UQE uses LLMs to analyze unstructured data efficiently.
problem Efficient analytics on unstructured data.
method Proposes UQE, a query engine that uses LLMs to interpret UQL queries.
result Demonstrates efficient analytics on various unstructured data types.
Paper uses RL and DCAE to classify large unstructured data with fewer features.
problem Classifying large unstructured data with high precision using fewer features.
method Deep Convolutional Autoencoder (DCAE) for feature learning and Double DQN/Retrace RL algorithms for policy optimization.
result The approach achieves high classification precision with fewer features than traditional methods.
Paper provides conditions for reliable use of pre-trained embeddings in econometrics.
problem Uncertainty in using pre-trained embeddings for econometric tasks.
method Derives sufficient conditions and convergence rates for machine learning models with pre-trained embeddings.
result Establishes theoretical foundations for reliable use of pre-trained embeddings in econometrics.
This paper explains how transformers learn from unstructured data in ICL.
problem Understanding how transformers learn from unstructured data in in-context learning.
method A simple transformer model with one or two attention layers and positional encoding is used to study the role of each component in ICL.
result A transformer with two attention layers and a look-ahead attention mask can learn from unstructured data.
Develops Φ-DVAE for assimilating unstructured data into physical models.
problem Challenges in incorporating unstructured data into physical models.
method Physics-informed dynamical variational autoencoder (Φ-DVAE) combining latent state-space model and VAE. result Demonstrates data-efficient dynamics encoding with competitive performance and uncertainty quantification.
LLMs learn new tasks from unstructured data, but it depends on word co-occurrence and positional information.
problem Understanding how LLMs can learn new tasks from unstructured data without explicit training.
method Examined the capabilities of LLMs trained on unstructured data, focusing on sequence model requirements and training data structure.
result Many ICL capabilities can emerge from word co-occurrence in unstructured data, but positional information is crucial for certain tasks.
Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, wh…
LLMs help automate extraction of actuarial variables from unstructured claims data.
problem Manual processing of unstructured claims data is time-consuming and inconsistent.
method Two-stage processing architecture using LLMs, modular Python pipeline.
result LLM-based extraction achieved high accuracy and practical actuarial value.
Healthcare data continues to flourish yet a relatively small portion, mostly structured, is being utilized effectively for predicting clinical outcomes. The rich subjective information available in unstructured clinical notes can possibly facilitate higher discrimination but tends to be under-utilized in mortality pred…
Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured poi…
GPI uses GenAI models to infer causal and predictive effects from unstructured data.
problem Estimating causal and predictive effects from unstructured data like text and images.
method Leverages open-source GenAI models to generate and represent unstructured data, applying machine learning to these representations.
result GPI efficiently estimates causal and predictive effects with quantified uncertainty, without fine-tuning.
Enhanced regime shifts detection using unstructured text and financial data.
problem Detecting regime shifts in financial markets is challenging due to noisy and multicollinear data.
method Combines LLM reasoning on unstructured text and statistical validation on financial time series.
result Framework achieves F1 score of 0.82, outperforming pure data-driven methods.
Develops methods to improve demand counterfactuals from imperfect proxies.
problem Imperfect proxies in demand models lead to biased counterfactuals and invalid inference.
method Practical toolkit for market-level and individual data, requiring minimal computation.
result Improves substitution prediction and counterfactual performance.
We introduce a new Bayesian multi-class support vector machine by formulating a pseudo-likelihood for a multi-class hinge loss in the form of a location-scale mixture of Gaussians. We derive a variational-inference-based training objective for gradient-based learning. Additionally, we employ an inducing point approxima…
Automated tests detect interactions in unstructured data.
problem Detecting interactions between latent variables in low-dimensional systems.
method Derive two interaction tests based on pairwise interventions and integrate them into an active learning pipeline.
result Tests can identify more known biological interactions than random search and standard active learning baselines.
