The significant computational requirements of deep learning present a major bottleneck for its large-scale adoption on hardware-constrained IoT-devices. Here, we envision a new paradigm called EdgeAI to address major impediments associated with deploying deep networks at the edge. Specifically, we discuss the existing …
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
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48 results for “adoption challenges”
Deep learning faces adoption challenges in business analytics.
problem Adoption of deep learning in business analytics is hindered by various factors.
method Empirical study based on three industry use cases.
result Gradient boosting is recommended for structured datasets in business analytics.
Decentralized Finance (Literacy) today and in 2034: Initial Insights from Singapore and beyondq-fin.GN
Experts predict significant adoption of decentralized finance by 2034, with traditional finance adapting.
problem Adoption and integration of decentralized finance (DeFi) in financial services.
method Survey analysis using New Institutional Economics and Dynamic Capabilities Theory.
result Experts expect adoption of DeFi to rise from negligible to 43% by 2034, with traditional finance likely to embrace it.
Survey of stablecoins to reduce cryptocurrency volatility.
problem Reduction of cryptocurrency volatility during financial crises.
method Classification of stablecoin approaches and assessment of tradeoffs.
result Different stablecoin types offer varying tradeoffs and challenges.
Paper proposes a new method for predicting DER adoption with hierarchical guarantees.
problem Accurately predicting DER adoption in electric grids with uncertainty and spatial disparity.
method Multivariate Hawkes process for modeling DER adoption dynamics and split conformal prediction algorithm for hierarchical validity.
result Empirical evaluation shows superior predictive accuracy and uncertainty calibration compared to existing methods.
Study blockchain's impact on primary financial market challenges.
problem Challenges of blockchain in securities issuance and trading.
method Hybrid method combining interviews and surveys.
result Complex due diligence, mismatch, and difficult monitoring are significant challenges.
Recent results of deep convolutional networks in visual recognition challenges open the path to a whole new set of disruptive user experiences such as visual search or recommendation. The list of companies offering this type of service is growing everyday but the adoption rate and the relevancy of results may vary a lo…
Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of …
Machine learning simplifies finance, but faces challenges.
problem Adopting machine learning in finance.
method Analyzing challenges in financial services adoption.
result Challenges in finance adoption of ML.
The challenge of learning disentangled representation has recently attracted much attention and boils down to a competition using a new real world disentanglement dataset (Gondal et al., 2019). Various methods based on variational auto-encoder have been proposed to solve this problem, by enforcing the independence betw…
This paper investigates to identify the requirement and the development of machine learning-based mobile big data analysis through discussing the insights of challenges in the mobile big data (MBD). Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the …
Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the parameter posterior of a neural network via MCMC. Such challenges culminate to lack of convergence to the parameter posterior. Nevertheless, thi…
This paper reviews methods to improve AI explainability in finance.
problem Lack of explainability in AI models, especially in finance.
method Categorizes methods to improve explainability of deep learning models.
result Provides a comparative survey of methods to enhance AI explainability.
Technology Adoption and Network Externalities in Financial Systems: A Spatial-Network Approachecon.EM
Paper analyzes tech adoption in financial networks, finding key leadership and diffusion dynamics.
problem Understanding technology adoption and network effects in financial systems.
method Developed a spatial-network framework with a master equation and Feynman-Kac representation.
result Found strong support for two-regime adoption dynamics and significant leadership in network central banks.
We propose an exact slice sampler for Hierarchical Dirichlet process (HDP) and its associated mixture models (Teh et al., 2006). Although there are existing MCMC algorithms for sampling from the HDP, a slice sampler has been missing from the literature. Slice sampling is well-known for its desirable properties includin…
The paper addresses challenges in edge deep learning for IoT, proposing new directions.
problem Challenges in large-scale deep learning adoption for IoT devices.
method Unified view targeting three research directions: federated learning, data-independent deployment, and communication-aware inference.
result A network-centric approach is needed for edge intelligence.
NPE improves scalability and efficiency for ERGMs.
problem Scalability and efficiency issues in Bayesian ERGM estimation.
method Neural posterior estimation (NPE) for ERGMs using neural network density estimation.
result NPE provides more efficient and scalable inference for ERGMs.
