Study on cold and freezing sets in digital images.
problem Properties of cold sets in digital images.
method Analysis of properties and relationships between cold and freezing sets.
result Examined relationships between cold and freezing sets.
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48 results for “cold sets”
Bayesian neural networks with data augmentation show a persistent cold posterior effect.
problem Understanding the cold posterior effect in Bayesian neural networks with data augmentation.
method Developed principled Bayesian neural networks using data augmentation, providing exact likelihoods and tight bounds.
result The cold posterior effect persists even in models incorporating data augmentation, suggesting it's not an artifact.
Playlist recommendation involves producing a set of songs that a user might enjoy. We investigate this problem in three cold-start scenarios: (i) cold playlists, where we recommend songs to form new personalised playlists for an existing user; (ii) cold users, where we recommend songs to form new playlists for a new us…
Examines how irreducibility and rigidity affect digital images.
problem Understanding interactions between irreducibility and rigidity in digital images.
method Analyzes Cartesian products, wedges, and cold and freezing sets.
result Interactions between irreducibility and rigidity in digital images.
The cold posterior effect is explored through PAC-Bayes bounds for small sample sizes.
problem The cold posterior effect in approximate Bayesian inference for small datasets.
method Investigation through PAC-Bayes generalization bounds, focusing on temperature parameter λ.
result The temperature parameter λ in PAC-Bayes bounds captures the cold posterior effect.
Cold posteriors improve Bayesian neural networks by reducing overestimation of aleatoric uncertainty.
problem Overestimation of aleatoric uncertainty in Bayesian neural networks.
method Tuning the temperature of the posterior on a validation set.
result Reducing temperature leads to better reflection of true prior beliefs.
Numerically estimates Colding-Minicozzi entropies of self-shrinkers.
problem Estimating Colding-Minicozzi entropies of self-shrinkers.
method Numerical estimation of entropies for specific self-shrinkers.
result Colding-Minicozzi entropies of -dimensional Angenent torus are decreasing with dimension.
New examples show non-integer Hausdorff dimensions in collapsing spaces.
problem Understanding Hausdorff dimensions in collapsing Ricci limit spaces.
method Provided examples of spaces with irregular Hausdorff dimensions.
result Hausdorff dimension of singular set exceeds regular set's dimension.
Proof of Reifenberg theorem in metric spaces, expanding on Cheeger and Colding's work.
problem Proving the Reifenberg theorem in metric spaces using Gromov-Hausdorff distance.
method Detailed proof of Cheeger and Colding's result, expanding on their arguments.
result BiLipschitz version of the Reifenberg theorem in metric spaces.
Cold-start PV forecasting uses synthetic histories to train time-series foundation models.
problem Cold-start PV forecasting
method Zero-shot pipeline with synthetic histories
result TabPFN-TS achieves the lowest error under Real Feedback strategy
We address the cold start problem in recommendation systems assuming no contextual information is available neither about users, nor items. We consider the case in which we only have access to a set of ratings of items by users. Most of the existing works consider a batch setting, and use cross-validation to tune param…
SPARC tackles cold-start nodes in graphs by using spectral embeddings.
problem Cold-start nodes in graphs lacking initial connections.
method Introduces SPARC, a framework utilizing spectral embeddings to predict on cold-start nodes.
result SPARC outperforms existing models on cold-start nodes across tasks.
Content-based news recommendation systems need to recommend news articles based on the topics and content of articles without using user specific information. Many news articles describe the occurrence of specific events and named entities including people, places or objects. In this paper, we propose a graph traversal…
Study bounds self-shrinker entropy using Li-Yau volume and Colding-Minicozzi entropy.
problem Bounding entropy of self-shrinkers in arbitrary codimensions.
method Introduced stable conformal volume and virtual entropy to prove bounds.
result Entropy bounds are sharp and independent of codimension.
The paper investigates quantitative rigidity using Colding's monotonicity formulas for Ricci curvature.
problem Quantifying rigidity in manifolds with nonnegative Ricci curvature.
method Investigates pinching of Colding's monotone functionals and constructs -splitting functions.
result Quantitative control of splitting functions by pinching at independent points controls the distance to the nearest cone.
