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Found 6 papers, 2 papers with code
The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing
The pursuit of fairness in machine learning models has emerged as a critical research challenge in different applications ranging from bank loan approval to face detection.
Stress and Adaptation: Applying Anna Karenina Principle in Deep Learning for Image Classification
In our paper, we have generated artificial perturbations to our model by hot-swapping the activation and loss functions during the training.
Fair Classification via Transformer Neural Networks: Case Study of an Educational Domain
Educational technologies nowadays increasingly use data and Machine Learning (ML) models.
Pre-Trained Language Transformers are Universal Image Classifiers
The pretrained transformer of GPT-2 is trained to generate text and then fine-tuned to classify facial images.
Pretrained Language Models are Symbolic Mathematics Solvers too!
In this paper, we present a sample efficient way of solving the symbolic tasks by first pretraining the transformer model with language translation and then fine-tuning the pretrained transformer model to solve the downstream task of symbolic mathematics.
Searching for Topological Symmetry in Data Haystack
To compute the symmetry in a grid structure, we introduce three legal grid moves (i) Commutation (ii) Cyclic Permutation (iii) Stabilization on sets of local grid squares, grid blocks.
