{'fraction_non_alphanumeric': 0.035164835164835165, 'fraction_numerical': 0.015384615384615385, 'mean_word_length': 4.588957055214724, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
CS81: Learning words with Deep Belief Networks

In this project, we use a Deep Belief Network (Hinton et al., 2006) to learn words in a fixed-size vocabulary, given input in multiple modalities (image and audio data). The goal of this project is like that of Plunkett et al. (1992): to model vocabulary acquisition, and address the Symbol Grounding problem from a connectionist standpoint. Our model learns to classify both spoken and hand-written digits in three distinct learning tasks. First, we train our network only on the image data, second, we train only on the audio data, and finally, we train on a combined dataset of paired image and audio data. Unlike Plunkett et al. (1992), we use a generative model, which allows us to fix the class labels and generate input vectors that our model considers good representatives of that class. The model also achieves high accuracy on the classification tasks.