Study finds internal view preferred over external view in defining startups.
problem Insufficient understanding of how to start a new business.
method Empirical study with 701 entrepreneurs reflecting on nine resource categories and three business mission elements.
result Tendency to favor internal view over external view, stronger in stable economies.
Study evaluates human capital's role in Moroccan hotels' value creation.
problem Limited research on human capital's role in value creation.
method Linear regression analysis using hotel annual reports and a human capital scale.
result Human capital positively impacts value creation in Moroccan hotels.
A novel GAN approach for multiview learning with missing data.
problem Learning from multiview data with missing observations.
method Biconditional GAN with two generators and a discriminator for conditional generation of missing views.
result The discriminator achieves significant classification performance and generators learn missing views without external resources.
The increasing complexity of the power grid, due to higher penetration of distributed resources and the growing availability of interconnected, distributed metering devices re- quires novel tools for providing a unified and consistent view of the system. A computational framework for power systems data fusion, based on…
Adaptive kernel model for multi-view learning reduces overfitting and scales with data.
problem Overfitting and high computational cost in multi-view learning.
method Bayesian framework, Dirichlet process Gaussian mixtures, random Fourier features, max-margin constraints, MCMC sampler.
result Adaptive learning of shift-invariant kernels from data.
Paper addresses federated learning in edge computing with resource constraints.
problem Learning models from edge data without centralizing raw data.
method Adaptive Federated Learning with gradient descent and control algorithm.
result Proposed algorithm minimizes loss function with optimal performance.
A new method improves RVFL networks for resource-efficient machine learning.
problem Deploying machine learning on edge devices with limited resources.
method Density encoding and hyperdimensional computing operations.
result The proposed method achieves higher accuracy and lower energy consumption.
Modeling alignment as resource-limited cognitive processes, researchers derive performance bounds.
problem Systematic deviations in feedback-based alignment of large language models.
method Modeling alignment as a two-stage cascade U o H o Y U o H o Y U oH o Y given S S S , with cognitive and total capacities. result Capacity-coupled Alignment Performance Interval derived from Fano and PAC-Bayes bounds.
Bias is essential for machine learning success, quantifiable and conserved.
problem The necessity and quantification of bias in machine learning success.
method Quantifying bias relative to possible datasets and demonstrating its role in increasing success probability.
result Bias is a conserved quantity and essential for favorably biasing towards a fixed target.
Mirror Descent framework for efficient neural network quantization.
problem Efficiently quantize large neural networks for resource-limited devices.
method Introduce Mirror Descent framework for constrained optimization of quantized neural networks.
result Mirror Descent framework achieves state-of-the-art performance in quantized neural networks.
Proposes DMVST-Net for taxi demand prediction.
problem Improving taxi demand prediction for smart city resource allocation.
method Deep Multi-View Spatial-Temporal Network (DMVST-Net) combining LSTM, CNN, and semantic views.
result Demonstrates effectiveness over state-of-the-art methods on large-scale taxi demand data.
Unified framework for multi-view autoencoders simplifies model implementation and evaluation.
problem Inconsistent notation and implementation of multi-view autoencoders.
method Unified mathematical framework, documentation, and Python library.
result Comparable or superior performance in benchmarking experiments.
Paper proposes a new method for quantizing channel state information to optimize resource allocation.
problem Optimizing resource allocation for a receiver sending quantized channel state information to a transmitter.
method Introducing a task-oriented approach where the receiver sends the right amount of information to the transmitter.
result Analytical solution for optimal task-oriented CSI quantizer found for a specific energy-efficient power control problem.
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…
Study capacity constraints in continual learning with a simple model.
problem Understanding optimal resource allocation for agents with limited memory and compute resources.
method Analyzes a capacity-constrained linear-quadratic-Gaussian (LQG) sequential prediction problem and demonstrates optimal capacity allocation strategies.
result Derives a solution to the capacity-constrained LQG sequential prediction problem and shows how to optimally allocate capacity across sub-problems in the steady state.
This paper tackles unpaired data in multi-view learning, proposing a new framework and models.
problem Handling unpaired data in multi-view learning, which is more common than paired data.
method Generalized uncorrelated multi-view subspace learning framework with successive alternating approximation (SAA) method.
result Proposed models perform competitively or better than baselines in multi-view feature extraction and multi-modality classification.
