A special spine of a three-manifold is said to be poor if it does not contain proper simple subpolyhedra. Using the Turaev-Viro invariants, we establish that every compact three-dimensional manifold M with connected nonempty boundary has a finite number of poor special spines. Moreover, all poor special spines of the m…
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48 results for “poor”
Optimizing machine learning with poor starting points using Nesterov acceleration.
problem The challenge of achieving good performance with machine learning models starting from suboptimal initial conditions.
method Combining Robbins and Monro method for optimal starting points with Nesterov acceleration for poor starting points, especially with minibatch training.
result Nesterov acceleration can improve performance even with poor starting points, especially in the initial iterations.
CHEER boosts poor models using rich model knowledge.
problem Improving performance of models trained on limited data.
method Develops CHEER framework to infuse rich model knowledge into poor models.
result CHEER significantly improves model performance on physiological datasets.
RDPD uses KD to improve models trained on poor data using rich data from other environments.
problem Deploying models in environments with varying data quality and quantity.
method Knowledge distillation from high-quality, rich data to improve low-quality, poor data models.
result RDPD models consistently outperform baselines and state-of-the-art methods across multiple datasets.
Poor approximators found in neural networks and random feature models.
problem Understanding why certain neural networks and models perform poorly in approximating functions.
method Established a scale separation of Kolmogorov width type and applied it to neural networks and random feature models.
result Reproducing kernel Hilbert spaces and two-layer neural networks are poor -approximators for certain functions.
Model shows who pays higher taxes affects wealth distribution.
problem Determining who should pay higher taxes to prevent wealth concentration.
method Dynamic agent model with random wealth multiplicative process and linear tax rate.
result Tax rate structure affects long-term wealth distribution.
Machine learning identifies poor households with simple survey questions.
problem Classifying poor households in data-scarce countries.
method Cross-validation and parameter regularization to reduce overfitting.
result The model accurately predicts poverty using only ten survey questions.
Improves manifold learning by identifying and fixing stuck points.
problem Local minima in nonlinear embedding methods lead to poor data structure visualization.
method Introduces a method to temporarily allow pressured points to use an extra dimension in the embedding space.
result Significantly improves the objective function value of existing methods even after getting stuck in a poor local minimum.
The aim of these notes is to relate covariant stochastic integration in a vector bundle (as in Norris \cite{Norris}) with the usual Stratonovich calculus via the connector $\K:TE \rightarrow E$ (cf. e.g. Paterson \cite{Paterson} or Poor \cite{Poor}) which carries the connection dependence.
Study analyzes mobile money adoption in 3 cultures, finds context-dependent models.
problem Identifying individuals most likely to benefit from mobile money adoption.
method Combining terabyte-scale data from Ghana, Pakistan, and Zambia to develop supervised learning models.
result Models fit on one population do not generalize to another, highlighting context-dependency.
Neural networks memorize exceptions, leading to poor generalization.
problem Memorization of exceptions hinders neural network generalization.
method Formalized memorization-generalization interplay, proposed MAT to shift logits.
result MAT improves generalization by learning robust patterns invariant across distributions.
The study proves poor ideal three-edge triangulations are minimal for certain 3-manifolds.
problem Finding minimal ideal triangulations for specific 3-manifolds.
method Analyzing properties of poor ideal three-edge triangulations and applying them to construct minimal triangulations.
result Poor ideal three-edge triangulations are proven to be minimal for certain 3-manifolds.
Investor skill levels affect optimal portfolio size, study shows.
problem Optimal portfolio size for different skill levels of investors.
method Mathematical methods to study annual and continuous portfolio diversification, regression analysis.
result Strong investors should hold concentrated portfolios, poor investors should hold diversified portfolios.
New bounds for high-dimensional sparse linear bandits, balancing information and regret.
problem Stochastic linear bandits with high-dimensional sparse features.
method Derivation of minimax regret lower and upper bounds for explore-then-commit algorithm.
result Optimal rate of for data-poor regime, complemented by under signal magnitude assumption.
Improved deep Q-learning with SVRG reduces variance and stabilizes training.
problem Excessive variance in gradient estimation hinders deep Q-learning performance.
method Utilized stochastic variance reduced gradient (SVRG) techniques.
result Significantly improved performance on 18 out of 20 Atari games compared to baseline methods.
