A key pretrial hearing to determine what evidence prosecutors can use in Bill Cosby's Pennsylvania sex assault case has been scheduled for early November. Prosecutors hope to call 13 other accusers to show the comedian had a pattern of drugging and molesting women. The criminal charges involve an encounter with Andrea Constand in 2004. Prosecutors also want to use Cosby's deposition from Constand's 2005 lawsuit. Cosby acknowledges under oath that he had sexual encounters with a series of women after giving them drugs or alcohol.

Back in 1970, the social activist Irina Dunn scribbled a slogan on the back of a toilet cubicle door at the University of Sydney. It said: "A woman needs a man like a fish needs a bicycle." The phrase went viral and eventually became a famous refrain for the growing feminist movement of the time. The phrase is also an example of sarcasm. The humor comes from the fact that a fish doesn't need a bicycle.

Multilingual Text Classification (MLTC) is a text classification task in which documents are written each in one among a set L of natural languages, and in which all documents must be classified under the same classification scheme, irrespective of language. There are two main variants of MLTC, namely Cross-Lingual Text Classification (CLTC) and Polylingual Text Classification (PLTC). In PLTC, which is the focus of this paper, we assume (differently from CLTC) that for each language in L there is a representative set of training documents; PLTC consists of improving the accuracy of each of the |L| monolingual classifiers by also leveraging the training documents written in the other (|L| − 1) languages. The obvious solution, consisting of generating a single polylingual classifier from the juxtaposed monolingual vector spaces, is usually infeasible, since the dimensionality of the resulting vector space is roughly |L| times that of a monolingual one, and is thus often unmanageable. As a response, the use of machine translation tools or multilingual dictionaries has been proposed. However, these resources are not always available, or are not always free to use. One machine-translation-free and dictionary-free method that, to the best of our knowledge, has never been applied to PLTC before, is Random Indexing (RI). We analyse RI in terms of space and time efficiency, and propose a particular configuration of it (that we dub Lightweight Random Indexing LRI). By running experiments on two well known public benchmarks, Reuters RCV1/RCV2 (a comparable corpus) and JRC-Acquis (a parallel one), we show LRI to outperform (both in terms of effectiveness and efficiency) a number of previously proposed machine-translation-free and dictionary-free PLTC methods that we use as baselines.

Last time we investigated the k-nearest-neighbors algorithm and the underlying idea that one can learn a classification rule by copying the known classification of nearby data points. This required that we view our data as sitting inside a metric space; that is, we imposed a kind of geometric structure on our data. One glaring problem is that there may be no reasonable way to do this. While we mentioned scaling issues and provided a number of possible metrics in our primer, a more common problem is that the data simply isn't numeric. For instance, a poll of US citizens might ask the respondent to select which of a number of issues he cares most about. There could be 50 choices, and there is no reasonable way to assign these numerical values so that all are equidistant in the resulting metric space. Another issue is that the quality of the data could be bad. For instance, there may be missing values for some attributes (e.g., a respondent may neglect to answer one or more questions).

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Looking in fine form after two days of rest, Roger Federer equaled Jimmy Connors' Open-era record by reaching his 14th Wimbledon quarterfinal and added to his own mark by making it at least that far at a Grand Slam tournament for the 48th time. Federer, a seven-time champion at the All England Club, has not dropped a set in the tournament through four matches after beating unseeded American Steve Johnson 6-2, 6-3, 7-5 at Centre Court on Monday. Johnson was making his debut in the fourth round of a major. The No. 3-seeded Federer hadn't played since Friday, when he was the only man to finish a third-round match. He next faces No. 9 Marin Cilic, the 2014 US Open champion, who advanced when Kei Nishikori retired from their fourth-round match.

We introduce structured prediction energy networks (SPENs), a flexible framework for structured prediction. A deep architecture is used to define an energy function of candidate labels, and then predictions are produced by using back-propagation to iteratively optimize the energy with respect to the labels. This deep architecture captures dependencies between labels that would lead to intractable graphical models, and performs structure learning by automatically learning discriminative features of the structured output. One natural application of our technique is multi-label classification, which traditionally has required strict prior assumptions about the interactions between labels to ensure tractable learning and prediction. We are able to apply SPENs to multi-label problems with substantially larger label sets than previous applications of structured prediction, while modeling high-order interactions using minimal structural assumptions. Overall, deep learning provides remarkable tools for learning features of the inputs to a prediction problem, and this work extends these techniques to learning features of structured outputs. Our experiments provide impressive performance on a variety of benchmark multi-label classification tasks, demonstrate that our technique can be used to provide interpretable structure learning, and illuminate fundamental trade-offs between feed-forward and iterative structured prediction.

Machine learning algorithms are optimized to model statistical properties of the training data. If the input data reflects stereotypes and biases of the broader society, then the output of the learning algorithm also captures these stereotypes. In this paper, we initiate the study of gender stereotypes in {\em word embedding}, a popular framework to represent text data. As their use becomes increasingly common, applications can inadvertently amplify unwanted stereotypes. We show across multiple datasets that the embeddings contain significant gender stereotypes, especially with regard to professions. We created a novel gender analogy task and combined it with crowdsourcing to systematically quantify the gender bias in a given embedding. We developed an efficient algorithm that reduces gender stereotype using just a handful of training examples while preserving the useful geometric properties of the embedding. We evaluated our algorithm on several metrics. While we focus on male/female stereotypes, our framework may be applicable to other types of embedding biases.

I have a dataset of users, each user has has daily information about his activities (numerical values representing some measurements of his physical activities). In addition, each user in each day has a boolean value that represents if he/she took a particular action. The data set is not fixed, so new activities information and action are added for each user each new day. Build a model that predicts which user is likely to take the action in the near future (e.g. in any of the next 7 days). My approach is to build feature vectors representing the activity values for each users over a period of time, and use the action column as a source of ground-truth.

We study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and natural language processing. Our approach seeks to adaptively generate computationally costly features during test-time in order to reduce the computational cost of prediction while maintaining prediction performance. We show that training the adaptive feature generation system can be reduced to a series of structured learning problems, resulting in efficient training using existing structured learning algorithms. This framework provides theoretical justification for several existing heuristic approaches found in literature. We evaluate our proposed adaptive system on two structured prediction tasks, optical character recognition (OCR) and dependency parsing and show strong performance in reduction of the feature costs without degrading accuracy.