arXiv research

Improved aspect detection from few seed keywords.

Paper proposes a method to learn word translations bidirectionally.

This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive po…

Paper proposes GSSNMF for legal document classification and topic modeling.

Deep neural networks (DNNs) have achieved unprecedented performance on a wide range of complex tasks, rapidly outpacing our understanding of the nature of their solutions. This has caused a recent surge of interest in methods for rendering modern neural systems more interpretable. In this work, we propose to address th…

This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phra…

Paper tackles graph matching with partially correct seeds, improving performance guarantees.

The paper shows how shared random seeds can reduce variance in machine learning evaluations.

New method stabilizes machine learning predictions across random seeds.

Fairness audits fail under missing protected labels, especially at zero access.

Study finds many Lagrangian fillings for certain Legendrian links.

The paper examines how random seed affects model stability and proposes ASWA and NASWA techniques to improve model robustness.

New method fills cluster seeds with exact Lagrangian structures.

Many methods for automated software test generation, including some that explicitly use machine learning (and some that use ML more broadly conceived) derive new tests from existing tests (often referred to as seeds). Often, the seed tests from which new tests are derived are manually constructed, or at least simpler t…

Bayesian optimization improves performance with common random numbers.

New method finds 198,846 toric-colorable seeds of Picard number 5.

Optimized biopharmaceutical seed train design reduces variability and saves time.

Study on how intraclass variability affects Temporal Ensembling accuracy.

Transformers can be made to implement specific algorithms by controlling training parameters.

PNN-smoothing improves $k$-means clustering by merging subsets' clusterings.

Given two graphs, the graph matching problem is to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs. The seeded graph matching problem is the graph matching problem when we are first given a partial alignment that we are tasked with completing. In this paper, we m…

Data-aware methods for dimensionality reduction and matrix decomposition aim to find low-dimensional structure in a collection of data. Classical approaches discover such structure by learning a basis that can efficiently express the collection. Recently, "self expression", the idea of using a small subset of data vect…

Community detection is, at its core, an attempt to attach an interpretable function to an otherwise indecipherable form. The importance of labeling communities has obvious implications for identifying clusters in social networks, but it has a number of equally relevant applications in product recommendations, biologica…

We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm. Our Joint Optimization of Fidelity and Commensurability (JOFC) algorithm embeds two graphs into a common Euclidean space where the matching inference task can be performed. Through real and simulated …

Efficiently selects seed nodes to maximize content influence in unknown social networks.

Eradicating hunger and malnutrition is a key development goal of the 21st century. We address the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision-making framework. Specifically, we introduce a novel hierarchical machine learning mechanism for predicting c…

Paper proves uniqueness of a complex construction.

We prove that for a generic $3$-dimensional integrable rolling distribution of contact elements (excluding developable seed and isotropic developable leaves) isometric correspondence of leaves of a general nature (independent of the shape of the seed) requires the Bäcklund transformation.

PPM improves graph matching for correlated Gaussian Wigner models with high probability.

We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs. Our algorithm combines spectral graph embedding with existing state-of-the-art seeded graph matching procedures. We justify our approach by proving that modestly correlated, large stochastic blo…

Discrete knot theory models use lattice-filtered graphs to detect merging knot components.

New method uses cluster shapes to improve track finding in particle collisions.

TOO optimizes stochastic epidemiological models by finding both parameter settings and random seeds.

OmniMatch algorithm perfectly matches graphs without edge correlation.

A cluster variety of Fock and Goncharov is a scheme constructed from the data related to the cluster algebras of Fomin and Zelevinsky. A seed is a combinatorial data which can be encoded as an $n\times n$ matrix with integer entries, or as a quiver in special cases, together with $n$ formal variables. A mutation is a c…

We study a well known noisy model of the graph isomorphism problem. In this model, the goal is to perfectly recover the vertex correspondence between two edge-correlated Erdős-Rényi random graphs, with an initial seed set of correctly matched vertex pairs revealed as side information. For seeded problems, our result pr…

SEED RL accelerates deep RL training on modern accelerators.

Regularization improves stability and consistency of sparse autoencoders.

We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift. We establish statistical consistency guarantees for this modification. We then…

The paper proves there are many Lagrangian fillings for Legendrian links of affine type.

The paper proposes a method to stabilize predictions by identifying causal variables using a seed variable.

Consistently checking the statistical significance of experimental results is one of the mandatory methodological steps to address the so-called "reproducibility crisis" in deep reinforcement learning. In this tutorial paper, we explain how the number of random seeds relates to the probabilities of statistical errors. …

The study finds many Lagrangian fillings for Legendrian links of specific types.

VALAN is a framework for navigation agents in photo-realistic environments.

A fast regime-split Black-Scholes implied volatility solver

MONSTOR estimates influence in unseen networks with high accuracy.

The paper defines and studies discrete p-density and compression-radius profiles of lattice knots.

Empirical study shows SGD's random seed impacts model weights more than training examples, suggesting intrinsic privacy.