A new method monitors unstructured 3D shapes without registration.
problem Error-prone registration and mesh reconstruction steps in PCD monitoring.
method Intrinsic geometric properties of shapes, using Laplacian and geodesic distances.
result Effective monitoring of defects without registration and mesh reconstruction.
Deep neural networks predict prostate motion from MR images.
problem Predicting prostate motion during ultrasound-guided interventions.
method Biomechanically-trained deep neural networks on unstructured nodes.
result Trained networks yield near real-time inference with 0.017 mm error.
SparseRT accelerates sparse computations on GPUs for deep learning inference.
problem Efficiently handling unstructured sparsity patterns on GPUs for deep learning.
method SparseRT, a code generator that leverages unstructured sparsity for accelerating sparse linear algebra operations.
result Geometric mean speedups of 3.4x at 90% sparsity and 5.4x at 95% sparsity for 1x1 convolutions and fully connected layers.
Study introduces a benchmark suite for evaluating neural MI estimators on real-world unstructured datasets.
problem Lack of comprehensive evaluation methods for neural MI estimators on real-world unstructured datasets.
method Developed a benchmark suite using same-class sampling and a binary symmetric channel trick.
result Showed accurate manipulation of true MI values of real-world datasets.
Weak supervision enables learning causal representations from unstructured data.
problem Learning high-level causal representations from unstructured data like images.
method Weakly supervised setting with paired samples before and after interventions. Implicit latent causal models using variational autoencoders.
result Models can reliably identify causal structure and disentangle causal variables.
Electronic Health Records (EHRs) have been heavily used to predict various downstream clinical tasks such as readmission or mortality. One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature. Although recent advances in deep learning (DL…
Framework extracts symptoms from EHRs for rapid disease outbreak detection.
problem Extracting relevant data from unstructured medical texts.
method Conformal active learning for efficient data mining.
result Framework achieves strong performance with minimal manual labeling.
Study copyright's impact on creative industries using AI-generated fonts.
problem Estimating supply and demand in creative industries with AI-generated content.
method Neural network embeddings, spatial regression, event-study analyses, structural model of supply and demand.
result Copyright can raise consumer welfare by encouraging product relocation.
A new model improves semi-supervised learning for unstructured data.
problem Manual labeling of unstructured data is expensive, time-consuming, and prone to errors.
method Batch Semi-Supervised Self-Organizing Map (Batch SS-SOM) integrating deep learning techniques.
result Batch SS-SOM performs well in semi-supervised classification and clustering, even with few labeled samples.
Log messages are now widely used in software systems. They are important for classification as millions of logs are generated each day. Most logs are unstructured which makes classification a challenge. In this paper, Deep Learning (DL) methods called Auto-LSTM, Auto-BLSTM and Auto-GRU are developed for anomaly detecti…
Improved weather forecasting with gridded pseudo-token TNPs.
problem Handling large-scale, unstructured spatio-temporal data in weather forecasting.
method Introducing gridded pseudo-token transformer neural processes (TNPs) with efficient attention mechanisms.
result Consistently outperforms baselines on various synthetic and real-world regression tasks involving large-scale data.
Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock ma…
Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatica…
Paper analyzes deep learning models for credit rating prediction using text and numerical data.
problem Improving credit rating prediction using multi-modal deep learning.
method Testing different deep learning models and fusion strategies for structured and unstructured datasets.
result CNN-based multi-modal model with two fusion strategies outperformed other models.
With the advent of Web 2.0, various types of data are being produced every day. This has led to the revolution of big data. Huge amount of structured and unstructured data are produced in financial markets. Processing these data could help an investor to make an informed investment decision. In this paper, a framework …
Self-Organizing Maps (SOM) are popular unsupervised artificial neural network used to reduce dimensions and visualize data. Visual interpretation from Self-Organizing Maps (SOM) has been limited due to grid approach of data representation, which makes inter-scenario analysis impossible. The paper proposes a new way to …
This paper analyzes text in financial disclosures to improve financial analysis.
problem Insufficient analysis of unstructured text in financial disclosures.
method Reviews and explores methods in computational linguistics and NLP.
result Highlights limitations of sentiment metrics and suggests future research areas.
