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.
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. 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.
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.
New method expands seed genes to functionally related clusters.
problem Discovering functionally related genes lacking GO terms.
method Semi-supervised learning with positive and unlabeled examples.
result LPU approaches significantly outperform existing methods.
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 fast regime-split Black-Scholes implied volatility solver
problem Fast computation of implied volatility
method Analytical and numerical expansions
result Achieves near-machine precision with minimal iterations
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.
Regularization improves stability and consistency of sparse autoencoders.
problem Varying features across random seeds and training choices in SAEs.
method Added L1 or L2 penalties on encoder and decoder weights.
result L2 regularization increases cross-seed feature consistency.
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.
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.
New method speeds up k-means clustering for large k by improving nearest-neighbor search.
problem Efficiently clustering large datasets with high-dimensional points.
method Seeded Approximate Nearest-Neighbor Search methods to improve Lloyd's algorithm.
result Significantly faster k-means clustering for large k values.
Confidence-based filtering reveals latent structure in diffusion models.
problem Unclear latent structure in diffusion models.
method Confidence scores from a classifier.
result Class-relevant latent structure emerges under confidence-based filtering.
New method uses Rashomon sets to improve Bayesian inference in factorial designs.
problem Combustion of model uncertainty in factorial designs leads to multimodal posterior and convergence issues.
method Rashomon-seeded annealing, integrating high-performing models as warm start for AIS.
result Restores full posterior inference without exhaustive enumeration of model space.
We consider the problem of \emph{influence maximization}, the problem of maximizing the number of people that become aware of a product by finding the `best' set of `seed' users to expose the product to. Most prior work on this topic assumes that we know the probability of each user influencing each other user, or we h…
In this paper, we present a novel approach for initializing deep neural networks, i.e., by turning PCA into neural layers. Usually, the initialization of the weights of a deep neural network is done in one of the three following ways: 1) with random values, 2) layer-wise, usually as Deep Belief Network or as auto-encod…
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.
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.
Improved graph matching using covariates for network data integration.
problem Matching networks without unique identifiers.
method Two novel covariate-assisted seeded graph matching methods.
result Improved alignment accuracy through covariate information.
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.
New method for initializing networks robustly, improving performance across various settings.
problem Insufficient initialization strategies for weight-normalized networks and ResNets.
method Proposes a novel initialization strategy based on mean field approximation.
result Improves generalization performance and robustness to hyper-parameters.
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.
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 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.
IIC decouples causal identification into two phases, significantly reducing the HTC gap in linear SEMs.
problem Determining causal effect coefficients in linear SEMs with latent confounders using the Half-Trek Criterion (HTC) leaves a gap of inconclusive causal effects.
method Iterative Identification Closure (IIC) framework that decouples causal identification into two phases: a seed function S_0 and Reduced HTC propagation.
result IIC strictly subsumes both HTC and ancestor decomposition, reducing the HTC gap by over 80% with combined seeds.
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.
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.
initKmix generates better initial partitions for k-means clustering of mixed datasets.
problem Random initial partitions lead to inconsistent clustering results.
method initKmix runs k-means multiple times, using different attributes to create initial clusters, then combines results.
result initKmix produces more accurate and consistent clustering results.
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.
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…
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…
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.
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.
Study shows variability in CNN predictions for medical images, suggesting ensemble averaging to reduce it.
problem Variability in CNN predictions for medical images due to data ordering during training.
method Reproduced CheXNet results with random seeds to identify variability in predictions.
result Averaging predictions from multiple models reduces variability by nearly 70%.
New method designs joint initial noises for diffusion models to improve diversity and alignment.
problem Independent initial noises limit diversity in generated images.
method Coupling of initial noises, maintaining Gaussian distribution while allowing dependence.
result Repulsive Gaussian coupling improves diversity without increasing sampling cost.
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.
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. 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. Randomly initialized transformers show extreme token preferences.
problem Structural biases in randomly initialized transformers.
method Dissection of transformer architecture at initialization.
result Initialization-induced biases persist throughout training.
The paper finds Koopman invariant subspaces using personalized PageRank.
problem Selecting a finite dictionary of observables for Koopman-invariant span.
method Exploiting zero-block structure in EDMD matrices and applying PageRank.
result Personalized PageRank can detect Koopman invariant subspaces.
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…
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.