12 citations · 44 across the 15 of their papers we have counts for
15 papers
Class Prototypes based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos
Rohit Gupta, Anirban Roy, Claire Christensen +6
The recent growth in the consumption of online media by children during early childhood necessitates data-driven tools enabling educators to filter out appropriate educational cont…
Direct Amortized Likelihood Ratio Estimation
Adam D. Cobb, Brian Matejek, Daniel Elenius +2
We introduce a new amortized likelihood ratio estimator for likelihood-free simulation-based inference (SBI). Our estimator is simple to train and estimates the likelihood ratio us…
TIJO: Trigger Inversion with Joint Optimization for Defending Multimodal Backdoored Models
Indranil Sur, Karan Sikka, Matthew Walmer +5
We present a Multimodal Backdoor Defense technique TIJO (Trigger Inversion using Joint Optimization). Recent work arXiv:2112.07668 has demonstrated successful backdoor attacks on m…
AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs
Adam D. Cobb, Anirban Roy, Daniel Elenius +9
We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representatio…
Automatic Measures for Evaluating Generative Design Methods for Architects
Eric Yeh, Briland Hitaj, Vidyasagar Sadhu +3
The recent explosion of high-quality image-to-image methods has prompted interest in applying image-to-image methods towards artistic and design tasks. Of interest for architects i…
Design of Unmanned Air Vehicles Using Transformer Surrogate Models
Adam D. Cobb, Anirban Roy, Daniel Elenius +1
Computer-aided design (CAD) is a promising new area for the application of artificial intelligence (AI) and machine learning (ML). The current practice of design of cyber-physical…
Multiple Testing Framework for Out-of-Distribution Detection
Akshayaa Magesh, Venugopal V. Veeravalli, Anirban Roy +1
We study the problem of Out-of-Distribution (OOD) detection, that is, detecting whether a learning algorithm's output can be trusted at inference time. While a number of tests for…
Detecting out-of-context objects using contextual cues
Manoj Acharya, Anirban Roy, Kaushik Koneripalli +3
This paper presents an approach to detect out-of-context (OOC) objects in an image. Given an image with a set of objects, our goal is to determine if an object is inconsistent with…
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Ramneet Kaur, Susmit Jha, Anirban Roy +4
Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence…
Detecting OODs as datapoints with High Uncertainty
Ramneet Kaur, Susmit Jha, Anirban Roy +3
Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on out-of-distribution inputs (OODs). This limitation is one of the key challenges…
Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs
Ramneet Kaur, Susmit Jha, Anirban Roy +2
Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on out-of-distribution (OOD) inputs. This limitation is one of the key challenges i…
MISA: Online Defense of Trojaned Models using Misattributions
Panagiota Kiourti, Wenchao Li, Anirban Roy +2
Recent studies have shown that neural networks are vulnerable to Trojan attacks, where a network is trained to respond to specially crafted trigger patterns in the inputs in specif…
Detecting Trojaned DNNs Using Counterfactual Attributions
Karan Sikka, Indranil Sur, Susmit Jha +2
We target the problem of detecting Trojans or backdoors in DNNs. Such models behave normally with typical inputs but produce specific incorrect predictions for inputs poisoned with…
Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment
Samyak Datta, Karan Sikka, Anirban Roy +3
We address the problem of grounding free-form textual phrases by using weak supervision from image-caption pairs. We propose a novel end-to-end model that uses caption-to-image ret…
Understanding Visual Ads by Aligning Symbols and Objects using Co-Attention
Karuna Ahuja, Karan Sikka, Anirban Roy +1
We tackle the problem of understanding visual ads where given an ad image, our goal is to rank appropriate human generated statements describing the purpose of the ad. This problem…