40,910 research outputs found
DeepOBS: A Deep Learning Optimizer Benchmark Suite
Because the choice and tuning of the optimizer affects the speed, and
ultimately the performance of deep learning, there is significant past and
recent research in this area. Yet, perhaps surprisingly, there is no generally
agreed-upon protocol for the quantitative and reproducible evaluation of
optimization strategies for deep learning. We suggest routines and benchmarks
for stochastic optimization, with special focus on the unique aspects of deep
learning, such as stochasticity, tunability and generalization. As the primary
contribution, we present DeepOBS, a Python package of deep learning
optimization benchmarks. The package addresses key challenges in the
quantitative assessment of stochastic optimizers, and automates most steps of
benchmarking. The library includes a wide and extensible set of ready-to-use
realistic optimization problems, such as training Residual Networks for image
classification on ImageNet or character-level language prediction models, as
well as popular classics like MNIST and CIFAR-10. The package also provides
realistic baseline results for the most popular optimizers on these test
problems, ensuring a fair comparison to the competition when benchmarking new
optimizers, and without having to run costly experiments. It comes with output
back-ends that directly produce LaTeX code for inclusion in academic
publications. It supports TensorFlow and is available open source.Comment: Accepted at ICLR 2019. 9 pages, 3 figures, 2 table
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Natural language generation (NLG) is a critical component of spoken dialogue
and it has a significant impact both on usability and perceived quality. Most
NLG systems in common use employ rules and heuristics and tend to generate
rigid and stylised responses without the natural variation of human language.
They are also not easily scaled to systems covering multiple domains and
languages. This paper presents a statistical language generator based on a
semantically controlled Long Short-term Memory (LSTM) structure. The LSTM
generator can learn from unaligned data by jointly optimising sentence planning
and surface realisation using a simple cross entropy training criterion, and
language variation can be easily achieved by sampling from output candidates.
With fewer heuristics, an objective evaluation in two differing test domains
showed the proposed method improved performance compared to previous methods.
Human judges scored the LSTM system higher on informativeness and naturalness
and overall preferred it to the other systems.Comment: To be appear in EMNLP 201
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