Neurosymbolic programming

S Chaudhuri, K Ellis, O Polozov, R Singh… - … and Trends® in …, 2021‏ - nowpublishers.com
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 …

A review of some techniques for inclusion of domain-knowledge into deep neural networks

T Dash, S Chitlangia, A Ahuja, A Srinivasan - Scientific Reports, 2022‏ - nature.com
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 …

Harnessing deep neural networks with logic rules

Z Hu, X Ma, Z Liu, E Hovy, E **ng - arxiv preprint arxiv:1603.06318, 2016‏ - arxiv.org
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 …

MRMD2. 0: a python tool for machine learning with feature ranking and reduction

S He, F Guo, Q Zou, fnm HuiDing - Current Bioinformatics, 2020‏ - benthamdirect.com
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 …

MissForest—non-parametric missing value imputation for mixed-type data

DJ Stekhoven, P Bühlmann - Bioinformatics, 2012‏ - academic.oup.com
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 …

Neural-logic human-object interaction detection

L Li, J Wei, W Wang, Y Yang - Advances in Neural …, 2023‏ - proceedings.neurips.cc
The interaction decoder utilized in prevalent Transformer-based HOI detectors typically
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 …

[PDF][PDF] An analysis of Bayesian classifiers

P Langley, W Iba, K Thompson - Aaai, 1992‏ - Citeseer
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 …

[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 …

Knowledge-based artificial neural networks

GG Towell, JW Shavlik - Artificial intelligence, 1994‏ - Elsevier
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 …