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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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48 results for natural names

Context2Name predicts natural names from minified code, improving code readability.

problem Minified code makes it hard to understand natural names in JavaScript.
method Combines static analysis and neural networks to predict natural names.
result Successfully predicts 47.5% of all minified identifiers in real-world code.

System solves author name ambiguity in e-commerce catalogs.

problem Finding correct author names in e-commerce catalogs with abbreviations and spelling variants.
method Composite system using open data sources and machine learning techniques for natural language processing.
result Top proposal of the system is the normalized author name with 72% accuracy.

Paper proposes neural networks for automatically naming assembly functions.

problem Automatically assigning names to assembly code functions.
method Formal definition of problem, baseline models (Seq2Seq, Transformer), fine-tuning neural networks.
result Neural networks can effectively predict function names in binaries, even outperforming state-of-the-art.

We introduce the fibred toric varieties as equivariant CPr\mathbb{C}P^r bundles over lower dimensional toric varieties. An equivalent characterization is that the natural morphisms on them degenerate to bundle projections in the context of variation of toric varieties as GIT quotients. Our main observation is that these…

2010-12-11abs ↗pdf ↗

Paper proposes neural approach for Chinese named entity recognition.

problem Challenges in Chinese named entity recognition due to context-dependency and lack of word delimiters.
method Introduces a CNN-LSTM-CRF neural architecture and a unified framework for joint training with word segmentation.
result Improves Chinese named entity recognition performance, especially with limited training data.

This is a lecture note for the course DS-GA 3001 <Natural Language Understanding with Distributed Representation> at the Center for Data Science , New York University in Fall, 2015. As the name of the course suggests, this lecture note introduces readers to a neural network based approach to natural language understand…

2015-11-24abs ↗pdf ↗

The paper introduces uncertainty quantification for NER models.

problem Current NER models lack uncertainty measures, leading to downstream errors.
method Full-Sequence and Subsequence Conformal Prediction framework.
result The method provides formal guarantees about the reliability of model predictions.

Much work has been done refining and characterizing the receptive fields learned by deep learning algorithms. A lot of this work has focused on the development of Gabor-like filters learned when enforcing sparsity constraints on a natural image dataset. Little work however has investigated how these filters might expan…

2012-10-31abs ↗pdf ↗

Extended Kalman Filter is shown to be a gradient descent in trajectory space.

problem Estimating state of dynamical systems from noisy measurements.
method Recovery of extended Kalman filter equations from Amari's natural gradient in trajectory space.
result Extended Kalman Filter is equivalent to natural gradient descent in trajectory space.

We cast Amari's natural gradient in statistical learning as a specific case of Kalman filtering. Namely, applying an extended Kalman filter to estimate a fixed unknown parameter of a probabilistic model from a series of observations, is rigorously equivalent to estimating this parameter via an online stochastic natural…

2017-03-01abs ↗pdf ↗

The paper extends influence functions to sequence tagging tasks for better model interpretability.

problem Lack of interpretability methods for sequence tagging models.
method Define and compute influence of training instance segments on test segment predictions.
result The segment influence method tracks with true influence and identifies annotation errors.

Proposes a method to improve deep active learning for NER tasks.

problem Weaknesses of existing deep active learning algorithms in practice.
method Estimates error decay curves of feature-defined subsets to improve sampling efficiency and robustness.
result Significantly outperforms diversification-based methods for black-box NER taggers and makes sampling more robust to labeling noise.

We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time. Our motivation rests in the Thurstonian view that many discrete data types can be considered as being generated from a subset of underlying latent continuous variables, …

2014-08-01abs ↗pdf ↗

We investigate, in some details, symplectic equivalence between several conformal classes of Lorentz metrics on the hyperboloid of one sheet H1,1T×TΔH^{1,1} \cong T \times T - Δ and affine coadjoint orbits of the group Diff+(Δ)Diff_+(Δ) of orientation preserving diffeomorphisms of ΔTΔ\cong T with its natural projective structure. …

1998-06-24abs ↗pdf ↗

Improved NER performance on imbalanced data.

problem Highly unbalanced training data in NER tasks.
method Adapted a neural architecture with CRF and BI-LSTM layers, using pre-trained embeddings. Introduced a two-class split to optimize performance.
result Significant improvement in performance for weak classes with minimal training data.

