ChatGPT تسجيل الدخول أنت قلت: Give me a title and description for this presentation: Recursive Neural Network and title is 40 characters and description is 3000 characters قال ChatGPT: Sure! Here's a title that fits the 40-character limit and a detailed description up to 3000 characters: Title: Understanding Recursive Neural Networks Description: Recursive Neural Networks (RecNNs) are a class of deep learning models that apply the same set of weights recursively over structured input, such as trees. Unlike traditional feedforward or recurrent neural networks that handle flat or sequential data, RecNNs are designed to work on hierarchical structures, making them especially powerful in areas like natural language processing, sentiment analysis, and syntactic parsing. This presentation delves into the foundational concepts, architecture, and applications of Recursive Neural Networks. We begin by exploring the motivation behind using recursive models, particularly in scenarios where input data has an inherent recursive or tree-like structure—such as parsing a sentence into its grammatical components or analyzing nested expressions in programming languages. We will examine the architecture of a basic RecNN, where the same function is applied at each node of a tree, and how it differs from Recurrent Neural Networks (RNNs). Special attention will be given to the compositional nature of RecNNs, where child nodes are combined to produce parent node representations, allowing the model to learn complex structural dependencies. The presentation also covers advanced variants like the Tree-LSTM, which enhances the standard RecNN by incorporating long short-term memory units to better capture long-range dependencies and reduce vanishing gradient issues. We'll explore how these models are trained using gradient-based optimization techniques and how backpropagation through structure (BPTS) is applied in this context. Real-world applications of RecNNs are showcased, particularly in sentiment classification where the model interprets sentiment based on the syntactic structure of sentences rather than just the presence of keywords. We’ll look into benchmark datasets such as the Stanford Sentiment Treebank and how RecNNs outperform traditional models in capturing nuanced meanings. Challenges and limitations of RecNNs will also be discussed, including issues with scalability, training complexity, and the need for structured input like parse trees. We will touch upon recent research efforts aimed at overcoming these hurdles, including hybrid models and unsupervised approaches to structure learning. By the end of the presentation, attendees will have a comprehensive understanding of Recursive Neural Networks, their mathematical underpinnings, practical uses, and potential for future research. This talk is geared toward students, researchers, and professionals with a basic understanding of neural networks who are interested in exploring more structured.

1. Deep Learning Srihari
Recursive Neural Networks
Sargur Srihari
srihari@buffalo.edu
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2. Deep Learning Srihari
Topics
• Sequence Modeling: Recurrent and Recursive Nets
1. Unfolding Computational Graphs
2. Recurrent Neural Networks
3. Bidirectional RNNs
4. Encoder-Decoder Sequence-to-Sequence Architectures
5. Deep Recurrent Networks
6. Recursive Neural Networks
7. The Challenge of Long-Term Dependencies
8. Echo-State Networks
9. Leaky Units and Other Strategies for Multiple Time Scales
10.LSTM and Other Gated RNNs
11. Optimization for Long-Term Dependencies
12.Explicit Memory
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3. Deep Learning Srihari
Recursive Neural Networks
• They are yet another generalization of recurrent networks with
a different kind of computational graph
• It is structured as a deep tree, rather than the chain structure of
RNNs
• The typical computational graph for a recursive network is
shown next
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4. Deep Learning Srihari
Computational graph of a Recursive Network
• It generalizes a recurrent
network from a chain to a tree
• A variable sequence x(1),x(2),,x(t)
can be mapped to a fixed size
representation (the output o), with
a fixed set of parameters (the
weight matrices U,V,W)
• Figure illustrates supervised
learning case in which target y is
provided that is associated with
the whole sequence
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5. Deep Learning Srihari
Advantage of Recursive over Recurrent Nets
• For a sequence of the same length τ, the depth (measured as
the no. of compositions of nonlinear operations) can be reduced
from τ to O(log τ), which might help deal with long-term
dependencies
• An open question is how best to structure the tree
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6. Deep Learning Srihari
Need for Recursive nets in NLP