Graph neural networks improve cold start for new items in recommender systems.
problem Cold start problem for new items in recommender systems.
method Item hierarchy graphs and bespoke graph neural network architecture.
result Our method achieves better forecasting quality than state-of-the-art with comparable computational time.
The item cold-start problem seriously limits the recommendation performance of Collaborative Filtering (CF) methods when new items have either none or very little interactions. To solve this issue, many modern Internet applications propose to predict a new item's interaction from the possessing contents. However, it is…
Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start settingcs.IR
Paper presents content-based models for game recommendation in cold start scenarios.
problem Cold start problem in game recommendation where new games and players have no historical data.
method Uses survey data to develop content-based interaction models that generalize to new games, players, and both.
result Content models outperform collaborative filtering in predicting new interactions.
Study shows rigidity for entropy minimizers in non-monotone cases.
problem Rigidity of entropy minimizers in non-monotone settings.
method Elementary proofs in non-monotone situations.
result Showed rigidity for minimizers of generalized Colding-Minicozzi entropies.
TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Startcs.LG
TransCORALNet uses transformer and CORAL for supply chain credit assessment with cold start.
problem Supply chain credit assessment for new borrowers with limited data.
method Two-stream transformer CORAL networks with domain adaptation and LIME.
result TransCORALNet outperforms state-of-the-art models in accuracy.
Self-shrinkers are the special solutions of mean curvature flow in that evolve by shrinking homothetically; they serve as singularity models for the flow. The entropy of a hypersurface introduced by Colding-Minicozzi is a Lyapunov functional for the mean curvature flow, and is fundamental to their th…
Cold posteriors in BNNs harm performance, likely due to incorrect likelihood.
problem Cold posteriors in Bayesian neural networks degrade performance.
method Developed a generative model explaining cold posteriors and matched it to the tempered likelihoods.
result Cold posteriors are a result of using the wrong likelihood for image classification datasets.
We prove that `volume cone implies metric cone' in the setting of RCD spaces, thus generalising to this class of spaces a well known result of Cheeger-Colding valid in Ricci-limit spaces.
This work explores the ability of collective matrix factorization models in recommender systems to make predictions about users and items for which there is side information available but no feedback or interactions data, and proposes a new formulation with a faster cold-start prediction formula that can be used in rea…
Entropy derived from Colding's volume on Ricci-flat manifolds.
problem Deriving Perelman's entropy from Colding's monotonic volume.
method Applying Colding's monotonic volume to Perelman's N-space for harmonic functions on Ricci-flat manifolds.
result Entropy is the limit of Colding's monotonic volume.
Uniform Laplace comparison for Kähler Ricci flow on Fano manifolds.
problem Proving convergence of Kähler-Ricci flows on Fano manifolds.
method Uniform integral Laplace comparison, Cheeger-Colding theory, and previous results.
result Direct proof of the Hamilton-Tian conjecture on convergence of Kähler-Ricci flows, modulo a codimension 4 singular set.
FAB-COST improves cold-start recommendation accuracy with less data.
problem Cold-start problem in recommendation systems.
method Contextual bandit algorithm using Expectation Propagation and Assumed Density Filtering.
result FAB-COST outperforms Laplace approximation on real data.
Bending the Helicoidmath.DG
We construct Colding-Minicozzi limit minimal laminations in open domains in $\rth$ with the singular set of -convergence being any properly embedded -curve. By Meeks' -regularity theorem, the singular set of convergence of a Colding-Minicozzi limit minimal lamination is a locally finit…
Study minimal surfaces in complex hyperbolic space, linking entropy and volume.
problem Characterize minimal submanifolds in complex hyperbolic space.
method Analyze asymptotic regularity and introduce Colding-Minicozzi entropy and CR-volume.
result Establish a connection between Colding-Minicozzi entropy and CR-volume.
Motivated and inspired by the recent work of Colding [5] and Colding-Minicozzi [6] we derive several families of monotonicity formulas for manifolds with nonnegative Bakry-Emery Ricci curvature, extending the formulas in [5, 6].
