26,786 research outputs found
Classical Structured Prediction Losses for Sequence to Sequence Learning
There has been much recent work on training neural attention models at the
sequence-level using either reinforcement learning-style methods or by
optimizing the beam. In this paper, we survey a range of classical objective
functions that have been widely used to train linear models for structured
prediction and apply them to neural sequence to sequence models. Our
experiments show that these losses can perform surprisingly well by slightly
outperforming beam search optimization in a like for like setup. We also report
new state of the art results on both IWSLT'14 German-English translation as
well as Gigaword abstractive summarization. On the larger WMT'14 English-French
translation task, sequence-level training achieves 41.5 BLEU which is on par
with the state of the art.Comment: 10 pages, NAACL 201
Estimating Uncertainty Online Against an Adversary
Assessing uncertainty is an important step towards ensuring the safety and
reliability of machine learning systems. Existing uncertainty estimation
techniques may fail when their modeling assumptions are not met, e.g. when the
data distribution differs from the one seen at training time. Here, we propose
techniques that assess a classification algorithm's uncertainty via calibrated
probabilities (i.e. probabilities that match empirical outcome frequencies in
the long run) and which are guaranteed to be reliable (i.e. accurate and
calibrated) on out-of-distribution input, including input generated by an
adversary. This represents an extension of classical online learning that
handles uncertainty in addition to guaranteeing accuracy under adversarial
assumptions. We establish formal guarantees for our methods, and we validate
them on two real-world problems: question answering and medical diagnosis from
genomic data
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
We present a method that learns to integrate temporal information, from a
learned dynamics model, with ambiguous visual information, from a learned
vision model, in the context of interacting agents. Our method is based on a
graph-structured variational recurrent neural network (Graph-VRNN), which is
trained end-to-end to infer the current state of the (partially observed)
world, as well as to forecast future states. We show that our method
outperforms various baselines on two sports datasets, one based on real
basketball trajectories, and one generated by a soccer game engine.Comment: ICLR 2019 camera read
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