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Found 8 papers, 2 papers with code
LayoutPrompter: Awaken the Design Ability of Large Language Models
In this work, we propose LayoutPrompter, which leverages large language models (LLMs) to address the above problems through in-context learning.
A Parse-Then-Place Approach for Generating Graphic Layouts from Textual Descriptions
To model combined and incomplete constraints, we use a Transformer-based layout generation model and carefully design a way to represent constraints and layouts as sequences.
LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models
To tackle the challenge, we summarize three critical factors for achieving a mild forward process for the layout, i. e., legality, coordinate proximity and type disruption.
LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction
First, to flexibly handle diverse constraints, we propose a constraint serialization scheme, which represents different user constraints as sequences of tokens with a predefined format.
Data-Anonymous Encoding for Text-to-SQL Generation
On text-to-SQL generation, the input utterance usually contains lots of tokens that are related to column names or cells in the table, called \textit{table-related tokens}.
Slim-DP: A Light Communication Data Parallelism for DNN
However, with the increasing size of DNN models and the large number of workers in practice, this typical data parallelism cannot achieve satisfactory training acceleration, since it usually suffers from the heavy communication cost due to transferring huge amount of information between workers and the parameter server.
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks
In this framework, we propose to aggregate the local models by ensemble, i. e., averaging the outputs of local models instead of the parameters.
On the Depth of Deep Neural Networks: A Theoretical View
First, we derive an upper bound for RA of DNN, and show that it increases with increasing depth.
