5 citations · 10 across the 9 of their papers we have counts for
10 papers
Reliable Replay through Spatial Coherence in Online Continual Learning
Haixiang Sun, Jiefu Zhang, Yinghao He +4
Continually adapting models to new tasks requires retaining earlier knowledge under limited memory and computation. Experience replay addresses this challenge, but priorities based…
Pre-Deployment Complexity Estimation for Federated Perception Systems
KMA Solaiman, Shafkat Islam, Ruy de Oliveira +1
Edge AI systems increasingly rely on federated learning to train perception models in distributed, privacy-preserving, and resource-constrained environments. Before training, howev…
SAFER: Situation Aware Facial Emotion Recognition
Mijanur Palash, Bharat Bhargava
In this paper, we present SAFER, a novel system for emotion recognition from facial expressions. It employs state-of-the-art deep learning techniques to extract various features fr…
Continuous Learning Based Novelty Aware Emotion Recognition System
Mijanur Palash, Bharat Bhargava
Current works in human emotion recognition follow the traditional closed learning approach governed by rigid rules without any consideration of novelty. Classification models are t…
EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge
Mijanur Palash, Bharat Bhargava
Automatic emotion recognition has recently gained significant attention due to the growing popularity of deep learning algorithms. One of the primary challenges in emotion recognit…
AGAPECert: An Auditable, Generalized, Automated, Privacy-Enabling Certification Framework with Oblivious Smart Contracts
Servio Palacios, Aaron Ault, James V. Krogmeier +2
This paper introduces AGAPECert, an Auditable, Generalized, Automated, Privacy-Enabling, Certification framework capable of performing auditable computation on private data and rep…
AdaPool: A Diurnal-Adaptive Fleet Management Framework using Model-Free Deep Reinforcement Learning and Change Point Detection
Marina Haliem, Vaneet Aggarwal, Bharat Bhargava
This paper introduces an adaptive model-free deep reinforcement approach that can recognize and adapt to the diurnal patterns in the ride-sharing environment with car-pooling. Deep…
Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach
Trevor Bonjour, Marina Haliem, Aala Alsalem +5
Learning to adapt and make real-time informed decisions in a dynamic and complex environment is a challenging problem. Monopoly is a popular strategic board game that requires play…
PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning
Kaushik Manchella, Marina Haliem, Vaneet Aggarwal +1
The ubiquitous growth of mobility-on-demand services for passenger and goods delivery has brought various challenges and opportunities within the realm of transportation systems. A…
A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning
Marina Haliem, Ganapathy Mani, Vaneet Aggarwal +1
Significant development of ride-sharing services presents a plethora of opportunities to transform urban mobility by providing personalized and convenient transportation while ensu…