b'Edward Kim'

research

∙ 09/14/2023

Semantic Adversarial Attacks via Diffusion Models

Traditional adversarial attacks concentrate on manipulating clean exampl...

0 Chenan Wang, et al. ∙

research

∙ 09/01/2023

DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion Models

Generating high-quality labeled image datasets is crucial for training a...

0 Michael Shenoda, et al. ∙

research

∙ 07/28/2023

Lessons in Reproducibility: Insights from NLP Studies in Materials Science

Natural Language Processing (NLP), a cornerstone field within artificial...

0 Xiangyun Lei, et al. ∙

research

∙ 07/06/2023

3D Environment Modeling for Falsification and Beyond with Scenic 3.0

We present a major new version of Scenic, a probabilistic programming la...

0 Eric Vin, et al. ∙

research

∙ 07/04/2023

Generative Artificial Intelligence Consensus in a Trustless Network

We performed a billion locality sensitive hash comparisons between artif...

0 Edward Kim, et al. ∙

research

∙ 12/29/2022

Investigating Sindy As a Tool For Causal Discovery In Time Series Signals

The SINDy algorithm has been successfully used to identify the governing...

0 Andrew O'Brien, et al. ∙

research

∙ 12/06/2022

MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited Training Examples

Point-of-Care Ultrasound (POCUS) refers to clinician-performed and inter...

0 Darryl Hannan, et al. ∙

research

∙ 07/04/2022

Emergency Management and Recovery of Luna Classic

In early May 2022, the Terra ecosystem collapsed after the algorithmic s...

0 Edward Kim, et al. ∙

research

∙ 05/30/2022

Dictionary Learning with Accumulator Neurons

The Locally Competitive Algorithm (LCA) uses local competition between n...

0 Gavin Parpart, et al. ∙

research

∙ 04/14/2022

EvoSTS Forecasting: Evolutionary Sparse Time-Series Forecasting

In this work, we highlight our novel evolutionary sparse time-series for...

12 Ethan Jacob Moyer, et al. ∙

research

∙ 03/11/2022

Perception Over Time: Temporal Dynamics for Robust Image Understanding

While deep learning surpasses human-level performance in narrow and spec...

0 Maryam Daniali, et al. ∙

research

∙ 12/01/2021

Querying Labelled Data with Scenario Programs for Sim-to-Real Validation

Simulation-based testing of autonomous vehicles (AVs) has become an esse...

0 Edward Kim, et al. ∙

research

∙ 10/28/2021

A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation

Behavior prediction remains one of the most challenging tasks in the aut...

0 Francis Indaheng, et al. ∙

research

∙ 10/01/2021

Multi-Agent Algorithmic Recourse

The recent adoption of machine learning as a tool in real world decision...

0 Andrew O'Brien, et al. ∙

research

∙ 08/20/2021

Addressing the IEEE AV Test Challenge with Scenic and VerifAI

This paper summarizes our formal approach to testing autonomous vehicles...

0 Kesav Viswanadha, et al. ∙

research

∙ 07/09/2021

Parallel and Multi-Objective Falsification with Scenic and VerifAI

Falsification has emerged as an important tool for simulation-based veri...

0 Kesav Viswanadha, et al. ∙

research

∙ 06/18/2021

Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments

The capability of reinforcement learning (RL) agent directly depends on ...

21 Abdus Salam Azad, et al. ∙

research

∙ 06/13/2021

ATRAS: Adversarially Trained Robust Architecture Search

In this paper, we explore the effect of architecture completeness on adv...

0 Yigit Alparslan, et al. ∙

research

∙ 04/18/2021

Functional Protein Structure Annotation Using a Deep Convolutional Generative Adversarial Network

Identifying novel functional protein structures is at the heart of molec...

1 Ethan Moyer, et al. ∙

research

∙ 03/01/2021

Extreme Volatility Prediction in Stock Market: When GameStop meets Long Short-Term Memory Networks

The beginning of 2021 saw a surge in volatility for certain stocks such ...

0 Yigit Alparslan, et al. ∙

research

∙ 02/24/2021

Robust SleepNets

State-of-the-art convolutional neural networks excel in machine learning...

0 Yigit Alparslan, et al. ∙

research

∙ 01/16/2021

Evaluating Online and Offline Accuracy Traversal Algorithms for k-Complete Neural Network Architectures

Architecture sizes for neural networks have been studied widely and seve...

0 Yigit Alparslan, et al. ∙

research

∙ 01/16/2021

Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems

In recent years, deep neural networks have had great success in machine ...

0 Yigit Alparslan, et al. ∙

research

∙ 11/30/2020

A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving

Safely interacting with humans is a significant challenge for autonomous...

0 Jay Shenoy, et al. ∙

research

∙ 11/24/2020

The Interpretable Dictionary in Sparse Coding

Artificial neural networks (ANNs), specifically deep learning networks, ...

55 Edward Kim, et al. ∙

research

∙ 11/23/2020

The Selectivity and Competition of the Mind's Eye in Visual Perception

Research has shown that neurons within the brain are selective to certai...

14 Edward Kim, et al. ∙

research

∙ 10/13/2020

Scenic: A Language for Scenario Specification and Data Generation

We propose a new probabilistic programming language for the design and a...

0 Daniel J. Fremont, et al. ∙

research

∙ 03/17/2020

Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World

We present a new approach to automated scenario-based testing of the saf...

0 Daniel J. Fremont, et al. ∙

research

∙ 12/01/2019

A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors

Even as deep neural networks have become very effective for tasks in vis...

39 Edward Kim, et al. ∙

research

∙ 11/25/2019

Machine-learned metrics for predicting the likelihood of success in materials discovery

Materials discovery is often compared to the challenge of finding a need...

0 Yoolhee Kim, et al. ∙

research

∙ 11/25/2019

Machine-learned metrics for predicting thelikelihood of success in materials discovery

Materials discovery is often compared to the challenge of finding a need...

0 Yoolhee Kim, et al. ∙

research

∙ 05/16/2019

The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures

Materials science literature contains millions of materials synthesis pr...

0 Sheshera Mysore, et al. ∙

research

∙ 02/12/2019

VERIFAI: A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems

We present VERIFAI, a software toolkit for the formal design and analysi...

0 Tommaso Dreossi, et al. ∙

research

∙ 12/31/2018

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

Leveraging new data sources is a key step in accelerating the pace of ma...

0 Edward Kim, et al. ∙

research

∙ 11/17/2018

Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples

Although deep learning has shown great success in recent years, research...

0 Jacob M. Springer, et al. ∙

research

∙ 11/21/2017

Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons

Deep feed-forward convolutional neural networks (CNNs) have become ubiqu...

0 Edward Kim, et al. ∙

research

∙ 11/18/2017

Automatically Extracting Action Graphs from Materials Science Synthesis Procedures

Computational synthesis planning approaches have achieved recent success...

0 Sheshera Mysore, et al. ∙

Edward Kim

Featured Co-authors