A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new method for CT reconstruction that combines penalized weighted-least squares reconstruction (PWLS) with regularization based on a sparsifying…
Framework for automatically assessing and correcting data quality issues without domain knowledge.
problem Ensuring data quality in datasets across various domains.
method Hybrid approach combining statistical and machine learning methods.
result Effective detection and correction of missing values, duplicates, and typographical errors.
Study efficient auditing of ML fairness models.
problem Scalability of auditing ML models for fairness.
method Query-based auditing algorithms for estimating demographic parity.
result Optimal deterministic and practical randomized algorithms for fairness estimation.
Before the operation of a motor imagery based brain-computer interface (BCI) adopting machine learning techniques, a cumbersome training procedure is unavoidable. The development of a practical BCI posed the challenge of classifying single-trial EEG with a small training set. In this letter, we addressed this problem b…
CRPO solves challenging SRL problems with convergence guarantee.
problem Safe reinforcement learning with nonconvex constraints.
method Primal approach using alternating policy updates and NPG.
result Achieves convergence rate and error bound.
Fiber simplifies RL and population-based methods for distributed training.
problem Challenges in RL and population-based methods, including frequent interaction with simulations and dynamic scaling.
method Introducing Fiber, a scalable distributed computing framework.
result Significantly expands accessibility of large-scale parallel computation.
AI enhances financial forecasting with challenges in regulation and privacy.
problem Challenges in integrating AI with financial services and regulations.
method Integration of AI technologies like deep learning and reinforcement learning.
result AI improves financial forecasting but faces regulatory and privacy issues.
Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero coefficients using simple rules independently of the others. However, screening for overlapping group lasso remains an open challenge because…
New clustering method reduces data redundancy for better summaries.
problem Redundancies in data summaries limit their effectiveness in large datasets.
method Khatri-Rao clustering extends centroid-based clustering to produce more succinct summaries.
result Khatri-Rao k-Means and deep clustering frameworks produce more succinct summaries with similar accuracy.
Superior performance and ease of implementation have fostered the adoption of Convolutional Neural Networks (CNNs) for a wide array of inference and reconstruction tasks. CNNs implement three basic blocks: convolution, pooling and pointwise nonlinearity. Since the two first operations are well-defined only on regular-s…
Study analyzes smart contract adoption under bounded risk, showing stable adoption but fragile financial outcomes.
problem Understanding smart contract adoption in derivative markets under risk constraints.
method Structural theory linked with simulation and real-world validation.
result Adoption intensity is stable but profitability and service outcomes are sensitive to volatility.
Over-parameterized models, such as DeepNets and ConvNets, form a class of models that are routinely adopted in a wide variety of applications, and for which Bayesian inference is desirable but extremely challenging. Variational inference offers the tools to tackle this challenge in a scalable way and with some degree o…
Production recommendation systems rely on embedding methods to represent various features. An impeding challenge in practice is that the large embedding matrix incurs substantial memory footprint in serving as the number of features grows over time. We propose a similarity-aware embedding matrix compression method call…
In this work, we analyze the problem of adoption of mobile money in Pakistan by using the call detail records of a major telecom company as our input. Our results highlight the fact that different sections of the society have different patterns of adoption of digital financial services but user mobility related feature…
IntelligentPooling improves treatment decisions in mHealth.
problem Optimizing treatment decisions in mobile health with limited data and non-stationary responses.
method Generalized Thompson-Sampling bandit algorithms to IntelligentPooling, addressing differential response, limited data, and non-stationary responses.
result IntelligentPooling achieves 26% lower regret compared to state-of-the-art methods.
We win EVA2025 by estimating extreme precipitation events using Peaks Over Thresholds and martingale testing.
problem Estimating the probability of extreme precipitation events with limited data.
method Modeling Peaks Over Thresholds with an exponential distribution and using martingale testing for evaluation.
result Our method outperforms other approaches in estimating extreme precipitation events.
