The paper "Visualizing and Understanding Recurrent Networks" by Andrej Karpathy, Justin Johnson, and Li Fei-Fei from Stanford University explores the performance and limitations of Long Short-Term Memory (LSTM) networks, a variant of Recurrent Neural Networks (RNNs), through character-level language models. The authors aim to bridge the gap between practical success and theoretical understanding by analyzing the representations, predictions, and error types of LSTMs. They find that LSTMs can learn interpretable cells that track long-range dependencies such as line lengths, quotes, and brackets. Comparative analysis with finite horizon $n$-gram models reveals that LSTMs perform better on characters requiring long-range reasoning. The paper also conducts an error analysis, breaking down remaining errors into categories and suggesting areas for further study. The results highlight the importance of long-range structural dependencies and the need for new architectural improvements to address remaining limitations.The paper "Visualizing and Understanding Recurrent Networks" by Andrej Karpathy, Justin Johnson, and Li Fei-Fei from Stanford University explores the performance and limitations of Long Short-Term Memory (LSTM) networks, a variant of Recurrent Neural Networks (RNNs), through character-level language models. The authors aim to bridge the gap between practical success and theoretical understanding by analyzing the representations, predictions, and error types of LSTMs. They find that LSTMs can learn interpretable cells that track long-range dependencies such as line lengths, quotes, and brackets. Comparative analysis with finite horizon $n$-gram models reveals that LSTMs perform better on characters requiring long-range reasoning. The paper also conducts an error analysis, breaking down remaining errors into categories and suggesting areas for further study. The results highlight the importance of long-range structural dependencies and the need for new architectural improvements to address remaining limitations.