We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a variety of ranking based loss functions, such as pointwise, pairwise, and listwise ranking. We apply our framework on two tasks: Sentence Ordering and Order Discrimination. Our framework outperforms various state-of-the-art methods on these tasks on a variety of evaluation metrics. We also show that it achieves better results when using pairwise and listwise ranking losses, rather than the pointwise ranking loss, which suggests that incorporating relative positions of two or more sentences in the loss function contributes to better learning.

We present a label embedding based approach to large-scale multi-label learning, drawing inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings. Besides leading to a highly scalable model for multi-label learning, our approach highlights interesting connections between label embedding methods commonly used for multi-label learning and paragraph embedding methods commonly used for learning representations of text data. The framework easily extends to incorporating auxiliary information such as label-label correlations; this is crucial especially when many training instances are only partially annotated. To facilitate end-to-end learning, we develop a joint learning algorithm that can learn the embeddings as well as a regression model that predicts these embeddings for the new input to be annotated, via efficient gradient based methods. We demonstrate the effectiveness of our approach through an extensive set of experiments on a variety of benchmark datasets, and show that the proposed models perform favorably as compared to state-of-the-art methods for large-scale multi-label learning.

We present a feature vector formation technique for documents -Sparse Composite Document Vector (SCDV)which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation.In SCDV, word embeddings are clustered to capture multiple semantic contexts in which words occur.They are then chained together to form document topic-vectors that can express complex, multi-topic documents.Through extensive experiments on multi-class and multi-label classification tasks, we outperform the previous state-of-the-art method, NTSG (Liu et al., 2015a).We also show that SCDV embeddings perform well on heterogeneous tasks like Topic Coherence, context-sensitive Learning and Information Retrieval.Moreover, we achieve significant reduction in training and prediction times compared to other representation methods.SCDV achieves best of both worlds -better performance with lower time and space complexity.

Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also develop a new two-level ensemble approach utilising (with respect to the taxonomy tree) path-wise, node-wise and depth-wise classifiers to reduce error in the final product classification task. Our experiments show the effectiveness of the distributional representation and the ensemble approach on data sets from a leading e-commerce platform and achieve improved results on various evaluation metrics compared to earlier approaches.

As the size of digitized painting collections increase, it becomes more difficult to organize and retrieve paintings from these collections. To manage search and other similar operations efficiently, it becomes necessary to organize the painting databases into classes and sub-classes. Manual tagging of these ever-increasing databases would become very costly and time consuming. The above challenging problem has motivated researchers to work in the area of painting analysis, genre and style classification, artist classification and automatic annotation of paintings with these tags. These problems are quite difficult as first the expected human performance for this task for non-expert but reasonably knowledgeable individuals is believed to be well below 100% percent. And second, there is a very big databases of paintings with relatively few painters painting in a single genre and style and many who paint in multiple genres and styles. In this paper, we explore the problem of feature extraction on the paintings and focus on classification of paintings into their genres and styles. We worked with 6 genres and 10 styles. We get an accuracy of 84.56% for genre classification. We achieved an accuracy of 62.37% for classifying the paintings into 10 styles. We include a comparison to existing feature extraction and classification methods as well as an analysis of our own approach across different feature vectors.

In this paper, we study the generalization properties of online learning based stochastic methods for supervised learning problems where the loss function is dependent on more than one training sample (e.g., metric learning, ranking). We present a generic decoupling technique that enables us to provide Rademacher complexity-based generalization error bounds. Our bounds are in general tighter than those obtained by Wang et al. (COLT 2012) for the same problem. Using our decoupling technique, we are further able to obtain fast convergence rates for strongly con-vex pairwise loss functions. We are also able to analyze a class of memory efficient on-line learning algorithms for pairwise learning problems that use only a bounded subset of past training samples to update the hypothesis at each step. Finally, in order to complement our generalization bounds, we propose a novel memory efficient online learning algorithm for higher order learning problems with bounded regret guarantees.

In this paper we describe a method for the synthesis of robotic controllers using evolutionary techniques. A modified version of the recurrent neural network used for controlling the robots is evolved using genetic algorithm using the variable length genotype approach where the genotype encodes the network. It has been discovered that the separation of modalities in the network like vision and touch, for the first few layers helps in faster evolution as well as in the development of faster controller networks. The structure of the network that emerges from this kind of evolution is similar to brain-like networks. Performance of plastic versus non-plastic individuals has also been explored. Gene-blocking technique has been used for developing several behaviors in the same evolution cycle. The final controller developed at the end of the evolutionary process was tested on a Khepera.