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.
An ensemble of randomized NNs improves time series forecasting accuracy.
This research improves LSTM for monthly electricity demand forecasting using pattern-based methods.
Sequence classification is an important data mining task in many real world applications. Over the past few decades, many sequence classification methods have been proposed from different aspects. In particular, the pattern-based method is one of the most important and widely studied sequence classification methods in …
RestoreAI predicts landmine risk from patterns, improving clearance efficiency.
The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDive, a visual active learning system that helps to create visua…
Adaptive TFTs improve cryptocurrency price prediction accuracy.
Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the use…
Social media analytics allows us to extract, analyze, and establish semantic from user-generated contents in social media platforms. This study utilized a mixed method including a three-step process of data collection, topic modeling, and data annotation for recognizing exercise related patterns. Based on the findings,…
The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for a good representation that reveals the signal structure, and sol…
PCNN prunes CNN weights efficiently for hardware acceleration.
Efficiently clusters data with weak assumptions, robust to contamination.
Proposes a new stock prediction method that accounts for market dynamics.
With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochast…
The paper explains stock market predictability through a model of heterogeneous beliefs.
Model creates human-like text descriptions for time series data.
TrustNet robustly learns noise patterns from trusted data to improve weakly-supervised classification.
A framework for privacy-preserving DNN pruning and acceleration.
The paper presents a probabilistic method to discover daily human mobility patterns from mobile data.
New method enhances hotspot prediction in IC designs.
In this paper, we show that standard feed-forward and recurrent neural networks fail to learn abstract patterns based on identity rules. We propose Relation Based Pattern (RBP) extensions to neural network structures that solve this problem and answer, as well as raise, questions about integrating structures for induct…
Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information …
Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations wh…
Customer Satisfaction is the most important factors in the industry irrespective of domain. Key Driver Analysis is a common practice in data science to help the business to evaluate the same. Understanding key features, which influence the outcome or dependent feature, is highly important in statistical model building.…
Study detects unusual trading patterns on crypto exchanges using complexity measures.
Meta-learning approach for fast and compressive energy-based memory models.
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy making. Due to data collection mechanism, it is common to see data collection with unbalanced spatial distributions. For example, some cities ma…
Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, and complex fault propagation mechanisms. This paper presents a new data-driven framework for root-cause analysis, based…
Unified framework for pattern recovery in penalized and thresholded estimation.
Study forecasts cholera outbreaks in Malawi using dynamic models.
Study forecasts U.S. bond index using deep learning, finding persistence is key.
CAG method predicts nonlinear solid mechanics responses in real-time with high accuracy and efficiency.