7,599 research outputs found
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Metric learning has the aim to improve classification accuracy by learning a
distance measure which brings data points from the same class closer together
and pushes data points from different classes further apart. Recent research
has demonstrated that metric learning approaches can also be applied to trees,
such as molecular structures, abstract syntax trees of computer programs, or
syntax trees of natural language, by learning the cost function of an edit
distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree.
However, learning such costs directly may yield an edit distance which violates
metric axioms, is challenging to interpret, and may not generalize well. In
this contribution, we propose a novel metric learning approach for trees which
we call embedding edit distance learning (BEDL) and which learns an edit
distance indirectly by embedding the tree nodes as vectors, such that the
Euclidean distance between those vectors supports class discrimination. We
learn such embeddings by reducing the distance to prototypical trees from the
same class and increasing the distance to prototypical trees from different
classes. In our experiments, we show that BEDL improves upon the
state-of-the-art in metric learning for trees on six benchmark data sets,
ranging from computer science over biomedical data to a natural-language
processing data set containing over 300,000 nodes.Comment: Paper at the International Conference of Machine Learning (2018),
2018-07-10 to 2018-07-15 in Stockholm, Swede
Online Embedding Compression for Text Classification using Low Rank Matrix Factorization
Deep learning models have become state of the art for natural language
processing (NLP) tasks, however deploying these models in production system
poses significant memory constraints. Existing compression methods are either
lossy or introduce significant latency. We propose a compression method that
leverages low rank matrix factorization during training,to compress the word
embedding layer which represents the size bottleneck for most NLP models. Our
models are trained, compressed and then further re-trained on the downstream
task to recover accuracy while maintaining the reduced size. Empirically, we
show that the proposed method can achieve 90% compression with minimal impact
in accuracy for sentence classification tasks, and outperforms alternative
methods like fixed-point quantization or offline word embedding compression. We
also analyze the inference time and storage space for our method through FLOP
calculations, showing that we can compress DNN models by a configurable ratio
and regain accuracy loss without introducing additional latency compared to
fixed point quantization. Finally, we introduce a novel learning rate schedule,
the Cyclically Annealed Learning Rate (CALR), which we empirically demonstrate
to outperform other popular adaptive learning rate algorithms on a sentence
classification benchmark.Comment: Accepted in Thirty-Third AAAI Conference on Artificial Intelligence
(AAAI 2019
Robust and Communication-Efficient Collaborative Learning
We consider a decentralized learning problem, where a set of computing nodes
aim at solving a non-convex optimization problem collaboratively. It is
well-known that decentralized optimization schemes face two major system
bottlenecks: stragglers' delay and communication overhead. In this paper, we
tackle these bottlenecks by proposing a novel decentralized and gradient-based
optimization algorithm named as QuanTimed-DSGD. Our algorithm stands on two
main ideas: (i) we impose a deadline on the local gradient computations of each
node at each iteration of the algorithm, and (ii) the nodes exchange quantized
versions of their local models. The first idea robustifies to straggling nodes
and the second alleviates communication efficiency. The key technical
contribution of our work is to prove that with non-vanishing noises for
quantization and stochastic gradients, the proposed method exactly converges to
the global optimal for convex loss functions, and finds a first-order
stationary point in non-convex scenarios. Our numerical evaluations of the
QuanTimed-DSGD on training benchmark datasets, MNIST and CIFAR-10, demonstrate
speedups of up to 3x in run-time, compared to state-of-the-art decentralized
optimization methods
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