Search Results for author:
Found 12 papers, 2 papers with code
On the Scalability of GNNs for Molecular Graphs
However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to the lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures.
Effects of Conservatism on Offline Learning
The proposed answer studies conservatism in light of value function optimization, approximate objectives that upper bound underestimations and behavior cloning as auxilary regularization objective.
Surprise Minimizing Multi-Agent Learning with Energy-based Models
Multi-Agent Reinforcement Learning (MARL) has demonstrated significant success by virtue of collaboration across agents.
TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution
We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Energy-based Surprise Minimization for Multi-Agent Value Factorization
(2) EMIX highlights a practical use of energy functions in MARL with theoretical guarantees and experiment validations of the energy operator.
Maximum Mutation Reinforcement Learning for Scalable Control
Advances in Reinforcement Learning (RL) have demonstrated data efficiency and optimal control over large state spaces at the cost of scalable performance.
Continuous sign language recognition from wearable IMUs using deep capsule networks and game theory
Sign Language is used by the deaf community all over world.
Classification of Hand Gestures from Wearable IMUs using Deep Neural Network
The predicted outputs are analyzed in the form of classification accuracies which are then compared to the conventional classification schemes of SVM and kNN.
Transfer Learning for sEMG-based Hand Gesture Classification using Deep Learning in a Master-Slave Architecture
Recent advancements in diagnostic learning and development of gesture-based human machine interfaces have driven surface electromyography (sEMG) towards significant importance.
Dual Stage Classification of Hand Gestures using Surface Electromyogram
The performance of the proposed approach is compared to the conventional single stage classification approach in terms of classification accuracies.
Activity Detection from Wearable Electromyogram Sensors using Hidden Markov Model
Classified outputs are compared with the transition regions in a stimulus given to the subject to perform the activity.
Convolutional Neural Network Array for Sign Language Recognition using Wearable IMUs
The proposed work presents a novel one-dimensional Convolutional Neural Network (CNN) array architecture for recognition of signs from the Indian sign language using signals recorded from a custom designed wearable IMU device.
