13 citations · 64 across the 25 of their papers we have counts for
5 papers · 1 filter
Grounding Human-to-Vehicle Advice for Self-driving Vehicles
Jinkyu Kim, Teruhisa Misu, Yi-Ting Chen +2
Recent success suggests that deep neural control networks are likely to be a key component of self-driving vehicles. These networks are trained on large datasets to imitate human a…
Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data
Vidyasagar Sadhu, Teruhisa Misu, Dario Pompili
Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safety-critical applications. An AI system should be intelligent enough to detect such s…
Unsupervised Data Uncertainty Learning in Visual Retrieval Systems
Ahmed Taha, Yi-Ting Chen, Teruhisa Misu +2
We introduce an unsupervised formulation to estimate heteroscedastic uncertainty in retrieval systems. We propose an extension to triplet loss that models data uncertainty for each…
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems
Ahmed Taha, Yi-Ting Chen, Xitong Yang +2
We cast visual retrieval as a regression problem by posing triplet loss as a regression loss. This enables epistemic uncertainty estimation using dropout as a Bayesian approximatio…
Boosting Standard Classification Architectures Through a Ranking Regularizer
Ahmed Taha, Yi-Ting Chen, Teruhisa Misu +2
We employ triplet loss as a feature embedding regularizer to boost classification performance. Standard architectures, like ResNet and Inception, are extended to support both losse…