Incorrect fixed point results in digital topology are corrected.
problem Incorrect fixed point results in digital topology.
method Analysis of recent papers in digital topology.
result Corrected incorrect conclusions in fixed point results.
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48 results for “incorrect conclusions”
Several recent papers in digital topology have sought to obtain fixed point results by mimicking the use of tools from classical topology, such as complete metric spaces and homotopy invariant fixed point theory. We show that in many cases, researchers using these tools have derived conclusions that are incorrect or tr…
Traders underestimated risk-free rates, leading to poor investments.
problem Incorrect setting of risk-free rates by traders.
method Analysis of investment decisions and financial models.
result Underestimating risk-free rates led to flawed investment decisions.
Corrects errors in previous work on linear elasticity calculations.
problem Incorrect formulas and conclusions in previous research on linear elasticity.
method Identifying and correcting errors in previous calculations and conclusions.
result Proves the correctness of the main theorem in a previous paper.
On Binscatterecon.EM
Enhances binscatter method for better visualization and econometrics.
problem Incorrect conclusions from covariate adjustment in binscatter.
method Formalizes binscatter properties, introduces new tools for estimation and uncertainty quantification.
result Substantially different results from prior methods in applications.
New research shows calibration error is flawed when dealing with model uncertainty.
problem Current model evaluation techniques conflate model uncertainty with aleatoric uncertainty.
method Posterior predictive checks to evaluate deep learning models.
result Calibration error and variants are incorrect when model uncertainty is present.
Explainable AI improves human decision accuracy but does not enhance it significantly.
problem Improving human decision-making through explainable AI.
method Comparing human decision accuracy with and without AI predictions, including or excluding explanations.
result Providing AI predictions improves human decision accuracy, but explanations do not significantly enhance it.
New approach identifies and explains errors in machine learning pipelines.
problem Challenges in identifying and explaining errors in complex machine learning pipelines.
method Uses iteration and provenance to automatically infer root causes of failures.
result Significantly improves precision and recall compared to state-of-the-art methods.
Corrects errors in previous work on spectral asymptotics in elasticity.
problem Two-term asymptotics of elastic eigenvalues on Riemannian manifolds.
method Strongly continuous semigroups and pseudodifferential operators.
result Theorem 1.1 in \cite{Liu-21} is rigorously proven.
Critiques incorrect fixed point assertions in digital topology.
problem Incorrect or incorrectly proven fixed point assertions in digital topology.
method Critical review of existing assertions.
result Identifies and critiques incorrect fixed point assertions.
Corrects incorrect assertions about fixed points in digital topology.
problem Incorrect or incorrectly proven assertions about fixed points in digital metric spaces.
method Analysis of existing assertions and proofs.
result Identifies and corrects errors in published assertions.
The paper introduces a model to measure ASR fairness, addressing key issues.
problem Measuring fairness in ASR systems for different subgroups.
method Mixed-effects Poisson regression to control nuisance factors and handle unobserved heterogeneity.
result The method effectively addresses WER gaps among subgroups and is flexible for practical analyses.
Incorrect fixed point assertions in digital topology are discussed.
problem Incorrect or poorly stated fixed point assertions in digital topology.
method Discussion of problematic publications in digital metric spaces.
result Clarification of incorrect fixed point assertions.
Incorrect fixed point assertions in digital topology are discussed.
problem Incorrect, incorrectly proven, or trivial fixed point assertions in digital topology.
method Continues earlier work on identifying and critiquing bad fixed point assertions.
result Clarifies the nature and extent of incorrect fixed point assertions in digital topology.
The paper corrects and improves previous assertions in digital topology.
problem Incorrect or poorly proven assertions in digital topology.
method Review and correction of existing assertions.
result Improved and corrected assertions in digital topology.
The paper discusses methods for interval estimation of coefficients in penalized regression models for insurance data.
problem Valid inference on coefficients after feature selection in GLM family for insurance data.
method Proposes methodologies for constructing confidence intervals of coefficients after feature selection in GLM family.
result Valid inference on coefficients after feature selection in GLM family for insurance data.
Study shows AD for neural nets with machine-representable numbers can be incorrect.
problem Correctness of AD for neural nets with machine-representable numbers.
method Analyzed two sets of parameters: incorrect and non-differentiable. Proved bounds and conditions for AD correctness.
result AD can be incorrect for machine-representable numbers, but provides a Clarke subderivative on non-differentiable set.
Develops a theory for equivariant networks with partial domain symmetry.
problem Limited analysis of equivariant networks with partial domain symmetry.
method Proposes pointwise definitions of correct, incorrect, and extrinsic equivariance.
result Establishes error lower bounds for networks with partial symmetry.
A new CI test avoids information loss in discretized data.
problem Incorrect CI conclusions from discretized data.
method Proposes a sample-efficient CI test using GMM and nodewise regression.
result Derives an accurate test statistic and establishes its asymptotic distribution.
Interactions in information spread: quantification and interpretation using stochastic block modelscs.LG
New model quantifies interactions' role in real-world phenomena.
problem Understanding the role of interactions in real-world phenomena.
method Interactive Mixed Membership Stochastic Block Model (IMMSBM).
result Interactions significantly improve model predictive power.
Paper finds previous work on submanifolds incorrect.
problem Incorrect definition of semi-invariant submanifolds.
method Examined previous work's definition and found it flawed.
result Previous results on semi-invariant submanifolds are invalid.
Fixed point assertions in digital topology are often incorrect or poorly stated.
problem Fixed points in digital metric spaces
method Discussing publications with bad assertions
result Identifying and correcting errors in fixed point assertions
Fine-tuning neural networks to guarantee performance on specific examples can also introduce incorrect inputs.
problem Ensuring reliable performance of neural networks on specific examples.
method Using SMT solvers to fine-tune ReLU neural networks to guarantee outcomes on a finite set of particular examples.
result Fine-tuning can introduce incorrect inputs that trigger unexpected performance.
