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Found 11 papers, 5 papers with code
Demonstrations Are All You Need: Advancing Offensive Content Paraphrasing using In-Context Learning
Paraphrasing of offensive content is a better alternative to content removal and helps improve civility in a communication environment.
Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data
In this paper, we report the results of a detailed study that compares and contrasts various common choices and some hybrid data augmentation strategies in KD based human activity analysis.
Towards Explainable Student Group Collaboration Assessment Models Using Temporal Representations of Individual Student Roles
Collaboration is identified as a required and necessary skill for students to be successful in the fields of Science, Technology, Engineering and Mathematics (STEM).
Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint
In addition to these findings, our work also presents a new application of the OS regularizer in healthcare, increasing the post-hoc interpretability and performance of deep learning models for COVID-19 classification to facilitate adoption of these methods in clinical settings.
Role of Orthogonality Constraints in Improving Properties of Deep Networks for Image Classification
Standard deep learning models that employ the categorical cross-entropy loss are known to perform well at image classification tasks.
A Machine Learning Approach to Assess Student Group Collaboration Using Individual Level Behavioral Cues
K-12 classrooms consistently integrate collaboration as part of their learning experiences.
Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson's Disease Classification of Gait Patterns
We show that the features extracted for the target dataset can be used to train an effective classification model.
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification
We find that although the proposed geometrically constrained loss-function improves quantitative results modestly, it has a qualitatively surprisingly beneficial effect on increasing the interpretability of deep-net decisions as seen by the visual explanations generated by techniques such as the Grad-CAM.
Topological Descriptors for Parkinson's Disease Classification and Regression Analysis
In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson's disease classification and severity assessment.
PI-Net: A Deep Learning Approach to Extract Topological Persistence Images
To the best of our knowledge, we are the first to propose the use of deep learning for computing topological features directly from data.
Perturbation Robust Representations of Topological Persistence Diagrams
However, persistence diagrams are multi-sets of points and hence it is not straightforward to fuse them with features used for contemporary machine learning tools like deep-nets.
