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Found 18 papers, 6 papers with code
Signature Isolation Forest
Functional Isolation Forest (FIF) is a recent state-of-the-art Anomaly Detection (AD) algorithm designed for functional data.
Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation
Hallucinated translations pose significant threats and safety concerns when it comes to the practical deployment of machine translation systems.
Toward Stronger Textual Attack Detectors
The landscape of available textual adversarial attacks keeps growing, posing severe threats and raising concerns regarding the deep NLP system's integrity.
A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detection
A key feature of out-of-distribution (OOD) detection is to exploit a trained neural network by extracting statistical patterns and relationships through the multi-layer classifier to detect shifts in the expected input data distribution.
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability
Indeed, HTL relies only on a hypothesis learnt from such source data, relieving the hurdle of expansive data storage and providing great practical benefits.
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection
More importantly, we show that the usual choice (the last layer) is rarely the best one for OOD detection and that far better results could be achieved if the best layer were picked.
Beyond Mahalanobis-Based Scores for Textual OOD Detection
Deep learning methods have boosted the adoption of NLP systems in real-life applications.
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels
Temporal point processes (TPP) are a natural tool for modeling event-based data.
Learning Disentangled Textual Representations via Statistical Measures of Similarity
When working with textual data, a natural application of disentangled representations is fair classification where the goal is to make predictions without being biased (or influenced) by sensitive attributes that may be present in the data (e. g., age, gender or race).
Functional Anomaly Detection: a Benchmark Study
After an overview of the state-of-the-art and a visual-descriptive study, a variety of anomaly detection methods are compared.
Automatic Text Evaluation through the Lens of Wasserstein Barycenters
A new metric \texttt{BaryScore} to evaluate text generation based on deep contextualized embeddings e. g., BERT, Roberta, ELMo) is introduced.
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis
Because it determines a center-outward ordering of observations in $\mathbb{R}^d$ with $d\geq 2$, the concept of statistical depth permits to define quantiles and ranks for multivariate data and use them for various statistical tasks (e. g. inference, hypothesis testing).
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions
a probability distribution or a data set.
When OT meets MoM: Robust estimation of Wasserstein Distance
Issued from Optimal Transport, the Wasserstein distance has gained importance in Machine Learning due to its appealing geometrical properties and the increasing availability of efficient approximations.
Generalization Bounds in the Presence of Outliers: a Median-of-Means Study
In contrast to the empirical mean, the Median-of-Means (MoM) is an estimator of the mean $\theta$ of a square integrable r. v.
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure
a statistical population may play a crucial role in this regard, anomalies corresponding to observations with 'small' depth.
Functional Isolation Forest
For the purpose of monitoring the behavior of complex infrastructures (e. g. aircrafts, transport or energy networks), high-rate sensors are deployed to capture multivariate data, generally unlabeled, in quasi continuous-time to detect quickly the occurrence of anomalies that may jeopardize the smooth operation of the system of interest.