System designs for analyzing and pricing non-performing consumer credit portfolios.
problem Technical challenges in analyzing and pricing portfolios of non-performing consumer credit loans.
method Bottom-up architecture, simultaneous quantile regression, R-copula, Gaussian one-factor copula model.
result Successfully developed a methodology for analyzing credit portfolio risks of consumer loans.
Ising-like agent-based technology diffusion model: adoption patterns vs. seeding strategiesphysics.soc-ph
The well-known Ising model used in statistical physics was adapted to a social dynamics context to simulate the adoption of a technological innovation. The model explicitly combines (a) an individual's perception of the advantages of an innovation and (b) social influence from members of the decision-maker's social net…
A new method improves label propagation for unsupervised domain adaptation.
problem Improving unsupervised domain adaptation through semi-supervised learning techniques.
method Label Propagation with Augmented Anchors (ALP) for UDA.
result ALP improves over representative UDA methods and benchmarks.
Survey of LLMs in finance tasks, including adoption and performance.
problem Utilizing large language models in financial tasks.
method Review of current approaches, decision framework for adoption.
result Synthesizes state-of-the-art for LLMs in finance.
Survey on securing ML for healthcare, addressing privacy and robustness issues.
problem Security and robustness challenges in healthcare ML/DL applications.
method Overview of security and privacy methods for ML in healthcare.
result Discussion of current research challenges and future directions.
Study optimizes smart contract adoption under high demand variability using Negative Binomial models.
problem Effective supply chain management under high demand variability.
method Combines dynamic Negative Binomial demand modeling with endogenous smart contract adoption optimization.
result The NB model outperforms other benchmarks in forecasting and optimizing smart contract adoption and order quantity.
Qlib aims to integrate AI into quantitative investment.
problem Challenges in applying AI to quantitative investment.
method Design and develop Qlib to accommodate AI-driven workflow.
result Qlib realizes the potential of AI technologies in quantitative investment.
New algorithm for efficiently identifying the best arm in stochastic bandits.
problem Best arm identification in stochastic multi-armed bandits with fixed confidence.
method Sequential probability ratio tests for arm selection.
result Asymptotically optimal sample complexity and guaranteed PAC performance.
This paper develops explainable treatment policies for RPM using clinical knowledge.
problem Barriers to adoption of DHIs and lack of interpretability in purely black-box algorithms.
method Developed a pipeline for learning explainable treatment policies using clinician-informed representations.
result Policies learned from clinician-informed representations are more efficacious and efficient than black-box policies.
TreeDOX predicts chaotic systems without hyperparameter tuning.
problem Forecasting chaotic systems requires hyperparameter tuning, limiting adoption.
method TreeDOX uses time delay overembedding and Extra-Trees Regressors.
result TreeDOX achieves state-of-the-art performance on chaotic systems.
Advocates for Marr's levels of analysis to unify machine learning debates.
problem Challenges in aligning perspectives among machine learning researchers.
method Introduces Marr's levels of analysis from cognitive science and neuroscience.
result Marr's levels facilitate understanding and dissection of machine learning methods.
New copula models learn to forget dependencies, improving data representation.
problem Restrictive assumptions and poor scaling in existing copula models.
method Diffusion and flow-based copulas that progressively forget dependencies.
result Provable valid copulas at all times, superior performance in complex dependencies.
Deciding effective and timely preventive measures against complex social problems affecting relatively low income geographies is a difficult challenge. There is a strong need to adopt intelligent automation based solutions with low cost imprints to tackle these problems at larger scales. Starting with the hypothesis th…
Paper proposes counterfactual explanations for ML on multivariate time series data.
problem Lack of user trust and difficulty in debugging ML frameworks using multivariate time series data.
method Proposes a novel explainability technique for providing counterfactual explanations.
result Outperforms state-of-the-art explainability methods in metrics like faithfulness and robustness.
Paper fine-tunes LLMs for financial tasks using data fusion.
problem Improving LLMs for financial analysis tasks.
method Fine-tuned Llama3-8B and Mistral-7B using PEFT and LoRA, combined datasets for data fusion.
result Enhanced model performance across financial tasks.