Model predicts crop yields to help farmers choose varieties that balance risk and reward.
problem Optimally selecting seed varieties to increase crop yield while managing risk.
method Hierarchical machine learning for yield prediction, integrated with weather forecasting, and decision-making models.
result Achieved a median absolute error of 3.74 bushels per acre in yield predictions.
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 × n n\times n n × n matrix with integer entries, or as a quiver in special cases, together with n n n formal variables. A mutation is a c…
Efficiently selects seed nodes to maximize content influence in unknown social networks.
problem Maximizing content spread in social networks with unknown network model.
method Formulated as an infinite-horizon discounted MDP, uses model-based reinforcement learning to select seed users adaptively.
result Established a regret bound of O ~ ( T ) \widetilde O(\sqrt{T}) O ( T ) for the algorithm. New method fills cluster seeds with exact Lagrangian structures.
problem Surjectivity of map from Lagrangian fillings to cluster seeds.
method Construction of quiver with potential and Lagrangian disk surgeries.
result Trivial deformation space of quiver allows manipulation of fillings.
FIESTA optimizes model selection by efficiently evaluating multiple splits and seeds.
problem Inefficient model selection leading to unreliable performance comparisons.
method Adaptive bandit algorithms to determine optimal number of data splits and random seeds.
result Significantly reduces model evaluations while ensuring correct optimal model selection.
Seed selection affects Solvency II ratio, impacting insurance company stability.
problem Impact of RNG seed selection on Solvency II ratio stability.
method Detailed explanation of Solvency II ratio, theoretical background, and quality criteria for RNGs.
result True randomness is essential for stable Solvency II ratio results.
Improved community detection using effective resistance-based seed sets.
problem Low quality of seed nodes hinders community detection.
method Two-step process: effective resistance-based germination followed by PageRank.
result Improves precision and recall in community detection.
Fairness audits fail under missing protected labels, especially at zero access.
problem Understanding the reliability of fairness audits with incomplete protected-label data.
method Introduced a seed-calibrated stress test to separate missingness effects from seed-to-seed movement.
result Missing protected labels do not significantly alter fairness mitigation methods, but they can lead to harmful intersectional outcomes.
Paper tackles graph matching with partially correct seeds, improving performance guarantees.
problem Graph matching with partially correct seeds.
method Proposes algorithms for matching vertices based on 1-hop and 2-hop neighborhoods, analyzing their performance guarantees.
result New 2-hop algorithm requires fewer correct seeds than the 1-hop algorithm, especially for sparse graphs.
A cluster variety of Fock and Goncharov is a scheme constructed by gluing split algebraic tori, called seed tori, via birational gluing maps called mutations. In quantum theory, the ring of functions on seed tori are deformed to non-commutative rings, represented as operators on Hilbert spaces. Mutations are quantized …
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…
VALAN is a framework for navigation agents in photo-realistic environments.
problem Developing agents for indoor navigation tasks.
method Deep reinforcement learning with SEED RL architecture.
result VALAN framework can solve a variety of RL problems.
Study of Legendrian links using Floer theory and cluster varieties.
problem Understanding exact Lagrangian fillings of positive braid Legendrian links.
method Floer-theoretic approach and exact Lagrangian cobordisms.
result Proves that positive braid Legendrian links admit infinitely many exact Lagrangian fillings.
Improved K-Means++ and K-Means ∥ \| ∥ with faster run-time.
problem Efficiently selecting initial seeds for K-means clustering.
method Triangle inequality pruning and dynamic priority queue.
result Up to 17x speedup for K-Means++ and 551x for K-Means $\$ .
A new method averages neural network parameters to rank features robustly.
problem Neural networks' sensitivity to random initialization affects feature ranking robustness.
method Parameter averaging of multiple shallow networks trained with different random seeds.
result The averaged model discovers ground-truth feature importance consistently.
The paper improves ASR accuracy using semi-supervised learning and dropout.
problem Improving ASR accuracy with limited labeled data.
method Training a seed model on limited labeled data, using dropout for uncertainty, and data selection for diversity.
result The approach significantly reduces ASR errors compared to baseline.
