Imagine predicting the emotion of a tweet without providing any training examples of tweets with that emotion label.This research discusses the paradigm of Zero-shot learning for Text Classification and the paper is aptly titled as "Train Once, Test Anywhere: Zero-shot Learning For Text Classification". You can read the paper here or try a demo here.

Fire officials have deployed additional resources to be able to respond quickly in case blazes break out. Firefighters made quick work of a small brush fire that briefly threatened homes before dawn in Malibu.

We provide novel theoretical insights on structured prediction in the context of efficient convex surrogate loss minimization with consistency guarantees. For any task loss, we construct a convex surrogate that can be optimized via stochastic gradient descent and we prove tight bounds on the so-called "calibration function" relating the excess surrogate risk to the actual risk. In contrast to prior related work, we carefully monitor the effect of the exponential number of classes in the learning guarantees as well as on the optimization complexity. As an interesting consequence, we formalize the intuition that some task losses make learning harder than others, and that the classical 0-1 loss is ill-suited for general structured prediction.

In the past few years, machine learning (ML) has revolutionized the way we do business. A disruptive breakthrough that differentiates machine learning from other approaches to automation is a step away from the rules-based programming. ML algorithms allowed engineers to leverage data without explicitly programming machines to follow specific paths of problem-solving. Instead, machines themselves arrive at the right answers based on the data they have. This capability made business executives reconsider the ways they use data to make decisions.

We present a simple generative framework for learning to predict previously unseen classes, based on estimating class-attribute-gated class-conditional distributions. We model each class-conditional distribution as an exponential family distribution and the parameters of the distribution of each seen/unseen class are defined as functions of the respective observed class attributes. These functions can be learned using only the seen class data and can be used to predict the parameters of the class-conditional distribution of each unseen class. Unlike most existing methods for zero-shot learning that represent classes as fixed embeddings in some vector space, our generative model naturally represents each class as a probability distribution. It is simple to implement and also allows leveraging additional unlabeled data from unseen classes to improve the estimates of their class-conditional distributions using transductive/semi-supervised learning. Moreover, it extends seamlessly to few-shot learning by easily updating these distributions when provided with a small number of additional labelled examples from unseen classes. Through a comprehensive set of experiments on several benchmark data sets, we demonstrate the efficacy of our framework.

We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing $\kappa$ predominant classes using the labeled training examples such that the proportion of their non-predominant classes is below a fixed threshold. In the second step, a classifier is trained by minimizing a margin empirical loss over the labeled training set and a penalization term measuring the disability of the learner to predict the $\kappa$ predominant classes of the identified clusters. The resulting data-dependent generalization error bound involves the margin distribution of the classifier, the stability of the clustering technique used in the first step and Rademacher complexity terms corresponding to partially labeled training data. Our theoretical result exhibit convergence rates extending those proposed in the literature for the binary case, and experimental results on different multiclass classification problems show empirical evidence that supports the theory.

Categorically, artificial intelligence (AI) can appear be an odd juxtaposition of order and disorder -- we direct the AI with algorithms, yet the system produces new insights seemingly magically. Most of the well-known applications of machine learning and computational AI involve supervised learning. The modeler amasses a vast set of existing data (e.g., financial transactions, internet photographs, or the texts of tweets) and a base-level "ground truth" outcome that is already known, perhaps in retrospect or by expensive human investigation. Equipped with any number of computational algorithms, the scientist becomes the "supervisor" whose code trains the model to reproduce, in the lab, the known outcomes with a low probability of error. The models are then deployed to live a happy life scoring credit risk and fraud likelihood, finding pictures of Chihuahuas and muffins, or flagging insulting tweets.

Label Maker is a python library to help in extracting insight from satellite imagery. Label Maker creates machine-learning-ready training data for most popular ML frameworks, including Keras, Tensor Flow, and MXNet. It pulls data from OpenStreetMap and combines that with imagery sources like Mapbox or Digital Globe to create a single file for use in training machine learning algorithms. Supervised learning methods require two things: satellite imagery and ground-truth labels. If you're looking to train a model in Potsdam or a few other select cities, there are good datasets already available.

A winter storm warning expired in the Lake Tahoe region Friday afternoon. The weather service said 11 inches (28 centimeters) of snow was recorded Thursday night and early Friday at the Northstar ski resort near Truckee, California and about 10 inches (25 centimeters) at Mt. Rose southwest of Reno. Up to 7 inches (18 centimeters) was reported at Heavenly in South Lake Tahoe, California.

In the past few years, machine learning (ML) has revolutionized the way we do business. A disruptive breakthrough that differentiates machine learning from other approaches to automation is a step away from the rules-based programming. ML algorithms allowed engineers to leverage data without explicitly programming machines to follow specific paths of problem-solving. Instead, machines themselves arrive at the right answers based on the data they have. This capability made business executives reconsider the ways they use data to make decisions. In layman terms, machine learning is applied to make forecasts on incoming data using historic data as a training example.