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Found 24 papers, 7 papers with code
IKD+: Reliable Low Complexity Deep Models For Retinopathy Classification
Deep neural network (DNN) models for retinopathy have estimated predictive accuracies in the mid-to-high 90%.
Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification
To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models' confidence and predictive performance in detecting primary brain tumors - gliomas.
Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning
Contemporary model calibration techniques mitigate the problem of overconfident predictions by pushing down the confidence of the winning class while increasing the confidence of the remaining classes across all test samples.
Knowledge-based Analogical Reasoning in Neuro-symbolic Latent Spaces
However, connectionist models struggle to include explicit domain knowledge for deductive reasoning.
Machine Learning in Sports: A Case Study on Using Explainable Models for Predicting Outcomes of Volleyball Matches
Therefore, it is required to provide high-quality interpretable, and understandable explanations for a model's prediction in sports.
Decision Making
Explainable Artificial Intelligence (XAI)
+2
Composition of Relational Features with an Application to Explaining Black-Box Predictors
Using a notion of explanations based on the compositional structure of features in a CRM, we provide empirical evidence on synthetic data of the ability to identify appropriate explanations; and demonstrate the use of CRMs as 'explanation machines' for black-box models that do not provide explanations for their predictions.
Solving Visual Analogies Using Neural Algorithmic Reasoning
We consider a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input/output images are related, so as to analogously transform future inputs.
Using Program Synthesis and Inductive Logic Programming to solve Bongard Problems
The ability to solve Bongard problems is an example of such a test.
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.
Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment
We also provide experimental evidence comparing BotGNNs favourably to multi-layer perceptrons (MLPs) that use features representing a "propositionalised" form of the background knowledge; and BotGNNs to a standard ILP based on the use of most-specific clauses.
Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification
Our experiments are extensive, and we evaluate the predictive performance of our proposed hybrid vision model on seven different image classification datasets from a variety of domains such as digit and object recognition, biometrics, medical imaging.
Incorporating Domain Knowledge into Deep Neural Networks
We present a survey of ways in which domain-knowledge has been included when constructing models with neural networks.
LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting
Given a fill-in-the-blank-type question and a corresponding context, the task is to predict the most suitable word from a list of 5 options.
Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays
In this paper, deep neural networks are used to extract domain-specific features(morphological features like ground-glass opacity and disease indications like pneumonia) directly from the image data.
Incorporating Symbolic Domain Knowledge into Graph Neural Networks
These kinds of problems have been addressed effectively in the past by Inductive Logic Programming (ILP), by virtue of 2 important characteristics: (a) The use of a representation language that easily captures the relation encoded in graph-structured data, and (b) The inclusion of prior information encoded as domain-specific relations, that can alleviate problems of data scarcity, and construct new relations.
Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatility
For the multi-step forecasting (multiple days ahead forecast), we have evaluated the methods for different forecast periods.
A study on the use of Boundary Equilibrium GAN for Approximate Frontalization of Unconstrained Faces to aid in Surveillance
Specifically, the present work is a comprehensive study on the implementation of an auto-encoder based Boundary Equilibrium GAN (BEGAN) to generate frontal faces using an interpolation of a side view face and its mirrored view.
Reliable Evaluation of Neural Network for Multiclass Classification of Real-world Data
The results that are obtained for different parameters for each of the dataset could demonstrate interesting findings to support the usability of these set of performance evaluation parameters.
A Fuzzy MLP Approach for Non-linear Pattern Classification
Pattern classification using multilayer perceptron (MLP) trained with back propagation learning becomes much complex with increase in number of layers, number of nodes and number of epochs and ultimate increases computational time [31].
A Novel Approach for Intelligent Robot Path Planning
Path planning of Robot is one of the challenging fields in the area of Robotics research.
Robotics
Non-Correlated Character Recognition using Artificial Neural Network
Fifty-two sets of English alphabets are used to train the ANN and test the network.
Solution to Quadratic Equation Using Genetic Algorithm
A novel approach for solving Quadratic Equation based on Genetic Algorithms (GAs) is presented.
Time Efficient Approach To Offline Hand Written Character Recognition Using Associative Memory Net
The developed network consumes 3. 57 sec (average) in Serial implementation and 1. 16 sec (average) in Parallel implementation using OpenMP.
English Character Recognition using Artificial Neural Network
This work focuses on development of a Offline Hand Written English Character Recognition algorithm based on Artificial Neural Network (ANN).