Poor economies face frequent disruptions that trap them in producing simpler goods.
problem Frequent disruptions in poor economies prevent them from producing complex goods.
method Modeling an evolving input-output network with optimizing agents that adapt to disruptions.
result A poverty trap emerges where disruptions persist despite agents producing simpler goods.
Big investments are fragile and prone to poor outcomes due to uncertainty.
problem The fragility of large capital investments leading to poor returns.
method Characterizing fragility as easily harmed by randomness and analyzing various sources of uncertainty.
result Big capital investments have a disproportionate exposure to uncertainties that can lead to negative returns.
The Bellman error is a poor proxy for value function accuracy, even with all state-action pairs.
problem The Bellman error is a poor proxy for the accuracy of the value function.
method Study of the Bellman equation as a surrogate objective for value prediction accuracy.
result The magnitude of the Bellman error is only weakly related to the distance to the true value function, even with all state-action pairs.
Paper proves metrics of positive Ricci curvature on fiber bundles.
problem Existence of metrics with positive Ricci curvature on fiber bundles.
method Direct and simple proof, generalizing Nash and Poor's results.
result New examples of manifolds with positive Ricci curvature constructed.
Climate volatility reduces economic growth, especially in poorer countries.
problem Impact of climate volatility on economic growth.
method Exploiting data on 133 countries over 59 years, controlling for temperature changes.
result A 1 degree C increase in temperature volatility leads to a 0.3% decline in GDP growth.
Boosted decision trees typically yield good accuracy, precision, and ROC area. However, because the outputs from boosting are not well calibrated posterior probabilities, boosting yields poor squared error and cross-entropy. We empirically demonstrate why AdaBoost predicts distorted probabilities and examine three cali…
Study shows influence functions are poor for neural networks but useful for identifying influential examples.
problem Influence functions misalign with leave-one-out retraining in neural networks.
method Decomposed the discrepancy into five terms and studied their contributions across different architectures and datasets.
result Influence functions are a good approximation to the proximal Bregman response function (PBRF), useful for identifying influential examples.
This paper tackles poor approximations in learning-to-rank algorithms and proposes exact reranking methods.
problem Poor approximations in learning-to-rank algorithms based on convex proxies.
method Exact reranking algorithms based on mathematical programming.
result A relaxed version of the exact problem has the same optimal solution.
The gain-loss ratio is known to enjoy very good properties from a normative point of view. As a confirmation, we show that the best market gain-loss ratio in the presence of a random endowment is an acceptability index and we provide its dual representation for general probability spaces. However, the gain-loss ratio w…
Ubenwa diagnoses birth asphyxia from infant cries.
problem Difficulty in early detection of asphyxia in resource-poor settings.
method Machine learning system for automated infant cry analysis.
result Reduction in time, cost, and skill required for accurate diagnoses.
Machine learning suffers from poor design, data, and evaluation practices.
problem Ad hoc design, poor data hygiene, and lack of statistical rigor in model evaluation.
method Examines the entire machine learning process from design to evaluation, highlighting common pitfalls and providing recommendations.
result Common pitfalls in machine learning research and development are identified and actionable recommendations are provided.
Study user engagement in mobile health apps for health workers in resource-poor settings.
problem Detect churn and tailor content for health workers in mobile health apps.
method Probabilistic and survival analysis of behavioral logs.
result Personalized measures of meaningful engagement can enhance health outcomes.
Escape dynamics and implicit bias of one-pass SGD in overparameterized quadratic networkscond-mat.dis-nn
One-pass SGD dynamics in overparameterized quadratic networks show slow escape from poor solutions.
problem Slow escape from poor generalization solutions in overparameterized neural networks.
method Analysis of one-pass SGD dynamics using ordinary differential equations for overlap matrices.
result Overparameterization only modestly accelerates escape from poor solutions.
Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approachcs.LG
The paper addresses poor calibration in fine-tuned LLMs after preference alignment.
problem Poor calibration in fine-tuned Large Language Models (LLMs) after preference alignment.
method Proposes a calibration-aware fine-tuning approach to restore calibration without compromising model performance.
result Demonstrates the effectiveness of the proposed methods through extensive experiments.
New findings reveal discount regularization can be seen as a strong prior, leading to poor performance in unevenly sampled data.
problem Discount regularization leads to poor performance in unevenly sampled data.
method Equivalence theorem showing discount regularization as a strong prior, setting regularization parameters locally for individual state-action pairs.
result Discount regularization can be seen as a strong prior, leading to poor performance in unevenly sampled data.
