arXiv research

In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new …

New method detects conflicts in police body-worn audio.

NP$^2$MT uses phrase attention to translate phrases efficiently.

The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically se…

Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance. The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems on the task of English-to-Fr…

This paper improves translation of rare words in under-resourced languages.

Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural ma…

A new algorithm optimizes softmax units in large language models.

A new method uses PSO to optimize sentence weights for user-oriented document summaries.

Study invariant measures on measured laminations for subgroups of mapping class group.

New set-valued star-shaped risk measures introduced for better risk assessment.

The Bergman measure converges to the Zhang measure on a hybrid space.

Bayesian approach to robust risk measures under model uncertainty.

The paper classifies 1-dimensional uniform measures in various dimensions.

The paper studies dynamic star-shaped risk measures and their representation.

Introduces Star-Shaped deviation measures for risk analysis.

Transformers can interpolate between arbitrary measures.

Classifies invariant measures on specific character varieties.

Researchers compute the ratio between two normalizations of Thurston measure on measured laminations.

Paper characterizes star-shaped risk measures and their properties.

Paper compares fairness measures and feature importance measures using SHAP.

New Bayesian method for spectral deconvolution with Poisson noise.

Paper introduces quasi-logconvex risk measures and their properties.

Submodularity is studied for convex risk measures, including Expected Shortfall.

Paper compares graph and set partition measures for graph clustering.

New geometric measure simplifies complex analysis.

The paper explores non-convex risk measures and their characterizations.

The paper calculates extreme measures in continuous time conic finance.

Paper characterizes monotonic mean-deviation risk measures.

Introduces factor risk measures to assess risk relative to multiple factors.

Dual representations for robust risk measures and uncertainty sets.

A scalable approach to learning from probability measures using quantization.

Study on measurable pseudo-Anosov maps on surfaces.

The Cannon-Thurston map's pushed measures on the circle are singular with respect to sphere measures.

Risk measures for multivariate financial positions are studied in a utility-based framework. Under a certain incomplete preference relation, shortfall and divergence risk measures are defined as the optimal values of specific set minimization problems. The dual relationship between these two classes of multivariate ris…

New weighted surface area measures for convex bodies with applications.

Standardized fairness measures for continuous risk scores using Wasserstein distance.

Study SRB measures for Anosov actions on manifolds.

Study proposes worst+gap measure for better DG evaluation.

We study generalizations of Reifenberg's Theorem for measures in $\mathbb R^n$ under assumptions on the Jones' $β$-numbers, which appropriately measure how close the support is to being contained in a subspace. Our main results, which holds for general measures without density assumptions, give effective measure bounds…

The study evaluates AI model performance measures for medical use.

We introduce a weak notion of barycenter of a probability measure $μ$ on a metric measure space $(X, d, {\bf m})$, with the metric $d$ and reference measure ${\bf m}$. Under the assumption that optimal transport plans are given by mappings, we prove that our barycenter $B(μ)$ is well defined; it is a probability measur…

Study on convergence of Narasimhan-Simha measures on degenerating families of Riemann surfaces.

Stationary measures on hyperbolic surfaces with cusps are singular and stable under quasi-symmetries.

Study measures rigidity for random walks and flows via generalized u-Gibbs states.

The paper establishes a connection between different risk measures and their risk contributions.

The study shows finite measure-preserving isometry groups for certain metric measure spaces.

Investigates set-valued risk measures for processes and vectors, proving equivalence and providing new dual representations.