Geoffrey Hinton (Google) (Dec 2016)

Geoffrey Hinton (Google Scientific Advisor) – Lecture 4/16 (Dec 2016)

Chapters

00:00:01 Neural Networks for Relational Learning

Using Backpropagation to Learn Feature Representations:
The goal is to use the backpropagation algorithm to transform relational information into feature vectors that capture word meanings.

A Simple Family Tree Example:
A diagram shows a simple family tree with relationships like son, daughter, husband, and wife. The task is to train a neural network to understand this information.

Expressing Family Tree Information as Propositions:
The information in the family tree can be expressed as a set of propositions using relationships like father, mother, and brother.

Relational Learning as Discovering Regularities:
The relational learning task involves identifying patterns in a large set of triples that express the information in the family trees.

Using a Neural Network to Capture Regularities:
Instead of searching for symbolic rules, a neural network is used to capture the regularities in the data. The network learns to predict the third term of a triple given the first two terms.

Architecture of the Neural Network:
The network architecture is designed manually, with bottlenecks to force the learning of interesting representations.

Local Encoding of People and Relationships:
The input layer uses a local encoding scheme where exactly one neuron is turned on for each person or relationship.

Distributed Encoding of People:
The hidden layer uses a distributed encoding scheme where a person is represented by a pattern of activity over multiple neurons.

Discovering Useful Features through Training:
The network learns to extract features like nationality, branch of family tree, and generation without being explicitly told.

Generalization to Incomplete Triples:
The network can generalize to incomplete triples, correctly completing them even when trained on a subset of the data.

Significance of the Research:
This research demonstrated the ability of backpropagation to learn meaningful features from relational information. It paved the way for applying these techniques to larger datasets and more complex domains.

00:11:21 Neural Network Learning Algorithms and Representations of Concepts

00:22:45 Understanding Word Probabilities for Improved Speech Recognition

00:28:25 Neural Network Language Models for Predicting Word Sequences

Bengio’s Language Model Overview:
To predict the next word accurately, we must convert words into a vector of semantic and syntactic features and use previous words’ features. Bengio pioneered this approach using a network similar to the family trees network applied to a real problem.

Bengio’s Network Architecture:
The network takes an input word index and uses weights to determine the pattern of activity in a hidden layer. This hidden layer contains a distributed representation of the word, i.e., its feature vector, obtained via table lookup and modified through learning. Distributed representations of previous words are used to predict the probabilities of various words that might come next via a softmax output layer. Skip-layer connections from input words to output words improve the model’s performance.

Bengio’s Model Performance:
Bengio’s model initially performed slightly worse than trigrams in predicting the next word but improved when combined with trigrams. Modern language models using feature vectors for words have surpassed trigram models.

Challenges with Large Output Layers:
A large softmax output layer may require dealing with 100,000 different output words, leading to overfitting. Making the last hidden layer small to reduce weights may result in inaccurate probabilities for rare words, which are often relevant.

Exploring Alternative Architectures:
The next video will explore various ways to avoid using 100,000 output units in a softmax layer.

Word Representation Visualization:
An example of word representations learned by a particular method will be shown at the end of the video. Embedding words in a two-dimensional space allows us to see which words have similar representations.

Serial Architecture:
An alternative architecture is a serial architecture where context words and a candidate word are input to the network. The network outputs a score for how good the candidate word is in that context. This approach allows for the use of learned feature vectors for both context words and candidate words.

00:34:33 Neural Network Language Models for Natural Language Processing

Abstract

Unlocking the Secrets of Relational Learning and Language Modeling with Neural Networks

Revolutionizing Relational Learning: Neural Networks’ Mastery Over Data Structures

In the field of relational learning, a groundbreaking study in the 1980s set the stage for future advancements. Researchers discovered that neural networks, utilizing backpropagation, could effectively learn from relational data, like family trees. This process involved extracting meaningful features that captured the essence of the data’s structure and regularities. Intriguingly, these networks encoded entities through distributed representations, allowing them to draw parallels and distinctions between different entities. Such encoding led to the automatic emergence of relevant features, such as nationality or family branches, without requiring explicit guidance.

Using Backpropagation to Learn Feature Representations:

The goal of this approach is to use the backpropagation algorithm to transform relational information into feature vectors that capture word meanings. This process involves expressing relational data as propositions, identifying patterns in the data, and utilizing a neural network to learn the regularities. The network architecture is designed manually, with bottlenecks to force the learning of interesting representations.

A Simple Family Tree Example:

Consider a simple family tree with relationships like son, daughter, husband, and wife. The task is to train a neural network to understand this information. The information in the family tree can be expressed as a set of propositions using relationships like father, mother, and brother.

Expressing Family Tree Information as Propositions:

The information in the family tree can be expressed as a set of propositions using relationships like father, mother, and brother. The relational learning task involves identifying patterns in a large set of triples that express the information in the family trees.

Relational Learning as Discovering Regularities:

Instead of searching for symbolic rules, a neural network is used to capture the regularities in the data. The network learns to predict the third term of a triple given the first two terms.

