Many machine learning problems, especially multi-modal learning problems, have two sets of distinct features (e.g., image and text features in news story classification, or neuroimaging data and neurocognitive data in cognitive science research). This paper addresses the joint dimensionality reduction of two feature vectors in supervised learning problems. In particular, we assume a discriminative model where low-dimensional linear embeddings of the two feature vectors are sufficient statistics for predicting a dependent variable. We show that a simple algorithm involving singular value decomposition can accurately estimate the embeddings provided that certain sample complexities are satisfied, without specifying the nonlinear link function (regressor or classifier). The main results establish sample complexities under multiple settings. Sample complexities for different link functions only differ by constant factors.

A key barrier to making phonetic studies scalable and replicable is the need to rely on subjective, manual annotation. To help meet this challenge, a machine learning algorithm was developed for automatic measurement of a widely used phonetic measure: vowel duration. Manually-annotated data were used to train a model that takes as input an arbitrary length segment of the acoustic signal containing a single vowel that is preceded and followed by consonants and outputs the duration of the vowel. The model is based on the structured prediction framework. The input signal and a hypothesized set of a vowel's onset and offset are mapped to an abstract vector space by a set of acoustic feature functions. The learning algorithm is trained in this space to minimize the difference in expectations between predicted and manually-measured vowel durations. The trained model can then automatically estimate vowel durations without phonetic or orthographic transcription. Results comparing the model to three sets of manually annotated data suggest it out-performed the current gold standard for duration measurement, an HMM-based forced aligner (which requires orthographic or phonetic transcription as an input).

A former solicitor general has said she is concerned the Ched Evans rape case could discourage victims of sexual offences from coming forward. The 27-year-old footballer was cleared on Friday of raping a 19-year-old woman in a hotel room. Vera Baird told the BBC that details of the woman's sexual past should not have been heard in court. Mr Evans was found guilty of rape in 2012, but that conviction was quashed in April. The Chesterfield striker was accused of attacking the woman at a Premier Inn in Rhuddlan, Denbighshire, on 30 May 2011.

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.

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.