35,706 research outputs found
Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
Graph representation learning based on graph neural networks (GNNs) can
greatly improve the performance of downstream tasks, such as node and graph
classification. However, the general GNN models do not aggregate node
information in a hierarchical manner, and can miss key higher-order structural
features of many graphs. The hierarchical aggregation also enables the graph
representations to be explainable. In addition, supervised graph representation
learning requires labeled data, which is expensive and error-prone. To address
these issues, we present an unsupervised graph representation learning method,
Unsupervised Hierarchical Graph Representation (UHGR), which can generate
hierarchical representations of graphs. Our method focuses on maximizing mutual
information between "local" and high-level "global" representations, which
enables us to learn the node embeddings and graph embeddings without any
labeled data. To demonstrate the effectiveness of the proposed method, we
perform the node and graph classification using the learned node and graph
embeddings. The results show that the proposed method achieves comparable
results to state-of-the-art supervised methods on several benchmarks. In
addition, our visualization of hierarchical representations indicates that our
method can capture meaningful and interpretable clusters.Comment: 7 pages, 2 figures, 4 table
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations
Deep learning is increasingly used in decision-making tasks. However,
understanding how neural networks produce final predictions remains a
fundamental challenge. Existing work on interpreting neural network predictions
for images often focuses on explaining predictions for single images or
neurons. As predictions are often computed from millions of weights that are
optimized over millions of images, such explanations can easily miss a bigger
picture. We present Summit, an interactive system that scalably and
systematically summarizes and visualizes what features a deep learning model
has learned and how those features interact to make predictions. Summit
introduces two new scalable summarization techniques: (1) activation
aggregation discovers important neurons, and (2) neuron-influence aggregation
identifies relationships among such neurons. Summit combines these techniques
to create the novel attribution graph that reveals and summarizes crucial
neuron associations and substructures that contribute to a model's outcomes.
Summit scales to large data, such as the ImageNet dataset with 1.2M images, and
leverages neural network feature visualization and dataset examples to help
users distill large, complex neural network models into compact, interactive
visualizations. We present neural network exploration scenarios where Summit
helps us discover multiple surprising insights into a prevalent, large-scale
image classifier's learned representations and informs future neural network
architecture design. The Summit visualization runs in modern web browsers and
is open-sourced.Comment: Published in IEEE Transactions on Visualization and Computer Graphics
2020, and presented at IEEE VAST 201
Visual Analytics and Human Involvement in Machine Learning
The rapidly developing AI systems and applications still require human
involvement in practically all parts of the analytics process. Human decisions
are largely based on visualizations, providing data scientists details of data
properties and the results of analytical procedures. Different visualizations
are used in the different steps of the Machine Learning (ML) process. The
decision which visualization to use depends on factors, such as the data
domain, the data model and the step in the ML process. In this chapter, we
describe the seven steps in the ML process and review different visualization
techniques that are relevant for the different steps for different types of
data, models and purposes
Visualizing Dynamics: from t-SNE to SEMI-MDPs
Deep Reinforcement Learning (DRL) is a trending field of research, showing
great promise in many challenging problems such as playing Atari, solving Go
and controlling robots. While DRL agents perform well in practice we are still
missing the tools to analayze their performance and visualize the temporal
abstractions that they learn. In this paper, we present a novel method that
automatically discovers an internal Semi Markov Decision Process (SMDP) model
in the Deep Q Network's (DQN) learned representation. We suggest a novel
visualization method that represents the SMDP model by a directed graph and
visualize it above a t-SNE map. We show how can we interpret the agent's policy
and give evidence for the hierarchical state aggregation that DQNs are learning
automatically. Our algorithm is fully automatic, does not require any domain
specific knowledge and is evaluated by a novel likelihood based evaluation
criteria.Comment: Presented at 2016 ICML Workshop on Human Interpretability in Machine
Learning (WHI 2016), New York, N
Towards Better Analysis of Deep Convolutional Neural Networks
Deep convolutional neural networks (CNNs) have achieved breakthrough
performance in many pattern recognition tasks such as image classification.
