research
∙
06/08/2023
AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs
We present AircraftVerse, a publicly available aerial vehicle design dat...
share
research
∙
03/25/2023
Measuring Classification Decision Certainty and Doubt
Quantitative characterizations and estimations of uncertainty are of fun...
research
∙
01/10/2023
On the Robustness of AlphaFold: A COVID-19 Case Study
Protein folding neural networks (PFNNs) such as AlphaFold predict remark...
research
∙
11/11/2022
Design of Unmanned Air Vehicles Using Transformer Surrogate Models
Computer-aided design (CAD) is a promising new area for the application ...
research
∙
07/24/2022
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Machine learning models are prone to making incorrect predictions on inp...
research
∙
07/06/2022
Inferring and Conveying Intentionality: Beyond Numerical Rewards to Logical Intentions
Shared intentionality is a critical component in developing conscious AI...
research
∙
06/20/2022
Multiple Testing Framework for Out-of-Distribution Detection
We study the problem of Out-of-Distribution (OOD) detection, that is, de...
research
∙
02/14/2022
Principal Manifold Flows
Normalizing flows map an independent set of latent variables to their sa...
research
∙
02/11/2022
Detecting out-of-context objects using contextual cues
This paper presents an approach to detect out-of-context (OOC) objects i...
research
∙
01/07/2022
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Machine learning methods such as deep neural networks (DNNs), despite th...
research
∙
12/14/2021
Dual-Key Multimodal Backdoors for Visual Question Answering
The success of deep learning has enabled advances in multimodal tasks th...
research
∙
09/09/2021
Protein Folding Neural Networks Are Not Robust
Deep neural networks such as AlphaFold and RoseTTAFold predict remarkabl...
research
∙
08/13/2021
Detecting OODs as datapoints with High Uncertainty
Deep neural networks (DNNs) are known to produce incorrect predictions w...
research
∙
03/29/2021
Online Defense of Trojaned Models using Misattributions
This paper proposes a new approach to detecting neural Trojans on Deep N...
research
∙
03/23/2021
Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs
Deep neural networks (DNNs) are known to produce incorrect predictions w...
research
∙
12/03/2020
Detecting Trojaned DNNs Using Counterfactual Attributions
We target the problem of detecting Trojans or backdoors in DNNs. Such mo...
research
∙
11/25/2020
Learning Certified Control using Contraction Metric
In this paper, we solve the problem of finding a certified control polic...
research
∙
09/17/2020
An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks
Deep neural networks have been shown to be vulnerable to membership infe...
research
∙
10/29/2019
Estimating the Density of States of Boolean Satisfiability Problems on Classical and Quantum Computing Platforms
Given a Boolean formula Ï•(x) in conjunctive normal form (CNF), the densi...
research
∙
09/07/2019
On Need for Topology Awareness of Generative Models
Manifold assumption in learning states that: the data lie approximately ...
research
∙
09/07/2019
On Need for Topology-Aware Generative Models for Manifold-Based Defenses
ML algorithms or models, especially deep neural networks (DNNs), have sh...
research
∙
03/14/2019
Attribution-driven Causal Analysis for Detection of Adversarial Examples
Attribution methods have been developed to explain the decision of a mac...
research
∙
03/01/2019
TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents
Recent work has identified that classification models implemented as neu...
research
∙
05/18/2018
Trusted Neural Networks for Safety-Constrained Autonomous Control
We propose Trusted Neural Network (TNN) models, which are deep neural ne...
research
∙
10/11/2017
Specification Inference from Demonstrations
Learning from expert demonstrations has received a lot of attention in a...
research
∙
09/26/2017
Output Range Analysis for Deep Neural Networks
Deep neural networks (NN) are extensively used for machine learning task...
research
∙
05/15/2015
A Theory of Formal Synthesis via Inductive Learning
Formal synthesis is the process of generating a program satisfying a hig...
research
∙
07/21/2014