Researchers develop a framework for quantum machine learning models.
problem Comparing quantum machine learning models and their resource requirements.
method Constructive framework of linear quantum models using quantum information theory.
result Linear quantum models require exponentially more qubits than data re-uploading models for certain learning tasks.
Sequence-to-sequence models predict resource usage for co-scheduled jobs in data centers.
problem Challenges in co-scheduling jobs due to resource interference and inefficiencies.
method Sequence-to-sequence models based on recurrent neural networks for workload interference prediction.
result Models accurately forecast resource usage trends from job profiles, improving scheduling decisions.
Resource allocation improved using machine learning from terminal positions.
problem Optimizing resource allocation in next-gen wireless systems with fast-changing channel conditions.
method Supervised machine learning using position information of mobile terminals.
result Coordinates-based resource allocation performs similarly to traditional CSI-based methods.
Pareto law, which states that wealth distribution in societies have a power-law tail, has been a subject of intensive investigations in statistical physics community. Several models have been employed to explain this behavior. However, most of the agent based models assume the conservation of number of agents and wealt…
Optimizes resource allocation for virtualized network functions based on performance profiles.
problem Mapping SLA performance requirements to dynamic virtualized infrastructure resources.
method Profile-based resource allocation using VNF performance datasets and machine learning models.
result A method to predict and recommend optimal resource allocation for network services.
This study compares global vs local observation and action representations for DRL in RTS games.
problem Improving Deep Reinforcement Learning performance in RTS games.
method Comparing two observation and action representations in μRTS.
result Local representation outperforms global representation in resource harvesting tasks.
This paper presents an assessment of global economic energy potentials for all major natural energy resources. This work is based on both an extensive literature review and calculations using natural resource assessment data. Economic potentials are presented in the form of cost-supply curves, in terms of energy flows …
We propose to study market efficiency from a computational viewpoint. Borrowing from theoretical computer science, we define a market to be \emph{efficient with respect to resources S S S } (e.g., time, memory) if no strategy using resources S S S can make a profit. As a first step, we consider memory- m m m strategies whose a…
Some agent-based models for growth and allocation of resources are described. The first class considered consists of conservative models, where the number of agents and the size of resources are constant during time evolution. The second class is made up of multiplicative noise models and some of their extensions to co…
This paper uses reinforcement learning to predict and allocate resources for coexisting wireless systems.
problem Coexistence of wireless systems in industrial environments leads to resource conflicts.
method Reinforcement learning, specifically deep Q-networks and double deep Q-networks, for resource allocation prediction.
result A prediction accuracy of at least 98% can be achieved in coexistence scenarios.
This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.
problem Allocating heterogeneous resources among multiple agents in a decentralized manner.
method Liquid-Graph-Time Clustering-IPPO, integrating dynamic cluster consensus.
result LGTC-IPPO achieves more stable rewards, better coordination, and robust performance.
Develops geometry for Lotka-Volterra model of species competition.
problem Population dynamics of competing species.
method Least squares variational method, Lagrange-Hamilton geometry.
result Jacobi stability discussed for the Lotka-Volterra system.
A smart method predicts and optimizes decisions online with resource constraints.
problem Online decision-making with resource constraints.
method Combines prediction and optimization with dual update using mirror descent.
result Regret bounds and convergence rates for general convex feasible regions.
Automated tool reduces FPGA inference latency to 5 μs for deep neural networks.
problem Deploying ultra low-latency, low-power deep neural networks on FPGAs.
method Extending hls4ml library, using model compression techniques like pruning and quantization-aware training.
result Achieved inference latency of 5 μs with 97% resource reduction.
This survey reviews Heterogeneous Representation Learning (HRL) for diverse data types.
problem Challenges in learning from heterogeneous data types.
method Unified learning framework for modeling various learning settings.
result Unified mathematical framework for multi-view, transfer, privileged, and multi-task learning.
CNN-DTW system improves keyword spotting in under-resourced languages.
problem Keyword spotting in nearly zero-resource languages.
method Multilingual bottleneck features, CNN-DTW, DTW template matching, convolutional neural network.
result Multilingual BNFs improve CNN-DTW by 10.9%.
This paper improves translation of rare words in under-resourced languages.
problem Translation of out-of-vocabulary words in low-resource languages.
method Developed neural and non-neural context models for target-language context integration.
result Neural language models with wide context are most effective in disambiguating OOV word translations.
