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Neurosymbolic programming
We survey recent work on neurosymbolic programming, an emerging area that bridges the
areas of deep learning and program synthesis. Like in classic machine learning, the goal …
areas of deep learning and program synthesis. Like in classic machine learning, the goal …
A review of some techniques for inclusion of domain-knowledge into deep neural networks
We present a survey of ways in which existing scientific knowledge are included when
constructing models with neural networks. The inclusion of domain-knowledge is of special …
constructing models with neural networks. The inclusion of domain-knowledge is of special …
Harnessing deep neural networks with logic rules
Combining deep neural networks with structured logic rules is desirable to harness flexibility
and reduce uninterpretability of the neural models. We propose a general framework …
and reduce uninterpretability of the neural models. We propose a general framework …
MRMD2. 0: a python tool for machine learning with feature ranking and reduction
Aims: The study aims to find a way to reduce the dimensionality of the dataset. Background:
Dimensionality reduction is the key issue of the machine learning process. It does not only …
Dimensionality reduction is the key issue of the machine learning process. It does not only …
MissForest—non-parametric missing value imputation for mixed-type data
Motivation: Modern data acquisition based on high-throughput technology is often facing the
problem of missing data. Algorithms commonly used in the analysis of such large-scale data …
problem of missing data. Algorithms commonly used in the analysis of such large-scale data …
Neural-logic human-object interaction detection
The interaction decoder utilized in prevalent Transformer-based HOI detectors typically
accepts pre-composed human-object pairs as inputs. Though achieving remarkable …
accepts pre-composed human-object pairs as inputs. Though achieving remarkable …
[ספר][B] Artificial intelligence: a new synthesis
NJ Nilsson - 1998 - books.google.com
Intelligent agents are employed as the central characters in this introductory text. Beginning
with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to …
with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to …
[PDF][PDF] An analysis of Bayesian classifiers
In this paper we present an average-case analysis of the Bayesian classi er, a simple
probabilistic induction algorithm that fares remarkably well on many learning tasks. Our …
probabilistic induction algorithm that fares remarkably well on many learning tasks. Our …
[ספר][B] Credit scoring and its applications
L Thomas, J Crook, D Edelman - 2017 - SIAM
Credit Scoring and Its Applications, Second Edition : Back Matter Page 1 Bibliography [1]
Acharya, VV, Bharath, ST, and Srinivasan, A. (2007) Does industry-wide distress affect …
Acharya, VV, Bharath, ST, and Srinivasan, A. (2007) Does industry-wide distress affect …
Knowledge-based artificial neural networks
Hybrid learning methods use theoretical knowledge of a domain and a set of classified
examples to develop a method for accurately classifying examples not seen during training …
examples to develop a method for accurately classifying examples not seen during training …