On-device research index

arXiv research

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.

169,291 papers · 148 categories

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13 results for “Domain-Aware”

Domain-Aware Dynamic Networkscs.LG

DDN dynamically combines weights for domain-specific models, improving performance.

problem Limited compute resources for edge devices.

method Domain-aware Dynamic Network (DDN) that combines specialized weights based on input domain.

result DDN achieves up to 2.6% higher AP50 than a static network on the BDD100K benchmark.

Domain-aware Dynamic Network Edge Devices Deep Learning Object Detection

2019-11-26abs ↗pdf ↗

Domain Aware Markov Logic Networkscs.LG

DA-MLNs improve MLNs by scaling feature weights based on domain size.

problem Extreme probabilities in MLNs when testing on different domain sizes.

method DA-MLNs divide ground feature weights by a scaling factor that depends on the number of connections.

result DA-MLNs achieve significantly higher accuracy on domains with different sizes compared to standard MLNs.

Markov Logic Networks Domain-Aware Feature Scaling Probability Extreme Accuracy Improvement

2018-07-03abs ↗pdf ↗

Graph Node Embeddings using Domain-Aware Biased Random Walkscs.LG

New graph embedding method uses domain-specific knowledge.

problem Lack of semantic information in existing graph embedding approaches.

method Domain-aware biased random walks to incorporate semantic information.

result Embeddings achieve equal or greater accuracy than domain-independent methods.

graph embeddings semantic information domain-aware random walks machine learning

2019-08-08abs ↗pdf ↗

Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributionscs.LG

Algorithm improves model performance on shifted concepts without retraining.

problem Improving model performance on shifted concepts with limited source data.

method Model consolidation of intermediate internal distributions after adaptation.

result Effective improvement in model performance on shifted concepts.

concept shift domain adaptation internal distribution model consolidation

2020-07-01abs ↗pdf ↗

From Time Series to Euclidean Spaces: On Spatial Transformations for Temporal Clusteringcs.LG

Paper proposes a novel approach to improve temporal clustering of time series data.

problem Challenges in clustering temporal data with varying sampling rates and high dimensionality.

method Transform time series into Euclidean space using similarity measures, then use CNN-GRU autoencoder for latent representation.

result Approach outperforms existing methods by up to 32% on various time series datasets.

temporal clustering time series CNN-GRU autoencoder clustering algorithms

2020-10-02abs ↗pdf ↗

Recurrent Graph Tensor Networks: A Low-Complexity Framework for Modelling High-Dimensional Multi-Way Sequencecs.LG

Develops a tensor network framework to reduce RNN complexity for high-dimensional sequence modeling.

problem Exponential parameter growth in RNNs for large multidimensional data.

method Embeds a multi-linear graph filter in a tensor network architecture to approximate RNN hidden states.

result Demonstrates superior performance and reduced complexity compared to traditional RNNs.

Recurrent Neural Networks Tensor Networks Graph Filters Sequence Modeling Multi-Way Data

2020-09-18abs ↗pdf ↗

Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware Gaussian Processesstat.ML

Advanced kernels improve Gaussian process accuracy by incorporating domain knowledge.

problem Improving function approximation accuracy in Gaussian processes.

method Advanced kernel designs that enforce specific function properties (symmetry, periodicity) and non-stationarity.

result Advanced kernels significantly enhance function approximation accuracy and relevance.

Gaussian processes kernel designs function approximation domain knowledge symmetry

2021-02-05abs ↗pdf ↗

Incorporating Interactive Facts for Stock Selection via Neural Recursive ODEsq-fin.ST

Stock selection improved with a novel neural model capturing continuous stock dynamics.

problem Lack of continuous stock dynamics prediction and implicit cross-domain dependencies.

method StockODE, a latent variable model with NRODEs and hierarchical hypergraph for continuous stock volatility and inter-domain dependencies.

result Significantly outperforms baselines, improving Sharpe Ratio by up to 18.57%.

stock selection neural networks continuous dynamics latent variables Gaussian prior

2022-10-28abs ↗pdf ↗

Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modellingq-fin.CP

GRTR framework uses graph regularization to improve financial forecasting.

problem High computational costs and economic domain knowledge loss in tensor models.

method Graph-Regularized Tensor Regression (GRTR) framework incorporating economic domain knowledge.

result Improved performance in multi-way financial forecasting with reduced computational costs.

Tensor Regression Graph Regularization Financial Forecasting Interpretability Economic Domain Knowledge

2022-10-26abs ↗pdf ↗

AI Trading: Evaluating Large Language Models for Technical Market Analysiscs.LG

This paper evaluates LLMs for technical market analysis, finding GPT-4 Turbo and FinGPT outperform passive benchmarks.

problem Evaluating LLMs for technical market analysis in financial markets.

method Structured evaluation of five LLMs (GPT-4 Turbo, Claude 3 Opus, Gemini 1.5 Pro, Llama 3 70B, FinGPT) on four tasks: candlestick pattern recognition, directional signal generation, backtesting, and financial report comprehension.

result GPT-4 Turbo and FinGPT outperform passive benchmarks in simulated backtesting, with GPT-4 Turbo achieving the highest annualized return and Sharpe ratio.

Large Language Models Technical Market Analysis Financial Markets Backtesting Candlestick Patterns

2026-07-16abs ↗pdf ↗

SX-GeoTree: Self-eXplaining Geospatial Regression Tree Incorporating the Spatial Similarity of Feature Attributionscs.LG

SX-GeoTree improves spatially coherent explanations in geospatial regression trees.

problem Capturing spatial dependence and producing robust explanations in tabular prediction models.

method Integrates three objectives: impurity reduction, spatial residual control, and explanation robustness via modularity maximization on a consensus similarity network.

result Improves residual spatial evenness and doubles attribution consensus (modularity: Fujian 0.19 vs 0.09; Seattle 0.10 vs 0.05).

geospatial regression spatial similarity explainable AI robustness modularity maximization

2025-11-25abs ↗pdf ↗

Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)astro-ph.IM

Predict missing and future data points in light curves using scalable Gaussian Processes.

problem Gappy time-series data from commercial cameras confound light curve prediction.

method MuyGPs, a scalable framework for hyperparameter estimation of Gaussian Processes using nearest neighbors sparsification and local cross-validation.

result MuyGPs enable accurate prediction of missing and future data points in light curves.

light curves Gaussian Processes scalable sparsification local cross-validation

2022-08-31abs ↗pdf ↗

Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networksstat.ML

Novel parallel GNN predicts protein-ligand interactions with high accuracy.

problem Accurate prediction of protein-ligand interactions for drug design.

method Parallel Graph Neural Networks (GNN) integrating 3D structural data.

result GNN achieves high accuracy in predicting binary interactions and activity.

Graph Neural Networks Protein-Ligand Interactions 3D Structural Data Drug Design Interpretability

2021-11-30abs ↗pdf ↗