Results 91 to 100 of about 55,142 (282)
Advanced Intelligent Systems, EarlyView.An instance‐level, model‐agnostic explanation of class differentiation is introduced through SHAP‐LCD, linking probability shifts to feature‐wise Shapley contributions. The method operates on tabular and image data and is released in a fully reproducible implementation, offering a transparent way to examine, at each instance, why predictive models ...
Roxana M. Romero Luna +2 more
wiley +1 more source
On Explainability of Cluster Prototypes with Rough Sets: A Case Study in the FMCG Market
International Journal of Applied Mathematics and Computer ScienceDespite the growing popularity of machine learning (ML), such solutions are often incomprehensible to employees and difficult to control. Addressing this issue, we discuss some essential problems of explainable ML applications in the fast-moving consumer
Grzegorowski Marek +5 more
doaj +1 more source
Contractions of Filippov algebras
, 2011 We introduce in this paper the contractions $\mathfrak{G}_c$ of $n$-Lie (or Filippov) algebras $\mathfrak{G}$ and show that they have a semidirect structure as their $n=2$ Lie algebra counterparts.
Horvathy P. A. +7 more
core +1 more source
Anatomical Sciences Education, EarlyView.Abstract Multimodal large language models (LLMs) are now deeply integrated into medical education and widely used by medical students, yet it remains unclear whether current models possess the accuracy and reliability needed to support image‐based learning.
Ming Lu, Josiah Cheng, Vinod Gopalan
wiley +1 more source
Compressed Sensing Performance Analysis via Replica Method using Bayesian framework [PDF]
, 2014 Compressive sensing (CS) is a new methodology to capture signals at lower rate than the Nyquist sampling rate when the signals are sparse or sparse in some domain.
Barzideh, Faraz +2 more
core
On Advice Complexity of the k-server Problem under Sparse Metrics
, 2013 We consider the k-server problem under the advice model of computation when the underlying metric space is sparse. On one side, we show that an advice of size {\Omega}(n) is required to obtain a 1-competitive algorithm for sequences of size n, even for ...
Gupta, Sushmita +2 more
core +1 more source
Overcoming Catastrophic Forgetting by XAI
CoRR, 2022 Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life. Taking the advantages of interpretable machine learning (interpretable ML), this work proposes a novel tool called Catastrophic Forgetting Dissector (or CFD) to explain ...
openaire +2 more sources
Biotechnology and Bioengineering, EarlyView.ABSTRACT The growing demand for biopharmaceutical products reflects their effectiveness in medical treatments. However, developing new biopharmaceuticals remains a major bottleneck, often taking up to a decade before market approval. Machine learning (ML) models have the potential to accelerate this process, but their success depends on access to large
Mohammad Golzarijalal +2 more
wiley +1 more source
The Role of Normalization in the Belief Propagation Algorithm [PDF]
, 2011 An important part of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov Random Field.
Furtlehner, Cyril +2 more
core +3 more sources