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Found 41 papers, 10 papers with code
Universal representations for financial transactional data: embracing local, global, and external contexts
Effective processing of financial transactions is essential for banking data analysis.
Diversity-Aware Ensembling of Language Models Based on Topological Data Analysis
Ensembles are important tools for improving the performance of machine learning models.
From Variability to Stability: Advancing RecSys Benchmarking Practices
In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets.
Challenges in data-based geospatial modeling for environmental research and practice
With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research.
RepQ: Generalizing Quantization-Aware Training for Re-Parametrized Architectures
However, there are various methods to improve their efficiency.
Long-term drought prediction using deep neural networks based on geospatial weather data
The problem of high-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance.
Machine Translation Models Stand Strong in the Face of Adversarial Attacks
Through our investigation, we provide evidence that machine translation models display robustness displayed robustness against best performed known adversarial attacks, as the degree of perturbation in the output is directly proportional to the perturbation in the input.
Correcting sampling biases via importance reweighting for spatial modeling
In machine learning models, the estimation of errors is often complex due to distribution bias, particularly in spatial data such as those found in environmental studies.
Uncertainty Estimation of Transformers' Predictions via Topological Analysis of the Attention Matrices
In this paper, we propose a method for uncertainty estimation based on the topological properties of the attention mechanism and compare it with classical methods.
Designing an attack-defense game: how to increase robustness of financial transaction models via a competition
We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input.
Hiding Backdoors within Event Sequence Data via Poisoning Attacks
This adoption brings new danger, as deep black-box models are known to be vulnerable to adversarial attacks.
Portfolio Selection via Topological Data Analysis
Portfolio management is an essential part of investment decision-making.
Label Attention Network for sequential multi-label classification: you were looking at a wrong self-attention
For example, the micro-AUC of our approach is $0. 9536$ compared to $0. 7501$ for a vanilla transformer.
Continuous-time convolutions model of event sequences
Massive samples of event sequences data occur in various domains, including e-commerce, healthcare, and finance.
Uncertainty estimation for time series forecasting via Gaussian process regression surrogates
This study proposes a new method for uncertainty estimation based on the surrogate Gaussian process model.
Robust representations of oil wells' intervals via sparse attention mechanism
The focus in our experiments is on oil&gas data, namely, well logs, a prominent example of multivariate time series.
Non-contrastive representation learning for intervals from well logs
One of the possible approaches is self-supervised learning (SSL).
Self-supervised similarity models based on well-logging data
Adopting data-based approaches leads to model improvement in numerous Oil&Gas logging data processing problems.
ScaleFace: Uncertainty-aware Deep Metric Learning
The performance of modern deep learning-based systems dramatically depends on the quality of input objects.
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise
The approach utilizes entropy regularization, quantization noise, and Adaptive Deviation for Quantization (ADQ) module to enhance the search procedure.
Effective training-time stacking for ensembling of deep neural networks
Ensembling is a popular and effective method for improving machine learning (ML) models.
Transfer learning for ensembles: reducing computation time and keeping the diversity
Our approach for the transfer learning of ensembles consists of two steps: (a) shifting weights of encoders of all models in the ensemble by a single shift vector and (b) doing a tiny fine-tuning for each individual model afterwards.
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective
The problem of out-of-distribution detection for graph classification is far from being solved.
Usage of specific attention improves change point detection
The change point is a moment of an abrupt alteration in the data distribution.
Deep learning model solves change point detection for multiple change types
Common approaches assume that there are only two fixed distributions for data: one before and another after a change point.
Embedded Ensembles: Infinite Width Limit and Operating Regimes
In this limit, we identify two ensemble regimes - independent and collective - depending on the architecture and initialization strategy of ensemble models.
Similarity learning for wells based on logging data
The essence of the interwell correlation constitutes an assessment of the similarities between geological profiles.
Bank transactions embeddings help to uncover current macroeconomics
Financial transactions are long, and a number of clients is huge, so we develop an efficient approach that allows fast and accurate estimation of macroeconomic indexes based on a stream of transactions consisting of millions of transactions.
A Differentiable Language Model Adversarial Attack on Text Classifiers
Moreover, due to the usage of the fine-tuned language model, the generated adversarial examples are hard to detect, thus current models are not robust.
Adversarial Attacks on Deep Models for Financial Transaction Records
In this work, we examine adversarial attacks on transaction records data and defences from these attacks.
InDiD: Instant Disorder Detection via Representation Learning
For sequential data, a change point is a moment of abrupt regime switch in data streams.
COHORTNEY: Non-Parametric Clustering of Event Sequences
Cohort analysis is a pervasive activity in web analytics.
Unsupervised anomaly detection for discrete sequence healthcare data
The models provide state-of-the-art results for unsupervised anomaly detection for fraud detection in healthcare.
Recurrent Convolutional Neural Networks help to predict location of Earthquakes
For historical data on Japan earthquakes our model predicts occurrence of an earthquake in $10$ to $60$ days from a given moment with magnitude $M_c > 5$ with quality metrics ROC AUC $0. 975$ and PR AUC $0. 0890$, making $1. 18 \cdot 10^3$ correct predictions, while missing $2. 09 \cdot 10^3$ earthquakes and making $192 \cdot 10^3$ false alarms.
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world
The first approach adopts a Monte-Carlo method and allows usage in any scenario, the second approach uses a continuous relaxation of models and target metrics, and thus allows usage of state-of-the-art methods for adversarial attacks with little additional effort.
Application of Machine Learning to accidents detection at directional drilling
We present a data-driven algorithm and mathematical model for anomaly alarming at directional drilling.
Real-time data-driven detection of the rock type alteration during a directional drilling
During the directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20m between the bit and high-fidelity rock type sensors.
Multifidelity Bayesian Optimization for Binomial Output
The acquisition function typically depends on the mean and the variance of the surrogate model at a given point.
Data-driven model for the identification of the rock type at a drilling bit
Directional oil well drilling requires high precision of the wellbore positioning inside the productive area.
Large Scale Variable Fidelity Surrogate Modeling
Engineers widely use Gaussian process regression framework to construct surrogate models aimed to replace computationally expensive physical models while exploring design space.
Minimax Error of Interpolation and Optimal Design of Experiments for Variable Fidelity Data
The key question in this setting is how the sizes of the high and low fidelity data samples should be selected in order to stay within a given computational budget and maximize accuracy of the regression model prior to committing resources on data acquisition.
