instruction:
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
question:
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy).   Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pretrained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis.   We also present an analysis showing that exposing the deep internals of the pretrained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

answer:
We introduce a new type of deep contextualized word representation that significantly improves the state of the art for a range of challenging NLP tasks.


question:
Neural networks with low-precision weights and activations offer compelling
 efficiency advantages over their full-precision equivalents. The two most
 frequently discussed benefits of quantization are reduced memory consumption,
 and a faster forward pass when implemented with efficient bitwise
 operations. We propose a third benefit of very low-precision neural networks:
 improved robustness against some adversarial attacks, and in the worst case,
 performance that is on par with full-precision models. We focus on the very
 low-precision case where weights and activations are both quantized to $\pm$1,
 and note that stochastically quantizing weights in just one layer can sharply
 reduce the impact of iterative attacks. We observe that non-scaled binary neural
 networks exhibit a similar effect to the original \emph{defensive distillation}
 procedure that led to \emph{gradient masking}, and a false notion of security.
 We address this by conducting both black-box and white-box experiments with
 binary models that do not artificially mask gradients.
answer:
We conduct adversarial attacks against binarized neural networks and show that we reduce the impact of the strongest attacks, while maintaining comparable accuracy in a black-box setting


question:
Normalizing constant (also called partition function, Bayesian evidence, or marginal likelihood) is one of the central goals of Bayesian inference, yet most of the existing methods are both expensive and inaccurate. Here we develop a new approach, starting from posterior samples obtained with a standard Markov Chain Monte Carlo (MCMC). We apply a novel Normalizing Flow (NF) approach to obtain an analytic density estimator from these samples, followed by Optimal Bridge Sampling (OBS) to obtain the normalizing constant. We compare our method which we call Gaussianized Bridge Sampling (GBS) to existing methods such as Nested Sampling (NS) and Annealed Importance Sampling (AIS) on several examples, showing our method is both significantly faster and substantially more accurate than these methods, and comes with a reliable error estimation.
answer:

We develop a new method for normalization constant (Bayesian evidence) estimation using Optimal Bridge Sampling and a novel Normalizing Flow, which is shown to outperform existing methods in terms of accuracy and computational time.