22 research outputs found
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation
The ability to perform effective planning is crucial for building an
instruction-following agent. When navigating through a new environment, an
agent is challenged with (1) connecting the natural language instructions with
its progressively growing knowledge of the world; and (2) performing long-range
planning and decision making in the form of effective exploration and error
correction. Current methods are still limited on both fronts despite extensive
efforts. In this paper, we introduce the Evolving Graphical Planner (EGP), a
model that performs global planning for navigation based on raw sensory input.
The model dynamically constructs a graphical representation, generalizes the
action space to allow for more flexible decision making, and performs efficient
planning on a proxy graph representation. We evaluate our model on a
challenging Vision-and-Language Navigation (VLN) task with photorealistic
images and achieve superior performance compared to previous navigation
architectures. For instance, we achieve a 53% success rate on the test split of
the Room-to-Room navigation task through pure imitation learning, outperforming
previous navigation architectures by up to 5%
Graph matching by graph neural network
Graph matching or network alignment refers to the problem of matching two correlated graphs. This thesis presents a deep Q learning based method, which represents the matching process by a graph neural network. By breaking the symmetry, the parameterized graph neural network is able to capture a wide range of neighborhoods. Extensive experiments on various training and testing data have shown better performance, strong scalability and the ability to adapt to different domains
G-CREWE: Graph CompREssion With Embedding for Network Alignment
Network alignment is useful for multiple applications that require
increasingly large graphs to be processed. Existing research approaches this as
an optimization problem or computes the similarity based on node
representations. However, the process of aligning every pair of nodes between
relatively large networks is time-consuming and resource-intensive. In this
paper, we propose a framework, called G-CREWE (Graph CompREssion With
Embedding) to solve the network alignment problem. G-CREWE uses node embeddings
to align the networks on two levels of resolution, a fine resolution given by
the original network and a coarse resolution given by a compressed version, to
achieve an efficient and effective network alignment. The framework first
extracts node features and learns the node embedding via a Graph Convolutional
Network (GCN). Then, node embedding helps to guide the process of graph
compression and finally improve the alignment performance. As part of G-CREWE,
we also propose a new compression mechanism called MERGE (Minimum dEgRee
neiGhbors comprEssion) to reduce the size of the input networks while
preserving the consistency in their topological structure. Experiments on all
real networks show that our method is more than twice as fast as the most
competitive existing methods while maintaining high accuracy.Comment: 10 pages, accepted at the 29th ACM International Conference
onInformation and Knowledge Management (CIKM 20