Self-taught optimizer improves code generation using language models.
problem Improving code generation using language models.
method Recursive self-improvement of a scaffolding program that generates code.
result Improved scaffolding program generates programs with significantly better performance.
Enhances k-NN accuracy through randomized hyperstructure.
problem Improves k-NN accuracy by optimizing neighbor selection.
method Constructs a random n-dimensional hyperstructure around test instances to refine neighbor selection.
result 85.71% accuracy on Haberman's Cancer Survival dataset, compared to 80.95% for conventional k-NN.
The paper shows how shared random seeds can reduce variance in machine learning evaluations.
problem The statistical structure of comparative evaluation under shared random seeds is not well understood.
method An extended learning-based multi-agent economic simulator was used to demonstrate the effects of shared random seeds on variance reduction.
result Pairing seeds can reduce variance in machine learning evaluations, especially when outcomes are positively correlated at the seed level.
The paper connects Legendrian links to cluster theory and exact Lagrangian fillings.
problem Understanding the relationship between Legendrian links and cluster theory.
method Using exact Lagrangian fillings and cluster theory, the paper establishes connections between Legendrian links and cluster varieties.
result The augmentation variety of certain Legendrian 2-bridge links is isomorphic to a product of cluster varieties.
New method stabilizes machine learning predictions across random seeds.
problem Machine learning predictions vary across random seeds, causing instability.
method Introduces adaptive cross-bagging to eliminate seed dependence.
result Adaptive cross-bagging achieves targeted stability in debiased machine learning.
Proposes provenance and pseudo-provenance for automated test generation.
problem Invalidation of provenance in generated tests.
method Annotation of generated tests with provenance trails and pseudo-provenance.
result Validates the reliability of generated tests and their relation to seeds.
Study finds many Lagrangian fillings for certain Legendrian links.
problem Understanding Lagrangian fillings for Legendrian links of finite type.
method Use of N-graphs and combinatorics of seed patterns.
result Proves existence of at least seeds many exact embedded Lagrangian fillings for Legendrian links of type ADE.
The paper examines how random seed affects model stability and proposes ASWA and NASWA techniques to improve model robustness.
problem The impact of random seed on model performance and stability.
method A controlled study on attention, gradient-based, and surrogate model interpretations. Proposed techniques: Aggressive Stochastic Weight Averaging (ASWA) and Norm-filtered Aggressive Stochastic Weight Averaging (NASWA).
result Improvement in model robustness with ASWA and NASWA techniques, reducing standard deviation of model performance by 72%.
New algorithm for fuzzy clustering improves clustering quality.
problem Seeding iterative fuzzy algorithms for high-quality clustering.
method MaxMin Linear initialization algorithm for fuzzy C-Means.
result Validation through experiments on various datasets.
Proposes a method to accelerate safe sequential learning using offline data.
problem Limited exploration due to disconnected safe regions and slow task learning.
method Safe transfer sequential learning using Gaussian processes and offline data.
result Enhances global exploration across multiple disjoint safe regions with lower data consumption.
Bayesian optimization improves performance with common random numbers.
problem Optimizing expensive stochastic functions with common random numbers.
method Proposes a novel Gaussian process model and Knowledge Gradient for Common Random Numbers.
result Significant performance improvements with moderate computational cost.
Optimized biopharmaceutical seed train design reduces variability and saves time.
problem Designing robust biotechnological processes with cell cultures.
method Coupling uncertainty-based upstream simulation and Bayes optimization using Gaussian processes.
result Optimized seed train design results in lower cell density variability and reduced process duration.
New method finds 198,846 toric-colorable seeds of Picard number 5.
problem Enumerating toric-colorable seeds of Picard number 5.
method Binary matroid approach and dynamic programming algorithm.
result 198,846 mod 2 toric-colorable seeds of dimension four and Picard number five.
Study on how intraclass variability affects Temporal Ensembling accuracy.
problem Effect of intraclass variability on Temporal Ensembling accuracy.
method Investigated through experiments with varying seed sizes and types on different datasets.
result Significant drop in accuracy with high intraclass variability datasets, more seed images improve accuracy, and seed type impacts overall efficiency.