Using a Neural Network to Capture Regularities:

A neural network is used to capture the regularities in the data. The network architecture is designed manually, with bottlenecks to force the learning of interesting representations. The input layer uses a local encoding scheme where exactly one neuron is turned on for each person or relationship. The hidden layer uses a distributed encoding scheme where a person is represented by a pattern of activity over multiple neurons.

Architecture of the Neural Network:

The architecture of the neural network is designed manually, with bottlenecks to force the learning of interesting representations. The input layer uses a local encoding scheme where exactly one neuron is turned on for each person or relationship. The hidden layer uses a distributed encoding scheme where a person is represented by a pattern of activity over multiple neurons.

Local Encoding of People and Relationships:

The input layer uses a local encoding scheme where exactly one neuron is turned on for each person or relationship. The hidden layer uses a distributed encoding scheme where a person is represented by a pattern of activity over multiple neurons.

Distributed Encoding of People:

The hidden layer uses a distributed encoding scheme where a person is represented by a pattern of activity over multiple neurons. The network learns to extract features like nationality, branch of family tree, and generation without being explicitly told.

Discovering Useful Features through Training:

The network learns to extract features like nationality, branch of family tree, and generation without being explicitly told. The network can generalize to incomplete triples, correctly completing them even when trained on a subset of the data.

Generalization to Incomplete Triples:

The network can generalize to incomplete triples, correctly completing them even when trained on a subset of the data. This research demonstrated the ability of backpropagation to learn meaningful features from relational information. It paved the way for applying these techniques to larger datasets and more complex domains.

Bridging Theories in Cognitive Science through Neural Networks

Another significant insight emerged from the debate between feature theory and structuralist theory in cognitive science. While these theories presented concepts as sets of semantic features or as relational entities, respectively, Geoffrey Hinton proposed a unifying view. He suggested that neural networks could use vectors of semantic features to create a relational graph, allowing for explicit inference without following rigid rules of inference. This approach, using distributed representation, paved the way for a more nuanced understanding of cognitive processes.

Advancements in Language Modeling: Beyond Traditional Methods

Language modeling witnessed a transformative change with the introduction of neural networks. Traditional methods like the trigram model, which predicted words based on the frequency of word triples, faced limitations in understanding semantic relationships and generalizing from past experiences. In contrast, Yoshua Bengio’s approach used neural networks with a distributed representation of words, allowing for a more profound understanding of word relationships by converting them into vectors of semantic and syntactic features.

Statistical Language Models and Trigrams:

Statistical language models were popular before neural networks. One widely used method was the trigram model, which estimated the probability of a word based on the two preceding words.

Background:

Geoffrey Hinton discusses the relationship between the cost function and the output of a neural network. He explains how the steep derivative of the cost function balances the flat output change. Feature vectors can be used to represent words in speech recognition systems. These systems predict the next word in a sequence based on the previous words.

Speech Recognition and Feature Vectors:

Feature vectors can be used to represent words in speech recognition systems. These systems predict the next word in a sequence based on the previous words.

Trigram Method:

The trigram method is a standard technique for predicting the probability of the next word. It involves counting the frequencies of triples of words in a large text corpus. The relative probability of a word given the previous two words is calculated from these counts.

Limitations of the Trigram Method:

The trigram method is limited by the number of possible word combinations. It cannot handle contexts larger than two previous words due to data sparsity. It fails to utilize the similarities between words with similar meanings or related contexts.

The challenge, however, was managing a large softmax output layer, which could lead to overfitting and difficulties in accurately predicting probabilities for rare words. To overcome this, a serial architecture was proposed. This architecture allowed for the efficient scoring of candidate words in a given context, significantly reducing the computational burden. The network learned to predict the next word by adjusting weights based on the prediction error, resulting in more accurate language models than the traditional trigram approach.

Visualizing these word representations became crucial in understanding the relationships and semantic subtleties captured by the network. Tools like t-SNE provided two-dimensional maps that revealed clusters of semantically similar words, illustrating the network’s ability to discern nuanced differences.

Innovative Techniques for Efficient Word Prediction

Efficiency in word prediction was further enhanced by strategies like limiting candidate words to a smaller set and utilizing binary tree structures for efficient organization. These methods reduced computational demands and streamlined the learning process. Colabao and Weston’s approach, focusing on learning feature vectors without explicitly predicting the next word, marked another leap forward. This method trained networks to differentiate correct from random words within a context, leading to feature vectors that captured semantic distinctions and relationships.

A New Era in Natural Language Processing

The integration of neural networks into relational learning and language modeling has led to a revolution in natural language processing. These networks have not only mastered the art of predicting words with remarkable accuracy but also developed a deep understanding of word relationships and semantics. As we continue to refine these models, we stand on the brink of unlocking even greater capabilities in machine learning and artificial intelligence, promising exciting advancements in various applications from automated translation to intelligent personal assistants.

Notes by: Rogue_Atom

Geoffrey Hinton (Google Scientific Advisor) – Lecture 4/16 (Dec 2016)

Chapters

Abstract

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