Two projective structures on Riemann surfaces are described and shown not to be identical.

problem Characterizing and distinguishing projective structures on Riemann surfaces.
method Construction and analysis of projective structures pup_u and php_h using the period map and moduli space.
result The Hodge projective structure php_h is not the same as the uniformization projective structure pup_u.

We consider in this paper the minimally twisted chain link with 5 components in the 3-sphere, and we analyze the Dehn surgeries on it, namely the Dehn fillings on its exterior M5. The 3-manifold M5 is a nicely symmetric hyperbolic one, filling which one gets a wealth of hyperbolic 3-manifolds having 4 or fewer (includi…

2011-09-05abs ↗pdf ↗

Classifies geodesic orbit spaces with abelian isotropy subgroups.

problem Characterizing and classifying geodesic orbit spaces with specific isotropy subgroups.
method Simplified study of geodesic orbit metrics on G/S by reducing to submanifolds and generalized flag manifolds, using properties of root systems.
result Geodesic orbit spaces of the form (G/S,g) are naturally reductive.

In this paper, we consider the scalar curvature of Yamabe solitons. In particular we show that, with natural conditions and non positive Ricci curvature, any complete Yamabe soliton has constant scalar curvature, namely, it is a Yamabe metric. We also show that the quadratic decay at infinity of the Ricci curvature of …

2011-08-31abs ↗pdf ↗

We extend the notion of connection in order to be able to study singular geometric structures, namely, we consider a notion of connection on a Lie algebroid which is a natural extension of the usual concept of connection. Using connections, we are able to define holonomy of the orbit foliation of a Lie algebroid and pr…

2000-07-21abs ↗pdf ↗

Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price using various statistical, econ…

2018-05-29abs ↗pdf ↗

Vector embedding is a foundational building block of many deep learning models, especially in natural language processing. In this paper, we present a theoretical framework for understanding the effect of dimensionality on vector embeddings. We observe that the distributional hypothesis, a governing principle of statis…

2018-03-01abs ↗pdf ↗

AOPU stabilizes NN training by approximating natural gradient, improving stability and convergence.

problem Stability and interpretability in online NN training for industrial soft sensors.
method AOPU truncates gradient backpropagation, optimizing trackable parameters, and approximating natural gradient.
result AOPU achieves stable convergence and superior performance on chemical process datasets.

Stochastic Variational Optimization is a parallelizable method for gradient estimation.

problem Gradient estimation for differentiable objectives in parallel environments.
method Variational Optimization, Natural Evolution Strategies, Gaussian Perturbation, Directional Derivatives.
result Directional Derivatives are preferable to Variational Optimization for parallel Stochastic Gradient Descent.

The notion of a parallelizable distribution has been introduced and investigated. A non-integrable parallelizable distribution carries a natural sub-Riemannian structure. The geometry of this structure has been studied from the bi-viewpoint of absolute parallelism geometry and sub-Riemannian geometry. Two remarkable li…

2016-03-19abs ↗pdf ↗

Corpus poisoning can manipulate word meanings in word embeddings, affecting natural language processing tasks.

problem Controlling word meanings via corpus modifications.
method Developed an explicit expression over corpus features to control word embeddings.
result Demonstrated the ability to manipulate word meanings in word embeddings, affecting various downstream tasks.

New bialgebra structures for relative Poisson algebras are introduced.

problem Extending bialgebra structures from commutative differential algebras to relative Poisson algebras.
method Introducing new bialgebra structures (relative PCA bialgebras) and using commutative 2-cocycles.
result New bialgebra structures (relative PCA bialgebras) are equivalent to certain Manin triples.

We introduce a method to construct G2G_2-instantons over compact G2G_2-manifolds arising as the twisted connected sum of a matching pair of building blocks [Kov03,KL11,CHNP12]. Our construction is based on gluing G2G_2-instantons obtained from holomorphic bundles over the building blocks via the first named author's wo…

2013-10-29abs ↗pdf ↗