Neural Network Representation

Neural network representation research focuses on understanding how information is encoded and processed within artificial neural networks, aiming to improve model interpretability, fairness, and generalization. Current research investigates the relationship between network representations and human cognition, explores methods for removing biases and spurious correlations, and analyzes representational similarity across different architectures (e.g., convolutional neural networks, transformers) and training paradigms. These efforts are crucial for building more reliable and trustworthy AI systems, with implications for diverse fields ranging from computer vision and natural language processing to scientific modeling and medical diagnosis.