when adversarial attacks…
Evaluating Online and Offline Accuracy Traversal Algorithms for k-Complete Neural Network Architectures
Yigit Alparslan, Ethan Jacob Moyer, Edward Kim
Architecture sizes for neural networks have been studied widely and several search methods have been offered to find the best architecture size in the shortest amount of time possi…
Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems
Yigit Alparslan, Ethan Jacob Moyer, Isamu Mclean Isozaki +4
In recent years, deep neural networks have had great success in machine learning and pattern recognition. Architecture size for a neural network contributes significantly to the su…
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving
Jay Shenoy, Edward Kim, Xiangyu Yue +4
Safely interacting with humans is a significant challenge for autonomous driving. The performance of this interaction depends on machine learning-based modules of an autopilot, suc…
The Interpretable Dictionary in Sparse Coding
Edward Kim, Connor Onweller, Andrew O'Brien +1
Artificial neural networks (ANNs), specifically deep learning networks, have often been labeled as black boxes due to the fact that the internal representation of the data is not e…
The Selectivity and Competition of the Mind's Eye in Visual Perception
Edward Kim, Maryam Daniali, Jocelyn Rego +1
Research has shown that neurons within the brain are selective to certain stimuli. For example, the fusiform face area (FFA) region is known by neuroscientists to selectively activ…
Scenic: A Language for Scenario Specification and Data Generation
Daniel J. Fremont, Edward Kim, Tommaso Dreossi +4
We propose a new probabilistic programming language for the design and analysis of cyber-physical systems, especially those based on machine learning. Specifically, we consider the…
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors
Edward Kim, Divya Gopinath, Corina Pasareanu +1
Even as deep neural networks have become very effective for tasks in vision and perception, it remains difficult to explain and debug their behavior. In this paper, we present a pr…
Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder
Hao Sun, Jiadong Guo, Edward J. Kim +1
The increasing amount of data in astronomy provides great challenges for machine learning research. Previously, supervised learning methods achieved satisfactory recognition accura…
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Sheshera Mysore, Zach Jensen, Edward Kim +6
Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures…
VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems
Tommaso Dreossi, Daniel J. Fremont, Shromona Ghosh +4
We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components. VERIFAI particu…
Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples
Jacob M. Springer, Charles S. Strauss, Austin M. Thresher +2
Although deep learning has shown great success in recent years, researchers have discovered a critical flaw where small, imperceptible changes in the input to the system can drasti…
Automatically Extracting Action Graphs from Materials Science Synthesis Procedures
Sheshera Mysore, Edward Kim, Emma Strubell +6
Computational synthesis planning approaches have achieved recent success in organic chemistry, where tabulated synthesis procedures are readily available for supervised learning. T…
Star-galaxy Classification Using Deep Convolutional Neural Networks
Edward J. Kim, Robert J. Brunner
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning…
Teaching Data Science
Robert J. Brunner, Edward J. Kim
We describe an introductory data science course, entitled Introduction to Data Science, offered at the University of Illinois at Urbana-Champaign. The course introduced general pro…