Learning Dynamic Feature Selection for Fast Sequential Prediction

We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart of many NLP components. This is accomplished by partitioning the features into a sequence of templates which are ordered such that high confidence can often be reached using only a small fraction of all features. Parameter estimation is arranged to maximize accuracy and early confidence in this sequence. Our approach is simpler and better suited to NLP than other related cascade methods. We present experiments in left-to-right part-of-speech tagging, named entity recognition, and transition-based dependency parsing. On the typical benchmarking datasets we can preserve POS tagging accuracy above 97% and parsing LAS above 88.5% both with over a five-fold reduction in run-time, and NER F1 above 88 with more than 2x increase in speed.Comment: Appears in The 53rd Annual Meeting of the Association for Computational Linguistics, Beijing, China, July 201

Gibbs Max-margin Topic Models with Data Augmentation

Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover discriminative latent semantic structures and make accurate predictions for unseen testing data. However, the resulting learning problems are usually hard to solve because of the non-smoothness of the margin loss. Existing approaches to building max-margin supervised topic models rely on an iterative procedure to solve multiple latent SVM subproblems with additional mean-field assumptions on the desired posterior distributions. This paper presents an alternative approach by defining a new max-margin loss. Namely, we present Gibbs max-margin supervised topic models, a latent variable Gibbs classifier to discover hidden topic representations for various tasks, including classification, regression and multi-task learning. Gibbs max-margin supervised topic models minimize an expected margin loss, which is an upper bound of the existing margin loss derived from an expected prediction rule. By introducing augmented variables and integrating out the Dirichlet variables analytically by conjugacy, we develop simple Gibbs sampling algorithms with no restricting assumptions and no need to solve SVM subproblems. Furthermore, each step of the "augment-and-collapse" Gibbs sampling algorithms has an analytical conditional distribution, from which samples can be easily drawn. Experimental results demonstrate significant improvements on time efficiency. The classification performance is also significantly improved over competitors on binary, multi-class and multi-label classification tasks.Comment: 35 page

Fast Label Embeddings via Randomized Linear Algebra

Many modern multiclass and multilabel problems are characterized by increasingly large output spaces. For these problems, label embeddings have been shown to be a useful primitive that can improve computational and statistical efficiency. In this work we utilize a correspondence between rank constrained estimation and low dimensional label embeddings that uncovers a fast label embedding algorithm which works in both the multiclass and multilabel settings. The result is a randomized algorithm whose running time is exponentially faster than naive algorithms. We demonstrate our techniques on two large-scale public datasets, from the Large Scale Hierarchical Text Challenge and the Open Directory Project, where we obtain state of the art results.Comment: To appear in the proceedings of the ECML/PKDD 2015 conference. Reference implementation available at https://github.com/pmineiro/randembe

Distribution matching for transduction

Many transductive inference algorithms assume that distributions over training and test estimates should be related, e.g. by providing a large margin of separation on both sets. We use this idea to design a transduction algorithm which can be used without modification for classification, regression, and structured estimation. At its heart we exploit the fact that for a good learner the distributions over the outputs on training and test sets should match. This is a classical two-sample problem which can be solved efficiently in its most general form by using distance measures in Hilbert Space. It turns out that a number of existing heuristics can be viewed as special cases of our approach.

Deep Structured Models for Large Scale Object Co-detection and Segmentation

Structured decisions are often required for a large variety of image and scene understanding tasks in computer vision, with few of them being object detection, localization, semantic segmentation and many more. Structured prediction deals with learning inherent structure by incorporating contextual information from several images and multiple tasks. However, it is very challenging when dealing with large scale image datasets where performance is limited by high computational costs and expressive power of the underlying representation learning techniques. In this thesis, we present efficient and effective deep structured models for context-aware object detection, co-localization and instance-level semantic segmentation. First, we introduce a principled formulation for object co-detection using a fully-connected conditional random field (CRF). We build an explicit graph whose vertices represent object candidates (instead of pixel values) and edges encode the object similarity via simple, yet effective pairwise potentials. More specifically, we design a weighted mixture of Gaussian kernels for class-specific object similarity, and formulate kernel weights estimation as a least-squares regression problem. Its solution can therefore be obtained in closed-form. Furthermore, in contrast with traditional co-detection approaches, it has been shown that inference in such fully-connected CRFs can be performed efficiently using an approximate mean-field method with high-dimensional Gaussian filtering. This lets us effectively leverage information in multiple images. Next, we extend our class-specific co-detection framework to multiple object categories. We model object candidates with rich, high-dimensional features learned using a deep convolutional neural network. In particular, our max-margin and directloss structural boosting algorithms enable us to learn the most suitable features that best encode pairwise similarity relationships within our CRF framework. Furthermore, it guarantees that the time and space complexity is O(n t) where n is the total number of candidate boxes in the pool and t the number of mean-field iterations. Moreover, our experiments evidence the importance of learning rich similarity measures to account for the contextual relations across object classes and instances. However, all these methods are based on precomputed object candidates (or proposals), thus localization performance is limited by the quality of bounding-boxes. To address this, we present an efficient object proposal co-generation technique that leverages the collective power of multiple images. In particular, we design a deep neural network layer that takes unary and pairwise features as input, builds a fully-connected CRF and produces mean-field marginals as output. It also lets us backpropagate the gradient through entire network by unrolling the iterations of CRF inference. Furthermore, this layer simplifies the end-to-end learning, thus effectively benefiting from multiple candidates to co-generate high-quality object proposals. Finally, we develop a multi-task strategy to jointly learn object detection, localization and instance-level semantic segmentation in a single network. In particular, we introduce a novel representation based on the distance transform of the object masks. To this end, we design a new residual-deconvolution architecture that infers such a representation and decodes it into the final binary object mask. We show that the predicted masks can go beyond the scope of the bounding boxes and that the multiple tasks can benefit from each other. In summary, in this thesis, we exploit the joint power of multiple images as well as multiple tasks to improve generalization performance of structured learning. Our novel deep structured models, similarity learning techniques and residual-deconvolution architecture can be used to make accurate and reliable inference for key vision tasks. Furthermore, our quantitative and qualitative experiments on large scale challenging image datasets demonstrate the superiority of the proposed approaches over the state-of-the-art methods