PNN-smoothing improves k k k -means clustering by merging subsets' clusterings.
problem Improving k k k -means clustering initialization efficiency and effectiveness. method Split dataset into subsets, cluster each subset, merge with PNN method.
result PNN-smoothing enhances k k k -means++ seeding, reducing costs. 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…
Seeds help match noisy graphs faster and more efficiently.
problem Matching noisy graphs with side information.
method Large neighborhood statistics for seeded graph matching.
result Achieves information-theoretic limit in polynomial time.
The paper explains how many random seeds are needed for statistical significance in deep reinforcement learning experiments.
problem Ensuring statistical significance in deep reinforcement learning experiments.
method Theoretical guidelines for determining the number of random seeds for t-tests and bootstrap confidence intervals.
result Deviations from statistical test assumptions can lead to inaccurate evaluations of statistical errors.
Paper shows comparing single performance scores is insufficient for non-deterministic systems, proposing to compare score distributions.
problem Insufficient comparison of non-deterministic sequence tagging systems.
method Compare score distributions based on multiple executions of LSTM-networks.
result LSTM-networks produce superior and more stable performance when compared using score distributions.
Quickshift++ improves clustering stability and performance.
problem Improving initial seedings for clustering algorithms.
method Provably good initial seedings for Quick Shift clustering.
result Statistical consistency and strong clustering performance.
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 …
This is the third paper in a series devoted to enumerating the prime alternating knots and links. This paper establishes a method for enumerating the prime alternating links. It is shown that one may choose any prime alternating link diagram of a given minimal crossing size and by applications of just two operators (T …
The paper studies a method to sample nodes from a massive graph using personalized PageRank.
problem Sampling from a massive network is expensive and impractical; the paper provides an alternative.
method The paper introduces a crawling method to approximate the personalized PageRank vector without querying the entire graph.
result The adjusted personalized PageRank vector can effectively select nodes within the same block as the seed node.
Paper proves uniqueness of a complex construction.
problem Proving uniqueness of a complex construction.
method Defined a total complex and proved uniqueness of terms.
result Total complex C T o t ( L ) CTot(L) C T o t ( L ) is unique. PPM improves graph matching for correlated Gaussian Wigner models with high probability.
problem Graph matching in the Correlated Gaussian Wigner model with edge correlations.
method Seeded projected power method (PPM) for iterative improvement of initial partial matches.
result PPM recovers ground-truth matching with high probability in O(log n) iterations if seed is close enough.
Efficiently predict LLM benchmarks using feature selection and regression.
problem Predicting full benchmark scores with minimal question subsets.
method Multiple regression with feature selection, using kernel ridge regression and mRMR.
result Improved prediction accuracy and ranking correlation across various benchmarks.
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.
problem Detecting merging knot components in discrete models.
method Lattice-filtered move graphs to model knot types, identifying connected components and merge scales.
result Merge scale defined by connected components of lattice-filtered move graphs, with specific examples for the figure-eight knot.
New method uses cluster shapes to improve track finding in particle collisions.
problem Combining timing and additional detector information for efficient track finding.
method Neural networks to analyze cluster shapes for track seeding.
result Cluster shapes reduce fake combinatorial backgrounds while maintaining high track efficiency.
Improved aspect detection from few seed keywords.
problem Fine-grained aspect detection from user reviews is labor-intensive.
method Weakly supervised co-training with student-teacher approach.
result Significant improvement in F1 scores over previous methods.
DQ4FairIM uses RL to maximize influence while ensuring fairness across all groups.
problem Fairness in influence maximization in social networks.
method Fairness-aware deep RL method using Structure2Vec network embedding.
result DQ4FairIM achieves higher fairness than fairness-agnostic and fairness-aware baselines.
TOO optimizes stochastic epidemiological models by finding both parameter settings and random seeds.
problem Calibrating stochastic epidemiological models to match empirical observations.
method Gaussian process surrogates and Thompson sampling for optimization.
result Produces actual trajectories consistent with ground truth.