• Deep learning based methods learn low-dimensional, real-
valued vectors for word tokens, mostly from a large data
corpus, successfully capturing syntactic and semantic
aspects of text
• For tasks where the inputs are larger text units, e.g.,
phrases, sentences or documents, a compositional model
is first needed to aggregate tokens into a vector with fixed
dimensionality that can be used for other NLP tasks
• Models for achieving this fall into two categories: recurrent
models and recursive models
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7. Deep Learning Srihari
Recurrent Model for NLP
• Recurrent models deal successfully with time series data
• The recurrent models generally consider no linguistic structure
aside from the word order
• They were applied early on to NLP by modeling a sentence as
tokens processed sequentially and at each step combining the
current token with previously built embeddings
• Recurrent models can be extended to bidirectional ones from
both left to right and right to left
• These models consider no linguistic structure aside from word
order
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8. Deep Learning Srihari
Recursive Models for NLP
• Recursive neural models (also referred to as tree models) by
contrast are structured by syntactic parse trees
• Instead of considering tokens sequentially, recursive models
combine neighbors based on the recursive structure of parse
trees, starting from the leaves and proceeding recursively in a
bottom-up fashion until the root of the parse tree is reached
• Ex: for the phrase the food is delicious, following the operation sequence
((the food) (is delicious)) rather than the sequential order (((the food) is)
delicious)
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9. Deep Learning Srihari
Advantage of Recursive Model for NLP
• They have the potential of capturing long-distance
dependencies
• Two tokens may be structurally closer to each other even
though they are far away in word sequence
• Ex: a verb and its corresponding direct object can be far away
in terms of tokens if many adjectives lie inbetween, but they are
adjacent in the parse tree
• However parsing is slow and domain dependent
• See performance comparison with LSTM on four NLP tasks at
https://nlp.stanford.edu/pubemnlp2015_2_jiwei.pdf
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10. Deep Learning Srihari
Structure of the Tree
• One option is to have a tree structure that does not depend on
the data, such as a balanced binary tree
• In some application domains, external methods can suggest the
appropriate tree structure
• Ex: when processing natural language sentences, the tree structure for
the recursive network can be fixed to the structure of the parse tree of
the sentence provided by a natural language parse
• Ideally, one would like the learner itself to discover and infer the
tree structure that is appropriate for any given input
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11. Deep Learning Srihari
Variants of Recursive Net idea
• Associate data with a tree structure and associate inputs and
targets with individual nodes of the tree
• The computation performed for each node does not have to be the
artificial neuron computation (affine transformation of all inputs followed
by a monotone nonlinearity)
• Can use a tensor operations of bilinear forms
• Previously found useful to model linear relationships between
concepts when the concepts are represented by continuous vectors
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12. Deep Learning Srihari
Recursive Neural Networks
• Recursive neural networks
are also called Tree Nets
• Useful for learning tree-like
structures
• They are highly useful for
parsing natural scenes and
language
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13. Deep Learning Srihari
Unrolling Recurrent and Tree Nets
• In RNNs, at each time step the network takes as input its
previous state s(t-1) and its current input x(t) and produces an
output y(t) and a new hidden state s(t).
• TreeNets, on the other hand, don’t have a simple linear
structure like that.
• With RNNs, you can ‘unroll’ the net and think of it as a large
feedforward net with inputs x(0), x(1), …, x(T), initial state s(0),
and outputs y(0),y(1),…,y(T), with T varying depending on the
input data stream, and the weights in each of the cells tied with
each other.
• You can also think of TreeNets by unrolling them – the weights
in each branch node are tied with each other, and the weights
in each leaf node are tied with each other.
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14. Deep Learning Srihari
Advantage of Recursive Nets
• The advantage of Recursive Nets is that they can be very
powerful in learning hierarchical, tree-like structure.
• The disadvantages are, firstly, that the tree structure of every
input sample must be known at training time.
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