However, the development of high-quality deep models typically relies on a
substantial amount of trial-and-error, as there is still no clear understanding
of when and why a deep model works. In this paper, we present a visual
analytics approach for better understanding, diagnosing, and refining deep
CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this
formulation, a hybrid visualization is developed to disclose the multiple
facets of each neuron and the interactions between them. In particular, we
introduce a hierarchical rectangle packing algorithm and a matrix reordering
algorithm to show the derived features of a neuron cluster. We also propose a
biclustering-based edge bundling method to reduce visual clutter caused by a
large number of connections between neurons. We evaluated our method on a set
of CNNs and the results are generally favorable.Comment: Submitted to VIS 201
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
Modern deep transfer learning approaches have mainly focused on learning
generic feature vectors from one task that are transferable to other tasks,
such as word embeddings in language and pretrained convolutional features in
vision. However, these approaches usually transfer unary features and largely
ignore more structured graphical representations. This work explores the
possibility of learning generic latent relational graphs that capture
dependencies between pairs of data units (e.g., words or pixels) from
large-scale unlabeled data and transferring the graphs to downstream tasks. Our
proposed transfer learning framework improves performance on various tasks
including question answering, natural language inference, sentiment analysis,
and image classification. We also show that the learned graphs are generic
enough to be transferred to different embeddings on which the graphs have not
been trained (including GloVe embeddings, ELMo embeddings, and task-specific
RNN hidden unit), or embedding-free units such as image pixels
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
Learning on point cloud is eagerly in demand because the point cloud is a
common type of geometric data and can aid robots to understand environments
robustly. However, the point cloud is sparse, unstructured, and unordered,
which cannot be recognized accurately by a traditional convolutional neural
network (CNN) nor a recurrent neural network (RNN). Fortunately, a graph
convolutional neural network (Graph CNN) can process sparse and unordered data.
Hence, we propose a linked dynamic graph CNN (LDGCNN) to classify and segment
point cloud directly in this paper. We remove the transformation network, link
hierarchical features from dynamic graphs, freeze feature extractor, and
retrain the classifier to increase the performance of LDGCNN. We explain our
network using theoretical analysis and visualization. Through experiments, we
show that the proposed LDGCNN achieves state-of-art performance on two standard
datasets: ModelNet40 and ShapeNet
Review on Graph Feature Learning and Feature Extraction Techniques for Link Prediction
The problem of link prediction has recently attracted considerable attention
by research community. Given a graph, which is an abstraction of the
relationships among entities, the task of link prediction is to anticipate
future connections among entities in the graph, concerning its current state.
Extensive studies have examined this problem from different aspects and
proposed various methods, some of which might work very well for a specific
application but not as a global solution. This work presents an extensive
review of state-of-art methods and algorithms proposed on this subject and
categorizes them into four main categories: similarity-based methods,
probabilistic methods, relational models, and learning-based methods.
Additionally, a collection of network data sets has been presented in this
paper, which can be used to study link prediction. To the best of our
knowledge, this survey is the first comprehensive study that considers all of
the mentioned challenges and solutions for link prediction in graphs with the
improvements in the recent years, including the unsupervised and supervised
techniques and their evolution over the recent years.Comment: 31 pages, 7 figure
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem
The celebrated Seq2Seq technique and its numerous variants achieve excellent
performance on many tasks such as neural machine translation, semantic parsing,
and math word problem solving. However, these models either only consider input
objects as sequences while ignoring the important structural information for
encoding, or they simply treat output objects as sequence outputs instead of
structural objects for decoding. In this paper, we present a novel
Graph-to-Tree Neural Networks, namely Graph2Tree consisting of a graph encoder
and a hierarchical tree decoder, that encodes an augmented graph-structured
input and decodes a tree-structured output. In particular, we investigated our
model for solving two problems, neural semantic parsing and math word problem.
Our extensive experiments demonstrate that our Graph2Tree model outperforms or
matches the performance of other state-of-the-art models on these tasks.Comment: Long Paper in EMNLP 2020. 12 pages including reference
Learning Actor Relation Graphs for Group Activity Recognition
Modeling relation between actors is important for recognizing group activity
in a multi-person scene. This paper aims at learning discriminative relation
between actors efficiently using deep models. To this end, we propose to build
a flexible and efficient Actor Relation Graph (ARG) to simultaneously capture
the appearance and position relation between actors. Thanks to the Graph
Convolutional Network, the connections in ARG could be automatically learned
from group activity videos in an end-to-end manner, and the inference on ARG
could be efficiently performed with standard matrix operations. Furthermore, in
practice, we come up with two variants to sparsify ARG for more effective
modeling in videos: spatially localized ARG and temporal randomized ARG. We
perform extensive experiments on two standard group activity recognition
datasets: the Volleyball dataset and the Collective Activity dataset, where
state-of-the-art performance is achieved on both datasets. We also visualize
the learned actor graphs and relation features, which demonstrate that the
proposed ARG is able to capture the discriminative relation information for
group activity recognition.Comment: Accepted by CVPR 201
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