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Found 13 papers, 2 papers with code
FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing
However, the resulting model still consumes a large amount of GPU memory.
Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems
In this paper, we investigate deep reinforcement learning to control traffic lights, and both theoretical analysis and numerical experiments show that the intelligent behavior ``greenwave" (i. e., a vehicle will see a progressive cascade of green lights, and not have to brake at any intersection) emerges naturally a grid road network, which is proved to be the optimal policy in an avenue with multiple cross streets.
Fair and Efficient Distributed Edge Learning with Hybrid Multipath TCP
The bottleneck of distributed edge learning (DEL) over wireless has shifted from computing to communication, primarily the aggregation-averaging (Agg-Avg) process of DEL.
Regenerative Particle Thompson Sampling
Particle Thompson sampling (PTS) is an approximation of Thompson sampling obtained by simply replacing the continuous distribution by a discrete distribution supported at a set of weighted static particles.
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
In this paper, we present a scalable and elastic library ElegantRL-podracer for cloud-native deep reinforcement learning, which efficiently supports millions of GPU cores to carry out massively parallel training at multiple levels.
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance
Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading.
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.
Privacy-preserving Decentralized Aggregation for Federated Learning
In this paper, we develop a privacy-preserving decentralized aggregation protocol for federated learning.
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning
Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization.
Deep Reinforcement Learning for Intelligent Transportation Systems
Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe.
Practical Deep Reinforcement Learning Approach for Stock Trading
We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return.
Transform-Based Multilinear Dynamical System for Tensor Time Series Analysis
We propose a novel multilinear dynamical system (MLDS) in a transform domain, named $\mathcal{L}$-MLDS, to model tensor time series.
Multidimensional Data Tensor Sensing for RF Tomographic Imaging
The recently proposed transform-based tensor model is more appropriate for sensory data processing, as it helps exploit the geometric structures of the three-dimensional target and improve the recovery precision.
