Tanmay Bakshi
Ranking Amazon Reviews by Quality with Pointwise Ratings learned from Pairwise Data
#1about 7 minutes
Ranking Amazon reviews by learning a quality score
The model learns to assign a continuous quality score to reviews by training on a classification task rather than a direct regression problem.
#2about 7 minutes
How neural networks learn through feature embeddings
Neural networks transform complex input data into a high-dimensional embedding space where it becomes linearly separable for classification.
#3about 6 minutes
Processing sequential text data with Word2Vec and RNNs
Word2Vec creates semantic vector representations of words, which are then processed sequentially by Recurrent Neural Networks (RNNs) to understand context.
#4about 5 minutes
Identifying the limitations of recurrent neural networks
Recurrent neural networks are slow due to their sequential nature, lack general-purpose understanding, and struggle to process bidirectional context simultaneously.
#5about 8 minutes
Introducing the BERT and transformer architecture
The Transformer architecture uses self-attention to process text in parallel, enabling BERT to learn general, contextual word representations through masked language modeling.
#6about 6 minutes
Demonstrating self-attention with a character-level model
A simplified character-level model called 'Artist BERT' demonstrates how self-attention works by predicting masked characters in artist names.
#7about 8 minutes
Training a pointwise ranker from pairwise review data
A Siamese network architecture processes pairs of reviews to determine which is better, implicitly learning a pointwise quality score for each individual review.
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