2 citations · 4 across the 15 of their papers we have counts for
15 papers
Adversarially Robust Abductive Fusion of Pre-trained Transformer-based Perception Models
Mario Leiva, Yue Ma, Qinru Qiu +2
Deploying pre-trained perception models in novel environments degrades their accuracy under distributional shift, and assembling them alone does not recover it: combiners such as m…
EntailLLM: Verifying Agent-Generated Vulnerability Discovery Paths with Domain Knowledge via Logic Programming
Kaustuv Mukherji, Jaikrishna Manojkumar Patil, Colton Payne +4
Large language model agents are increasingly used to analyze potential software vulnerabilities, but their outputs can silently violate domain knowledge, limiting their reliability…
Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation
Joshua Shay Kricheli, Alexander Lawrence Reid, Soumajyoti Sarkar +2
Neural scaling laws approximate a language model's loss as a power-law function of parameter count and token count . Following Chinchilla-style compute-optimal training, man…
Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoning
Kaustuv Mukherji, Jaikrishna Manojkumar Patil, Dyuman Aditya +5
We introduce Lattice Annotated Temporal (LAT) Logic, an extension of Generalized Annotated Logic Programs (GAPs) that incorporates temporal reasoning and supports open-world semant…
Error Detection and Correction for Interpretable Mathematics in Large Language Models
Yijin Yang, Cristina Cornelio, Mario Leiva +1
Recent large language models (LLMs) have demonstrated the ability to perform explicit multi-step reasoning such as chain-of-thought prompting. However, their intermediate steps oft…
Machine Learning Model Integration with Open World Temporal Logic for Process Automation
Dyuman Aditya, Colton Payne, Mario Leiva +1
Recent advances in Machine Learning (ML) have produced models that extract structured information from complex data. However, a significant challenge lies in translating these perc…
Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments
Mario Leiva, Noel Ngu, Joshua Shay Kricheli +6
The deployment of pre-trained perception models in novel environments often leads to performance degradation due to distributional shifts. Although recent artificial intelligence a…
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection
Aditya Taparia, Noel Ngu, Mario Leiva +6
Although fusing multiple sensor modalities can enhance object detection performance, existing fusion approaches often overlook subtle variations in environmental conditions and sen…
Multiple Distribution Shift -- Aerial (MDS-A): A Dataset for Test-Time Error Detection and Model Adaptation
Noel Ngu, Aditya Taparia, Gerardo I. Simari +5
Machine learning models assume that training and test samples are drawn from the same distribution. As such, significant differences between training and test distributions often l…
Abduction of Domain Relationships from Data for VQA
Al Mehdi Saadat Chowdhury, Paulo Shakarian, Gerardo I. Simari
In this paper, we study the problem of visual question answering (VQA) where the image and query are represented by ASP programs that lack domain data. We provide an approach that…
Probabilistic Foundations for Metacognition via Hybrid-AI
Paulo Shakarian, Gerardo I. Simari, Nathaniel D. Bastian
Metacognition is the concept of reasoning about an agent's own internal processes, and it has recently received renewed attention with respect to artificial intelligence (AI) and,…
Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge
Joshua Shay Kricheli, Khoa Vo, Aniruddha Datta +2
Recent advances in Hierarchical Multi-label Classification (HMC), particularly neurosymbolic-based approaches, have demonstrated improved consistency and accuracy by enforcing cons…
Geospatial Trajectory Generation via Efficient Abduction: Deployment for Independent Testing
Divyagna Bavikadi, Dyuman Aditya, Devendra Parkar +4
The ability to generate artificial human movement patterns while meeting location and time constraints is an important problem in the security community, particularly as it enables…
Machine Learning Driven Biomarker Selection for Medical Diagnosis
Divyagna Bavikadi, Ayushi Agarwal, Shashank Ganta +4
Recent advances in experimental methods have enabled researchers to collect data on thousands of analytes simultaneously. This has led to correlational studies that associated mole…
Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning
Kaustuv Mukherji, Devendra Parkar, Lahari Pokala +3
Recent advances in reinforcement learning (RL) have shown much promise across a variety of applications. However, issues such as scalability, explainability, and Markovian assumpti…