Proposes a new framework for resource-limited recommendation.
problem Resource constraints affect user choices in recommendation tasks.
method Interest-behavior multiplicative network with MRRNNs and resource-limited branch.
result Framework effectively predicts user interactions considering resource limitations.
Boosting classifiers improve accuracy with noisy inputs.
problem Noisy communication or computation degrades boosting classifier accuracy.
method Optimize resource allocation for base classifiers based on importance metrics.
result Optimized noisy boosting classifiers are more robust than bagging.
HATCH learns optimal recommendations with resource constraints.
problem Resource-constrained recommendation systems.
method Hierarchical adaptive contextual bandits with adaptive resource allocation.
result HATCH achieves a regret bound of O ( T ) O(\sqrt{T}) O ( T ) . The paper evaluates index-based allocation policies using data from randomized control trials.
problem Evaluating index-based allocation policies in resource-scarce scenarios.
method Using data from randomized control trials, the paper introduces an efficient estimator and methods for computing asymptotically correct confidence intervals.
result Valid statistical conclusions can be drawn for index-based allocation policies.
Paper proposes DP-PASGD for efficient, private IoT learning.
problem Privacy and resource constraints in IoT.
method Differentially private federated learning (DP-PASGD) for resource-constrained IoT.
result DP-PASGD achieves efficient training while maintaining privacy.
Deep RL methods improve resource allocation in uncertain environments.
problem Optimizing resource allocation in dynamic, uncertain environments.
method Developed three DDPG-based approaches to handle constraints and combinatorial action spaces.
result Demonstrated improved performance over existing methods on real and semi-real data.
Enhances multi-project scheduling with multiple priority rules.
problem Resource allocation in multi-project scheduling with limited time and resources.
method Simulation-based approach using composite priority rules.
result Increased probability of finding schedules with shortest duration.
RL optimizes resource allocation in MG by balancing experience and exploration.
problem Optimal resource allocation in competitive scenarios.
method Introduced RL to MG, allowing dynamic strategy adjustment based on experience and expected rewards.
result Achieves optimal resource coordination by balancing exploitation and exploration.
Unified formula for arbitrary liquidity operations in weighted AMMs
problem Decentralized resource allocation in intelligent transportation systems
method Weighted invariant adapted from Balancer-type AMMs
result Unified formula for four resource allocation operations
Framework for online resource allocation using social welfare functions.
problem Optimal allocation of resources over time steps in a population.
method Confidence sequence framework for SWF-based online learning and inference, valid for any monotonic, concave, and Lipschitz-continuous SWF.
result Achieves near-optimal regret of i l d e O ( n + n k T ) ilde{O}(n+\sqrt{nkT}) i l d e O ( n + nk T ) for SWF-agnostic algorithm SWF-UCB. New loss function reduces outage probability in ML-assisted resource allocation.
problem Minimizing outage probability in ML-assisted resource allocation systems.
method Developed a novel loss function and trained an ML model to address the outage probability challenge.
result Exact and asymptotic expressions for the system's outage probability were established.
Meta-DRL improves resource allocation in O-RAN networks.
problem Dynamic resource allocation in O-RAN networks.
method Meta Deep Reinforcement Learning (Meta-DRL) inspired by MAML.
result 19.8% improvement in network management performance.
This paper optimizes revenue and resource balance in network revenue management.
problem Maximizing revenue while ensuring fair resource consumption across different suppliers.
method Introduces a regularized revenue objective and a primal-dual UCB algorithm for continuous prices and balancing.
result Achieves a worst-case regret of O ~ ( N 5 / 2 T ) \widetilde O(N^{5/2}\sqrt{T}) O ( N 5/2 T ) for revenue maximization and balancing. This paper optimizes resource allocation for crowdsourced live streaming to improve viewer experience and reduce costs.
problem Improving viewer quality of experience (QoE) in crowdsourced live streaming.
method A prediction-driven resource allocation framework using machine learning to predict viewer numbers and proactively allocate resources.
result Maximizes viewer QoE and minimizes resource allocation costs through precise resource provisioning.
Lotteries improve resource allocation for players with complex preferences.
problem Improving resource allocation for players with cumulative-prospect-theoretic preferences.
method Formulate an optimization problem to find optimal lottery allocations, prove existence of equilibrium prices, and derive qualitative features of optimal lottery structures.
result Lotteries can increase aggregate utility over deterministic allocations for players with cumulative-prospect-theoretic preferences.