Search Results for author:
Found 10 papers, 2 papers with code
From Large to Tiny: Distilling and Refining Mathematical Expertise for Math Word Problems with Weakly Supervision
In light of these challenges, we introduce an innovative two-stage framework that adeptly transfers mathematical Expertise from large to tiny language models.
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
The key lies in the generalizability of (i) the encoding method to model the question and the database schema and (ii) the question-schema linking method to learn the mapping between words in the question and tables/columns in the database schema.
Ranked #4 on
Text-To-SQL
on spider
(Exact Match Accuracy (Test) metric)
Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution
For image processing applications, the use of graph structures and GCNs have not been fully explored.
Semi-Supervised Disentangled Framework for Transferable Named Entity Recognition
In the proposed framework, the domain-specific information is integrated with the domain-specific latent variables by using a domain predictor.
TAG : Type Auxiliary Guiding for Code Comment Generation
Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e. g., operator, string, etc.
Disentanglement Challenge: From Regularization to Reconstruction
The challenge of learning disentangled representation has recently attracted much attention and boils down to a competition using a new real world disentanglement dataset (Gondal et al., 2019).
Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems
However, the mainstream domain adaptation methods cannot achieve ideal performance on time series data, because most of them focus on static samples and even the existing time series domain adaptation methods ignore the properties of time series data, such as temporal causal mechanism.
Data Driven Chiller Plant Energy Optimization with Domain Knowledge
Refrigeration and chiller optimization is an important and well studied topic in mechanical engineering, mostly taking advantage of physical models, designed on top of over-simplified assumptions, over the equipments.
Twisty Takens: A Geometric Characterization of Good Observations on Dense Trajectories
In nonlinear time series analysis and dynamical systems theory, Takens' embedding theorem states that the sliding window embedding of a generic observation along trajectories in a state space, recovers the region traversed by the dynamics.
Dynamical Systems Computational Geometry Algebraic Topology 37M10, 37M05, 37N99 I.3.5; G.1.m
An Encoder-Decoder Framework Translating Natural Language to Database Queries
These techniques help the neural network better focus on understanding semantics of operations in natural language and save the efforts on SQL grammar learning.
