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Found 8 papers, 1 papers with code
Continuous Learning Based Novelty Aware Emotion Recognition System
Current works in human emotion recognition follow the traditional closed learning approach governed by rigid rules without any consideration of novelty.
EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge
One of the primary challenges in emotion recognition is effectively utilizing the various cues (modalities) available in the data.
SAFER: Situation Aware Facial Emotion Recognition
To address these limitations, a novel dataset for facial emotion recognition is proposed.
AdaPool: A Diurnal-Adaptive Fleet Management Framework using Model-Free Deep Reinforcement Learning and Change Point Detection
To mitigate this problem in highly dynamic environments, we (1) adopt an online Dirichlet change point detection (ODCP) algorithm to detect the changes in the distribution of experiences, (2) develop a Deep Q Network (DQN) agent that is capable of recognizing diurnal patterns and making informed dispatching decisions according to the changes in the underlying environment.
Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach
Monopoly is a popular strategic board game that requires players to make multiple decisions during the game.
PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning
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 Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning
In this paper, we present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework that (1) dynamically generates optimal routes for each vehicle based on online demand, pricing associated with each ride, vehicle capacities and locations.
ConFoc: Content-Focus Protection Against Trojan Attacks on Neural Networks
We identify that they, including those related to the inserted triggers, contain both content (semantic information) and style (texture information), which are recognized as a whole by DNNs at testing time.
