In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
--------
Question: Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in capturing word similarities as well as semantic and syntactic regularities. Instead, we aim at reviving interest in a model based on counts. We present a systematic study of the use of the Hellinger distance to extract semantic representations from the word co-occurrence statistics of large text corpora. We show that this distance gives good performance on word similarity and analogy tasks, with a proper type and size of context, and a dimensionality reduction based on a stochastic low-rank approximation. Besides being both simple and intuitive, this method also provides an encoding function which can be used to infer unseen words or phrases. This becomes a clear advantage compared to predictive models which must train these new words.

Answer: Rehabilitation of Count-based Models for Word Vector Representations


Question: Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier methods for generating adversarial examples. In this short note, we report on recent results showing that simple feature squeezing techniques also make deep learning models significantly more robust against the Carlini/Wagner attacks, which are the best known adversarial methods discovered to date.

Answer: Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples


Question: In this paper we deal with a new approach to probabilistic reasoning in a logical frame­ work. Nearly almost all logics of probabil­ ity that have been proposed in the litera­ ture are based on classical two-valued logic. After making clear the differences between fuzzy logic and probability theory, here we propose a fuzzy logic of probability for which completeness results (in a probabilistic sense) are provided. The main idea behind this approach is that probability values of crisp propositions can be understood as truth­ values of some suitable fuzzy propositions as­ sociated to the crisp ones. Moreover, sug­ gestiotlS and examples of how to extend the formalism to cope with conditional probabil­ ities and with other uncertainty formalisms are also provided.

Answer:
Fuzzy logic and probability