Framework integrates financial and annual report data for better corporate credit ratings.
problem Lack of insights from non-financial data in credit rating models.
method Uses FinBERT to extract features from annual reports and combines them with financial data.
result Improves credit rating accuracy by 8-12%.
Product categorization using text data for eCommerce is a very challenging extreme classification problem with several thousands of classes and several millions of products to classify. Even though multi-class text classification is a well studied problem both in academia and industry, most approaches either deal with …
M2VN forecasts financial volatility by fusing time series data with news embeddings.
problem Forecasting financial volatility with unstructured news data.
method Combines deep neural networks with open-source market features and news embeddings.
result M2VN outperforms existing models in financial volatility forecasting.
Paper introduces a benchmark for predicting bankruptcy from text data.
problem Lack of a common benchmark dataset and evaluation strategy for unstructured data in bankruptcy prediction.
method Describes and evaluates several baseline models, including a bag-of-words model.
result A lightweight bag-of-words model performs surprisingly well, especially when considering data from multiple years.
Concept Relation Discovery and Innovation Enabling Technology (CORDIET), is a toolbox for gaining new knowledge from unstructured text data. At the core of CORDIET is the C-K theory which captures the essential elements of innovation. The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps (ESOM) and…
Corporate distress models typically only employ the numerical financial variables in the firms' annual reports. We develop a model that employs the unstructured textual data in the reports as well, namely the auditors' reports and managements' statements. Our model consists of a convolutional recurrent neural network w…
This paper compares unstructured and structured EM-based semi-supervised learning methods.
problem Semi-supervised learning with EM algorithm for structured prediction.
method Comparative study between unstructured and structured EM-based semi-supervised learning methods.
result Structured EM is more robust to class confusion in flood mapping datasets.
Study categorizes mutual funds using natural language processing from unstructured data.
problem Categorizing mutual funds using unstructured data for financial analysis.
method Used natural language processing models to classify mutual funds from their investment strategy descriptions.
result High accuracy in categorizing mutual funds using NLP from unstructured data.
New models exploit invariance to reduce model complexity.
problem Reducing model complexity for tasks with inherent invariances.
method Invariant random features and kernel methods.
result Exploiting invariance saves a dα factor in model complexity. Many applications collect a large number of time series, for example, the financial data of companies quoted in a stock exchange, the health care data of all patients that visit the emergency room of a hospital, or the temperature sequences continuously measured by weather stations across the US. These data are often r…
Online news media provides aggregated news and stories from different sources all over the world and up-to-date news coverage. The main goal of this study is to have a solution that considered as a homogeneous source for the news and to represent the news in a new conceptual framework. Furthermore, the user can easily …
New insights on pruning deep networks by preserving function locality.
problem Designing effective pruning methods for deep neural networks.
method Revisited loss modeling using first and second order Taylor expansions, emphasizing locality.
result Both first and second order Taylor expansions can achieve similar performance in pruning.
Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent search, statistical characterization, and meaningful classification. Latent Dirichlet…
Generative Intervention Models predict perturbation effects without knowing the underlying mechanisms.
problem Predicting perturbation effects when the mechanisms are unknown.
method Generative Intervention Models (GIM) that map perturbation features to distributions over atomic interventions in a causal model.
result GIMs achieve robust out-of-distribution predictions and infer underlying perturbation mechanisms.
Quantum algorithm finds extrema in discrete optimisation problems.
problem Finding extrema in discrete optimisation functions.
method Quantum unstructured search algorithm (QSERA) to map and find extrema.
result Quadratic speed-up over classical algorithms for discrete optimisation.
Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (GOs) and NGOs in response to their critical information requirements. We co-create products with UK and in-country commercial, GO and NGO pa…