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Found 10 papers, 4 papers with code
A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints
Under such distributions, computing the likelihood of even simple constraints is #P-hard.
A Unified Approach to Count-Based Weakly-Supervised Learning
In many cases, these weak labels dictate the frequency of each respective class over a set of instances.
Probabilistically Rewired Message-Passing Neural Networks
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input.
Semantic Strengthening of Neuro-Symbolic Learning
Numerous neuro-symbolic approaches have recently been proposed typically with the goal of adding symbolic knowledge to the output layer of a neural network.
SIMPLE: A Gradient Estimator for $k$-Subset Sampling
$k$-subset sampling is ubiquitous in machine learning, enabling regularization and interpretability through sparsity.
Semantic Probabilistic Layers for Neuro-Symbolic Learning
We design a predictive layer for structured-output prediction (SOP) that can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints.
Performance Evaluation of Infrared Image Enhancement Techniques
There are several types of IR radiation based on the range of wavelength and corresponding frequency.
Neuro-Symbolic Entropy Regularization
We propose a loss, neuro-symbolic entropy regularization, that encourages the model to confidently predict a valid object.
Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction
We study the problem of entity-relation extraction in the presence of symbolic domain knowledge.
Content-Based Image Retrieval Using Multiresolution Analysis Of Shape-Based Classified Images
The second feature vector describes the texture using eigenvalues of the 39 sub-bands that are generated after applying four levels 2D DWT in each channel (red, green and blue channels) of the image.