Regularizes sparse Gaussian processes for better model performance.
problem Efficient learning of inducing inputs in latent variable models.
method Proposes a regularization approach to balance reconstruction and approximation performance.
result Improves both inference and prediction performance in latent variable models.
New model improves image captioning's ability to describe unseen concepts.
problem Image captioning models struggle with describing unseen combinations of concepts.
method Proposes a multi-task model combining caption generation and image-sentence ranking, with a decoding mechanism to re-rank captions based on image similarity.
result The model significantly outperforms state-of-the-art models in compositional generalization.
Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the l…
New model uses less site-specific data for accurate hydrologic predictions.
problem Accurate rainfall-runoff modeling in data-poor regions.
method Data-driven learned embedding to replace location-specific attributes.
result Achieves state-of-the-art results with significantly less information.
Data mining revealed a cluster of economic, psychological, social and cultural indicators that in combination predicted corruption and wealth of European nations. This prosperity syndrome of self-reliant citizens, efficient division of labor, a sophisticated scientific community, and respect for the law, was clearly di…
We compare observed corporate cumulative default probabilities to those calculated using a stochastic model based on an extension of the work of Black and Cox and find that corporations default as if via diffusive dynamics. The model, based on a contingent-claims analysis of corporate capital structure, is easily calib…
ProMP improves Meta-RL by better handling credit assignment.
problem Poor credit assignment in Meta-RL leads to suboptimal performance.
method Develops a novel meta-learning algorithm controlling pre-adaptation and adapted policies.
result Significantly improves sample-efficiency and asymptotic performance.
We solve S-shaped utility portfolio selection with SD constraints using algorithms and neural networks.
problem Optimizing portfolios with S-shaped utility functions under SD constraints.
method First-order SD constraint solution, numerical algorithm for SSD, neural network approach.
result Effective numerical and neural network solutions for SSD constrained problems.
Proves deep learning has no poor local minima without unrealistic assumptions.
problem The difficulty of training deep neural networks.
method Mathematical proofs for deep linear and nonlinear neural networks.
result Deep neural networks have no poor local minima.
Improved neural network robustness with instance-specific perturbation margins.
problem Adversarial training fails to generalize well to unperturbed test set.
method Instance adaptive adversarial training with sample-specific perturbation margins.
result Test accuracy improves with a marginal drop in robustness.
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to learn re…
Revises precision-recall curves for generative models.
problem Improves evaluation of generative models by distinguishing mode-collapse and quality issues.
method Generalizes PR curve formulation to arbitrary measures, exposes a bridge to error rates, proposes a new algorithm to approximate precision-recall curves.
result Demonstrates the interest of the new formulation over the original approach on multi-modal datasets.
Paper proposes a method to protect DCNNs from adversarial attacks and poor quality images.
problem DCNNs are vulnerable to adversarial examples and perceptual quality issues.
method Combines image quality assessment with knowledge distillation.
result Detector effectively identifies poor quality and adversarial images.
Model explains how stablecoin runs are influenced by large sales and reserve quality.
problem Understanding and predicting stablecoin runs due to large sales and poor reserve quality.
method Global game model addressing both large sales and poor reserve quality, analyzing risk components.
result The probability of a run increases with large sales and decreases with precise public knowledge, but increases with precise private signals when fundamentals are weak.
SYMSGD improves parallel SGD by combining local models to match sequential SGD's results.
problem Inherently sequential nature of SGD limits parallel scalability and convergence.
method SYMSGD combines local models with a combiner to mimic sequential SGD's semantics.
result SYMSGD achieves up to 11x speedup on 16 cores compared to a sequential baseline.
pFedGame uses game theory for decentralized federated learning in dynamic networks.
problem Performance bottlenecks, data bias, model convergence issues, and model poisoning attacks in federated learning.
method pFedGame employs game theory to decentralize federated learning, avoiding a central aggregation server and addressing dynamic network challenges.
result pFedGame achieves higher accuracy (over 70%) in heterogeneous data compared to existing methods.
New federated learning protocols improve on knowledge distillation's poor performance.
problem Designing a universal API for federated learning without public data.
method Proposed Federated Kernel ridge regression using knowledge distillation.
result Performance of new protocols closely matches theoretical predictions.
Enhances reinforcement learning safety through risk-averse exploration.
problem Safety concerns in reinforcement learning due to sub-optimal actions.
method Distributionally robust policy iteration scheme with lower bound guarantees.
result Efficient algorithm that prevents poor decisions and converges to optimal policy.