arXiv research

PePR scores assess DL model performance per resource unit, promoting smaller, more efficient models.

Generative profiling improves real-time task timing for varied resource contexts.

Deep learning enhances water resources management through data analysis.

Paper benchmarks Bengali language classification tasks using MConv-LSTM network.

Review of efficient neural networks for TinyML on resource-constrained devices.

Study develops a smart contract framework for efficient and fair resource allocation.

We derive the most probable distribution of resources for a simple society. We find that a probabilistic analysis forbids both too much and too less equity, and selects instead a minimally ordered state. We give the detailed calculations for a special model where the population and resources are fixed, and resources ar…

Cost-benefit analysis often assumes accurate estimates, but this study finds significant inaccuracies.

Paper tackles non-monotonic resource utilization in sequential decision-making.

RL optimizes resource allocation in MG by balancing experience and exploration.

Study non-stationary bandits with resource constraints.

There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data. Despite its advantages in data privacy-preserving, Federated Learning (FL) still has …

Meta-DRL improves resource allocation in O-RAN networks.

Range penalization enhances statistical accuracy and resource efficiency in federated learning.

New loss function reduces outage probability in ML-assisted resource allocation.

Study compares LSTM and ANN architectures for forex prediction, finding ANN more efficient.

Classification tasks are common across many fields and applications where the decision maker's action is limited by resource constraints. In direct marketing only a subset of customers is contacted; scarce human resources limit the number of interviews to the most promising job candidates; limited donated organs are pr…

This work tackles resource allocation in asynchronous and stochastic systems.

Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the unde…

ATA optimizes task allocation in distributed machine learning.

Study efficient resource allocation for detecting extreme values.

Framework for online resource allocation using social welfare functions.

We introduce a simple analysis of the structural complexity of infinite-memory processes built from random samples of stationary, ergodic finite-memory component processes. Such processes are familiar from the well known multi-arm Bandit problem. We contrast our analysis with computation-theoretic and statistical infer…

Improved resource allocation method reduces procurement costs.

learn2mix trains neural nets faster by adjusting class proportions dynamically.

Data aggregation improves HAC for resource-constrained systems.

Paper proposes DP-PASGD for efficient, private IoT learning.

Nucleosome positioning is an important process required for proper genome packing and its accessibility to execute the genetic program in a cell-specific, timely manner. In the recent years hundreds of papers have been devoted to the bioinformatics, physics and biology of nucleosome positioning. The purpose of this rev…

HATCH learns optimal recommendations with resource constraints.

Many hyperparameter optimization (HyperOpt) methods assume restricted computing resources and mainly focus on enhancing performance. Here we propose a novel cloud-based HyperOpt (CHOPT) framework which can efficiently utilize shared computing resources while supporting various HyperOpt algorithms. We incorporate conven…

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) an…

Sentiment analysis is a task that may suffer from a lack of data in certain cases, as the datasets are often generated and annotated by humans. In cases where data is inadequate for training discriminative models, generate models may aid training via data augmentation. Generative Adversarial Networks (GANs) are one suc…

Optimizes CNN architectures by analyzing receptive fields without training.

The main purpose of this paper is to formalize the modelling process, analysis and mathematical definition of corruption when entering into a contract between principal agent and producers. The formulation of the problem and the definition of concepts for the general case are considered. For definiteness, all calculati…

Startups is a popular phenomenon that has a significant impact on global economy growth, innovation and society development. However, there is still insufficient understanding about startups, particularly, how to start a new business in the relation to consequent performance. Toward this knowledge, we have performed an…

A generalized gamification framework is introduced as a form of smart infrastructure with potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. The proposed framework enables a Human-Centric Cyber-Physical System using an interface to allow building managers to interact wi…

This study analyzes quantization in deep learning models using statistical physics methods.

The stability analysis of socioeconomic systems has been centered on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. …

Hybrid QNN-LSTM predicts financial stock market trends using quantum computing.

Communication networks shared by many users are a widespread challenge nowadays. In this paper we address several aspects of this challenge simultaneously: learning unknown stochastic network characteristics, sharing resources with other users while keeping coordination overhead to a minimum. The proposed solution comb…

Investigates upsampling vs. upweighting for balanced training on skewed datasets.

Compact neural network for ECG classification reduces resource needs.

Post-training quantization saves resources for neural networks.

This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.

Study user engagement in mobile health apps for health workers in resource-poor settings.

Deep learning benchmarks ECG analysis with strong performance.

New models improve morpheme segmentation in low-resource languages.

A new algorithm for resource-aware multi-armed bandits minimizes regret.