This paper was withdrawn as Lemma 2 is incorrect.
Withdrawn by the authors, the main theorem is incorrect
Decision trees can be biased towards minority class, contrary to belief.
problem Bias in decision trees towards minority class in imbalanced datasets.
method Critical evaluation of past literature, specific conditions analysis, tree-fitting adjustments, and post-hoc calibration methods.
result Decision trees can be biased towards minority class under specific conditions, not always towards majority.
Study on numerical reliability of AD for MaxPool in neural nets.
problem Reliability of automatic differentiation for nonsmooth operations like MaxPool.
method Investigation across precision levels and architectures on various datasets.
result Lower norms of nonsmooth Jacobians help maintain stable learning.
A Fake $CP^2 #4 {\bar CP^2}$math.GT
This paper is not ready for public consumption, as the last step (Figure 20) is incorrect.
Section 1.3 was incorrect, and 2.1 will be removed from further submissions. A rewritten version will be posted in the future.
Incorrect parity-based descriptions of realizable Gauss diagrams found, but bipartite graphs provide a valid approach.
problem Incorrect descriptions of realizable Gauss diagrams using parity conditions.
method Used bipartite graphs to describe realizable Gauss diagrams.
result Realizable Gauss diagrams can be accurately described using bipartite graphs.
Paper proposes methods to learn with multiple incorrect labels per example.
problem Learning with a single incorrect label per example limits potential.
method Proposes a novel problem setting allowing multiple incorrect labels per example and two learning methods.
result Demonstrates improved learning with multiple incorrect labels compared to single incorrect labels.
Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data. We prove that if the true causal network might be an arbitrary, linear Gaussian network or a discrete Bayes network, then every unambiguous causal conclusion produced by a consistent method f…
Deep learning models are evaluated for sensory information processing.
problem Unclear interpretation of model comparison techniques for DNNs.
method Explicitly define conclusions from existing model comparison techniques.
result Stronger conclusions about sensory processing mechanisms possible with DNNs.
Choosing a reference group in Oaxaca-Blinder decomposition can reverse conclusions.
problem The choice of reference group in Oaxaca-Blinder decomposition can lead to different conclusions.
method The study uses the Oaxaca-Blinder decomposition to investigate how the choice of reference group affects the results.
result The Oaxaca-Blinder decomposition can yield different conclusions based on the choice of reference group.
I show that Matsumoto conjectured inequality between relative length and Finsler length is false. The incorrectness of the claim is easily inferred from the geometry of the indicatrix.
A minimalist approach improves LLM reasoning by filtering incorrect responses.
problem Improving large language model (LLM) reasoning on complex tasks.
method Revisit GRPO from a reinforce-like algorithm perspective, proposing Reinforce-Rej.
result RAFT, a simple rejection sampling baseline, outperforms GRPO and PPO.
Improves estimation under model misspecification with fake features.
problem Model misspecification with fake features.
method Proposes a framework to decompose output error into underlying, fake, and missing features.
result Fake features can significantly improve estimation performance, even when not correlated with underlying features.
The Comment cond-mat/0503325 is built around two core statements, both of which are plainly incorrect.
New partial models correct for confounding effects in reinforcement learning.
problem Confounding effects in partial models lead to incorrect planning.
method Introduces causally correct partial models for reinforcement learning.
result Causally correct partial models avoid confounding effects and improve planning accuracy.
The results in the recently posted manuscript arXiv:math/0405153v3 are incorrect. The correct version of the aimed results is not original. The preprint contains material from references that are not properly quoted.
Work shows hallucination detection by LLMs is impossible without expert feedback.
problem Detecting hallucinations in LLMs is theoretically impossible without expert-labeled feedback.
method Investigated hallucination detection using a theoretical framework inspired by language identification.
result Automated hallucination detection is impossible for most language collections without expert-labeled feedback.
RRN accurately identifies landmarks in CMF bones without segmentation.
problem Accurately identifying landmarks in craniomaxillofacial bones without segmentation.
method End-to-end RRN architecture using dense-block units for landmark imputation.
result RRN achieves <2 mm RMS error in landmarking.
New method captures multimodal disconnectivity in schizophrenia.
problem Misinterpretation of single modality data in schizophrenia research.
method Gaussian graphical model and modularity-based approach on multimodal data.
result Identifies missing links in schizophrenia's default mode network.
This paper proposes a novel type of random forests called a denoising random forests that are robust against noises contained in test samples. Such noise-corrupted samples cause serious damage to the estimation performances of random forests, since unexpected child nodes are often selected and the leaf nodes that the i…
Paper warns of metric deformation in manifold learning, leading to incorrect answers.
problem Metric deformation in manifold learning.
method Analysis of manifold learning techniques.
result Metric deformation can lead to incorrect answers in manifold learning.
The Frölicher spectral sequence of a compact complex manifold measures the difference between Dolbeault cohomology and de Rham cohomology. We construct for nilmanifolds with left-invariant complex structure such that the -th differential does not vanish. This replaces an earlier incorrect e…
E-scores assess LLM outputs for correctness, addressing p-hacking issues.
problem Limited principled mechanisms to assess generative model correctness.
method Use e-values to complement LLM outputs with e-scores, providing flexibility in tolerance levels.
result Achieves guarantees of correctness assessment and upper bounds size distortion.
We study Bayesian discriminative inference given a model family $p(c,\x, θ)$ that is assumed to contain all our prior information but still known to be incorrect. This falls in between "standard" Bayesian generative modeling and Bayesian regression, where the margin $p(\x,θ)$ is known to be uninformative about $p(c|\x,…