arXiv research

Recommender system improves recall of omitted foods in online dietary surveys.

A novel continual prediction model outperforms traditional one-time models in predicting AKI.

Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care. We studied this problem using eight years of Electronic Health Records (EHR) data from Stanford Hospital. We fit models to predict 24 hour discharge across the entire inpatient population. The best …

New algorithm reduces online learning regret for bounded recall games.

This paper introduces a new method to train normalizing flows using precision-recall divergences.

Interpretation of electroencephalogram (EEG) signals can be complicated by obfuscating artifacts. Artifact detection plays an important role in the observation and analysis of EEG signals. Spatial information contained in the placement of the electrodes can be exploited to accurately detect artifacts. However, when few…

Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data p…

The study examines how class imbalance affects precision-recall curves.

We define a methodology to quantify market activity on a 24 hour basis by defining a scale, the so-called scale of market quakes (SMQ). The SMQ is designed within a framework where we analyse the dynamics of excess price moves from one directional change of price to the next. We use the SMQ to quantify the FX market an…

Revises precision-recall curves for generative models.

The study formalizes temporal precision and recall for anomaly detection in sequences.

Unified and extended precision-recall metrics for generative models.

For information retrieval and binary classification, we show that precision at the top (or precision at k) and recall at the top (or recall at k) are maximised by thresholding the posterior probability of the positive class. This finding is a consequence of a result on constrained minimisation of the cost-sensitive exp…

Transformers can store facts efficiently using associative memories.

Recalls intrinsically harmonic forms and open problems.

Paper tackles imbalanced binary classification by optimizing precision and recall directly.

LLMs can memorize economic data and recall exact values before their training cutoff.

RAGuard improves safety in LLMs for offshore wind maintenance.

A new algorithm LONR learns without terminal states or perfect recall.

Paper proposes a human-algorithm approach to reduce medical device recall risk and workload.

Framework for precise recall control in spatial conflation tasks.

A new method for generating replay samples on the fly, optimizing for not forgetting.

Proposes a new tree-based algorithm for class-imbalanced data.

Study identifies subphenotypes of pediatric sepsis to improve ML predictive performance.

The paper critiques and expands on common evaluation metrics in machine learning.

Modeling short selling risks to quantify losses.

Paper introduces a new evaluation framework for generative models using Rényi divergences.

New findings show tool-augmented models can recall unlimited facts, outperforming purely memorized models.

The optimal ranking score between precision and recall is rarely F1 and can be found using specific methods.

Recalls and refines the concept of algebraically rectifiable curves.

DIAL learns embeddings to maximize recall and accuracy for entity resolution.

Improves interpretability of neural network intermediate layers by making concept activations more robust and effective.

Transformers recall from long distributions with statistical guarantees.

Mobile app improves speech recognition of names with user feedback.

Enhanced anomaly detection using PRC-RF with autoencoders.

LLMs can be tricked into recalling facts based on context clues.

The local Hurst exponent, a measure employed to detect the presence of dependence in a time series, may also be used to investigate the source of intraday variation observed in the returns in foreign exchange markets. Given that changes in the local Hurst exponent may be due to either a time-varying range, or standard …

Evaluation often aims to reduce the correctness or error characteristics of a system down to a single number, but that always involves trade-offs. Another way of dealing with this is to quote two numbers, such as Recall and Precision, or Sensitivity and Specificity. But it can also be useful to see more than this, and …

Unified framework for sequence models using test-time regression.

New method detects novel node categories in graphs with distribution shifts.

KATA improves associative recall by optimizing feature maps derived from nonnegative attention weights.

THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.

New metrics improve reliability of image generation evaluation.

Differential equations can be used to construct predictive models of a diverse set of real-world phenomena like heat transfer, predator-prey interactions, and missile tracking. In our work, we explore one particular application of stochastic differential equations, the Black-Scholes-Merton model, which can be used to p…

Framework for imputing time series data with uncertainty measures.

Deep neural networks continue to show improved performance with increasing depth, an encouraging trend that implies an explosion in the possible permutations of network architectures and hyperparameters for which there is little intuitive guidance. To address this increasing complexity, we propose Evolutionary DEep Net…

We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independ…

Study improves detection of cryptocurrency pump-and-dump schemes.