25 research outputs found
A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network
In this work, we address the problem to model all the nodes (words or
phrases) in a dependency tree with the dense representations. We propose a
recursive convolutional neural network (RCNN) architecture to capture syntactic
and compositional-semantic representations of phrases and words in a dependency
tree. Different with the original recursive neural network, we introduce the
convolution and pooling layers, which can model a variety of compositions by
the feature maps and choose the most informative compositions by the pooling
layers. Based on RCNN, we use a discriminative model to re-rank a -best list
of candidate dependency parsing trees. The experiments show that RCNN is very
effective to improve the state-of-the-art dependency parsing on both English
and Chinese datasets
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates
The privacy-sensitive nature of decentralized datasets and the robustness of
eXtreme Gradient Boosting (XGBoost) on tabular data raise the needs to train
XGBoost in the context of federated learning (FL). Existing works on federated
XGBoost in the horizontal setting rely on the sharing of gradients, which
induce per-node level communication frequency and serious privacy concerns. To
alleviate these problems, we develop an innovative framework for horizontal
federated XGBoost which does not depend on the sharing of gradients and
simultaneously boosts privacy and communication efficiency by making the
learning rates of the aggregated tree ensembles learnable. We conduct extensive
evaluations on various classification and regression datasets, showing our
approach achieves performance comparable to the state-of-the-art method and
effectively improves communication efficiency by lowering both communication
rounds and communication overhead by factors ranging from 25x to 700x.Comment: Accepted at the 3rd ACM Workshop on Machine Learning and Systems
(EuroMLSys), May 8th 2023, Rome, Ital