33,925 research outputs found
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning
Quadratic Unconstrained Binary Optimization (QUBO) is a generic technique to
model various NP-hard combinatorial optimization problems in the form of binary
variables. The Hamiltonian function is often used to formulate QUBO problems
where it is used as the objective function in the context of optimization.
Recently, PI-GNN, a generic scalable framework, has been proposed to address
the Combinatorial Optimization (CO) problems over graphs based on a simple
Graph Neural Network (GNN) architecture. Their novel contribution was a generic
QUBO-formulated Hamiltonian-inspired loss function that was optimized using
GNN. In this study, we address a crucial issue related to the aforementioned
setup especially observed in denser graphs. The reinforcement learning-based
paradigm has also been widely used to address numerous CO problems. Here we
also formulate and empirically evaluate the compatibility of the
QUBO-formulated Hamiltonian as the generic reward function in the Reinforcement
Learning paradigm to directly integrate the actual node projection status
during training as the form of rewards. In our experiments, we observed up to
44% improvement in the RL-based setup compared to the PI-GNN algorithm. Our
implementation can be found in
https://github.com/rizveeredwan/learning-graph-structure
Continual Learning with Gated Incremental Memories for sequential data processing
The ability to learn in dynamic, nonstationary environments without
forgetting previous knowledge, also known as Continual Learning (CL), is a key
enabler for scalable and trustworthy deployments of adaptive solutions. While
the importance of continual learning is largely acknowledged in machine vision
and reinforcement learning problems, this is mostly under-documented for
sequence processing tasks. This work proposes a Recurrent Neural Network (RNN)
model for CL that is able to deal with concept drift in input distribution
without forgetting previously acquired knowledge. We also implement and test a
popular CL approach, Elastic Weight Consolidation (EWC), on top of two
different types of RNNs. Finally, we compare the performances of our enhanced
architecture against EWC and RNNs on a set of standard CL benchmarks, adapted
to the sequential data processing scenario. Results show the superior
performance of our architecture and highlight the need for special solutions
designed to address CL in RNNs.Comment: Accepted as a conference paper at 2020 International Joint Conference
on Neural Networks (IJCNN 2020). Part of 2020 IEEE World Congress on
Computational Intelligence (IEEE WCCI 2020
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