New algorithm reduces cold-start costs in multi-armed bandits for many products.
problem High burn-in costs in multi-armed bandits for new products.
method Two-phase bandit algorithm using subsampling and low-rank matrix estimation.
result Reduces burn-in costs and expedites experiment in large product sets.
In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new ite…
Active learning tackles cold start and imbalanced data issues.
problem Cold start problem and imbalanced data in ML models.
method ODAL approach with 3-stage AL policies.
result ODAL can quickly reach high performance models with limited labels.
Study self shrinkers with medium entropy in 4D space.
problem Analyzing self shrinkers with entropy bounds.
method Smooth asymptotically conical self shrinkers in R^4.
result Entropy bounded above by Λ_1.
We prove topological sphere theorems for RCD(n-1, n) spaces which generalize Colding's results and Petersen's result to the RCD setting. We also get an improved sphere theorem in the case of Einstein stratified spaces.
We continue the work of [10], studying properties of digital images determined by fixed point invariants. We introduce pointed versions of invariants that were introduced in [10]. We introduce freezing sets and cold sets to show how the existence of a fixed point set for a continuous self-map restricts the map on the c…
In this paper is to extend the Cheeger-Colding Theory to the class of conic Kahler-Einstein metrics. This extension provides a technical tool for [LTW] in which we prove a version of the Yau-Tian-Donaldson conjecture for Fano varieties with certain singularity.
We use the Dong-Sogge-Zelditch formula to obtain a lower bound for the volume of the nodal sets of eigenfunctions. Our result improves the recent results of Sogge-Zelditch and in dimensions n \leq 5 gives a new proof for the lower bounds of Colding-Minicozzi.
Restricted Boltzman Machines (RBMs) have been successfully used in recommender systems. However, as with most of other collaborative filtering techniques, it cannot solve cold start problems for there is no rating for a new item. In this paper, we first apply conditional RBM (CRBM) which could take extra information in…
Memory-Augmented Meta-Optimization improves cold-start recommendation.
problem Cold-start problem in recommender systems for new users or items.
method Memory-Augmented Meta-Optimization approach with personalized and task-specific memories.
result Significant improvement in cold-start recommendation performance on multiple datasets.
A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Seriesstat.ML
Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has never previously been observed. The latter challenge is the time series version of the cold-start problem seen in recommender systems which…
Entropy for submanifolds in hyperbolic space defined.
problem Entropy for submanifolds in hyperbolic space.
method Entropy defined analogous to Euclidean space.
result Entropy monotonicity along mean curvature flow in low dimensions.
NetDP predicts loan defaults using network data, addressing cold-start issues.
problem Cold-start problem in default prediction for new users.
method Combines unsupervised and supervised network representations, using parameter-server for scalability.
result Effectiveness in cold-start problem, especially for new users.
High-dimensional unimodal distributions can cause MCMC methods to fail.
problem Failure of MCMC methods in high-dimensional unimodal distributions.
method Examples and theoretical analysis of MCMC methods, including Metropolis-Hastings adjusted methods.
result MCMC methods can take an exponential run-time for high-dimensional unimodal distributions.
Cold-start is a very common and still open problem in the Recommender Systems literature. Since cold start items do not have any interaction, collaborative algorithms are not applicable. One of the main strategies is to use pure or hybrid content-based approaches, which usually yield to lower recommendation quality tha…
Among the machine learning applications to business, recommender systems would take one of the top places when it comes to success and adoption. They help the user in accelerating the process of search while helping businesses maximize sales. Post phenomenal success in computer vision and speech recognition, deep learn…
In this paper, we study the structure of the pointed-Gromov-Hausdorff limits of sequences of Ricci shrinkers. We define a regular-singular decomposition following the work of Cheeger-Colding for manifolds with a uniform Ricci curvature lower bound, and prove that the regular part of any Ricci shrinker limit space is co…
In silico drug-target interaction (DTI) prediction is an important and challenging problem in biomedical research with a huge potential benefit to the pharmaceutical industry and patients. Most existing methods for DTI prediction including deep learning models generally have binary endpoints, which could be an oversimp…