arXiv research

Study shows partially-typed NER datasets can match fully-typed ones in model performance.

Typilus predicts types for Python programs using neural networks.

Proposes CDTD, a diffusion model for mixed-type tabular data.

Paper proposes an adaptive modeling approach for row-type dependent predictive analysis in banking.

The Type IIA flow converges on symplectic manifolds, with singularity models identified.

Paper proposes a new model using Capsule Networks to classify malware types.

Crowdsourced labeling recovers task types with minimal queries.

Generative model synthesizes complex data structures with composite and nested types.

Improves biomedical entity linking with latent type modeling.

New model clusters mixed-type data with missing values, improving air quality analysis.

Extends Alòs' formula to Barndorff-Nielsen and Shephard model.

Classifies maximal symmetry models of CR dimension 1.

New method detects events with keywords, adapting to new types.

We consider a financial market model which consists of a financial asset and a large number of interacting agents classified into many types. Different types of agents are heterogeneous in their price expectations. Each agent can change its type based on the current empirical distribution of the types and the equilibri…

As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data source…

Proposes dynamic model type recommendation for OLP technique.

The Myers-Briggs Type Indicator (MBTI) is a popular personality metric that uses four dichotomies as indicators of personality traits. This paper examines the use of pre-trained language models to predict MBTI personality types based on scraped labeled texts. The proposed model reaches an accuracy of $0.47$ for correct…

Type system captures CI relationships for probabilistic models.

New model identifies cell-specific genes for cancer prognosis.

Sparse linear (or generalized linear) models combine a standard likelihood function with a sparse prior on the unknown coefficients. These priors can conveniently be expressed as a maximization over zero-mean Gaussians with different variance hyperparameters. Standard MAP estimation (Type I) involves maximizing over bo…

Machine learning disciplines shift values, not just model types.

This paper aims at the problem of link pattern prediction in collections of objects connected by multiple relation types, where each type may play a distinct role. While common link analysis models are limited to single-type link prediction, we attempt here to capture the correlations among different relation types and…

Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click prediction, conversions have different types in nature, and each type may be associated with different decisive factors. In this pap…

We develop a latent variable model and an efficient spectral algorithm motivated by the recent emergence of very large data sets of chromatin marks from multiple human cell types. A natural model for chromatin data in one cell type is a Hidden Markov Model (HMM); we model the relationship between multiple cell types by…

Study algebraic invariants from lightning self-attention models.

In this paper we investigate the singularities of Lagrangian mean curvature flows in $\mathbf{C}^m$ by means of smooth singularity models. Type I singularities can only occur at certain times determined by invariants in the cohomology of the initial data. In the type II case, these smooth singularity models are asympto…

A novel graph spectral method for mixed categorical and numerical data.

GDA-HIN adapts across heterogeneous networks by aligning shared and private node types.

VAEM extends VAEs to handle mixed-type data heterogeneity.

We investigate a class of feature allocation models that generalize the Indian buffet process and are parameterized by Gibbs-type random measures. Two existing classes are contained as special cases: the original two-parameter Indian buffet process, corresponding to the Dirichlet process, and the stable (or three-param…

We study the transfer of adversarial robustness of deep neural networks between different perturbation types. While most work on adversarial examples has focused on $L_\infty$ and $L_2$-bounded perturbations, these do not capture all types of perturbations available to an adversary. The present work evaluates 32 attack…

Type A surfaces are the locally homogeneous affine surfaces which can be locally described by constant Christoffel symbols. We address the issue of the geodesic completeness of these surfaces: we show that some models for Type A surfaces are geodesically complete, that some others admit an incomplete geodesic but model…

G-SHAP generates multiple types of explanations for machine learning models.

Enhanced fuzzy system predicts chaotic time series with improved accuracy.

We study almost-calibrated, $O(n)$-equivariant Lagrangian mean curvature flow in $\mathbb{C}^n$, and prove structural theorems about the Type I and Type II blowups of finite-time singularities. In particular, we prove that any Type I blowup of such a flow must be a special Lagrangian pair of transversely intersecting p…

We examine the efficiency of the Asymmetric Power ARCH (APARCH) model in the case where the residuals follow the standardized Pearson type IV distribution. The model is tested with a variety of loss functions and the efficiency is examined via application of several statistical tests and risk measures. The results indi…

The paper provides gradient estimates for nonlinear heat-type equations on smooth metric measure spaces.

FactTest assesses LLM factuality with Type I error control.

Study of conjugacy classes in infinite-type surfaces' mapping class groups.

We examine the topology of various spaces of locally homogeneous affine manifolds which arise from the classification result of Opozda [B. Opozda, A classification of locally homogeneous connections on 2-dimensional manifolds, Differential Geom. Appl. 21 (2004), 173-198.] as orbits of the action of $GL(2,\mathbb{R})$ (…

Paper introduces efficient methods for probabilistic querying of event sequences.

The partially observable hidden Markov model is an extension of the hidden Markov Model in which the hidden state is conditioned on an independent Markov chain. This structure is motivated by the presence of discrete metadata, such as an event type, that may partially reveal the hidden state but itself emanates from a …

Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of adversarial attack that can cheat classifiers by significant changes. For example, we can significantly change a face but well-trained neural…

This paper presents novel mixed-type Bayesian optimization (BO) algorithms to accelerate the optimization of a target objective function by exploiting correlated auxiliary information of binary type that can be more cheaply obtained, such as in policy search for reinforcement learning and hyperparameter tuning of machi…

SessionPath improves category suggestions in type-ahead search.

The paper classifies U.S. crop types using hyperspectral satellite imagery.

Compactness results for Hermitian manifolds help understand Type IIB flow.

New method for mixed data types in graphical models.