
-
Learning-Based Distributed Random Access for Multi-Cell IoT Networks with NOMA
Non-orthogonal multiple access (NOMA) is a key technology to enable mass...
read it
-
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data
Developing high-performing predictive models for large tabular data sets...
read it
-
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
An autotuning is an approach that explores a search space of possible im...
read it
-
Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks
Wide area networking infrastructures (WANs), particularly science and re...
read it
-
Graph Neural Network Architecture Search for Molecular Property Prediction
Predicting the properties of a molecule from its structure is a challeng...
read it
-
Meta Continual Learning via Dynamic Programming
Meta-continual learning algorithms seek to rapidly train a model when fa...
read it
-
Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning
We focus on the problem of how to achieve online continual learning unde...
read it
-
A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes
The canonical solution methodology for finite constrained Markov decisio...
read it
-
Towards On-Chip Bayesian Neuromorphic Learning
If edge devices are to be deployed to critical applications where their ...
read it
-
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting
Highway traffic modeling and forecasting approaches are critical for int...
read it
-
Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMA
In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) techni...
read it
-
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems
Quantum computing is a computational paradigm with the potential to outp...
read it
-
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
Quantum computing exploits basic quantum phenomena such as state superpo...
read it
-
Value-Added Chemical Discovery Using Reinforcement Learning
Computer-assisted synthesis planning aims to help chemists find better r...
read it
-
Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images
Strong gravitational lensing of astrophysical sources by foreground gala...
read it
-
Graph-Partitioning-Based Diffusion Convolution Recurrent Neural Network for Large-Scale Traffic Forecasting
Traffic forecasting approaches are critical to developing adaptive strat...
read it
-
MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles
Optimal engine operation during a transient driving cycle is the key to ...
read it
-
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models
Rapid simulations of advection-dominated problems are vital for multiple...
read it
-
Balsam: Automated Scheduling and Execution of Dynamic, Data-Intensive HPC Workflows
We introduce the Balsam service to manage high-throughput task schedulin...
read it
-
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research
Cancer is a complex disease, the understanding and treatment of which ar...
read it
-
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning
The ability to learn and adapt in real time is a central feature of biol...
read it
-
Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout
As neural networks have begun performing increasingly critical tasks for...
read it