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

Visual Chunking: A List Prediction Framework for Region-Based Object Detection

We consider detecting objects in an image by iteratively selecting from a set of arbitrarily shaped candidate regions. Our generic approach, which we term visual chunking, reasons about the locations of multiple object instances in an image while expressively describing object boundaries. We design an optimization criterion for measuring the performance of a list of such detections as a natural extension to a common per-instance metric. We present an efficient algorithm with provable performance for building a high-quality list of detections from any candidate set of region-based proposals. We also develop a simple class-specific algorithm to generate a candidate region instance in near-linear time in the number of low-level superpixels that outperforms other region generating methods. In order to make predictions on novel images at testing time without access to ground truth, we develop learning approaches to emulate these algorithms' behaviors. We demonstrate that our new approach outperforms sophisticated baselines on benchmark datasets.Comment: to appear at ICRA 201

Incremental Training of a Detector Using Online Sparse Eigen-decomposition

The ability to efficiently and accurately detect objects plays a very crucial role for many computer vision tasks. Recently, offline object detectors have shown a tremendous success. However, one major drawback of offline techniques is that a complete set of training data has to be collected beforehand. In addition, once learned, an offline detector can not make use of newly arriving data. To alleviate these drawbacks, online learning has been adopted with the following objectives: (1) the technique should be computationally and storage efficient; (2) the updated classifier must maintain its high classification accuracy. In this paper, we propose an effective and efficient framework for learning an adaptive online greedy sparse linear discriminant analysis (GSLDA) model. Unlike many existing online boosting detectors, which usually apply exponential or logistic loss, our online algorithm makes use of LDA's learning criterion that not only aims to maximize the class-separation criterion but also incorporates the asymmetrical property of training data distributions. We provide a better alternative for online boosting algorithms in the context of training a visual object detector. We demonstrate the robustness and efficiency of our methods on handwriting digit and face data sets. Our results confirm that object detection tasks benefit significantly when trained in an online manner.Comment: 14 page

Waterfall Traffic Classification: A Quick Approach to Optimizing Cascade Classifiers

Heterogeneous wireless communication networks, like 4G LTE, transport diverse kinds of IP traffic: voice, video, Internet data, and more. In order to effectively manage such networks, administrators need adequate tools, of which traffic classification is the basis for visualizing, shaping, and filtering the broad streams of IP packets observed nowadays. In this paper, we describe a modular, cascading traffic classification system—the Waterfall architecture—and we extensively describe a novel technique for its optimization—in terms of CPU time, number of errors, and percentage of unrecognized flows. We show how to significantly accelerate the process of exhaustive search for the best performing cascade. We employ five datasets of real Internet transmissions and seven traffic analysis methods to demonstrate that our proposal yields valid results and outperforms a greedy optimizer

Supervised Machine Learning Under Test-Time Resource Constraints: A Trade-off Between Accuracy and Cost

The past decade has witnessed how the field of machine learning has established itself as a necessary component in several multi-billion-dollar industries. The real-world industrial setting introduces an interesting new problem to machine learning research: computational resources must be budgeted and cost must be strictly accounted for during test-time. A typical problem is that if an application consumes x additional units of cost during test-time, but will improve accuracy by y percent, should the additional x resources be allocated? The core of this problem is a trade-off between accuracy and cost. In this thesis, we examine components of test-time cost, and develop different strategies to manage this trade-off. We first investigate test-time cost and discover that it typically consists of two parts: feature extraction cost and classifier evaluation cost. The former reflects the computational efforts of transforming data instances to feature vectors, and could be highly variable when features are heterogeneous. The latter reflects the effort of evaluating a classifier, which could be substantial, in particular nonparametric algorithms. We then propose three strategies to explicitly trade-off accuracy and the two components of test-time cost during classifier training. To budget the feature extraction cost, we first introduce two algorithms: GreedyMiser and Anytime Representation Learning (AFR). GreedyMiser employs a strategy that incorporates the extraction cost information during classifier training to explicitly minimize the test-time cost. AFR extends GreedyMiser to learn a cost-sensitive feature representation rather than a classifier, and turns traditional Support Vector Machines (SVM) into test- time cost-sensitive anytime classifiers. GreedyMiser and AFR are evaluated on two real-world data sets from two different application domains, and both achieve record performance. We then introduce Cost Sensitive Tree of Classifiers (CSTC) and Cost Sensitive Cascade of Classifiers (CSCC), which share a common strategy that trades-off the accuracy and the amortized test-time cost. CSTC introduces a tree structure and directs test inputs along different tree traversal paths, each is optimized for a specific sub-partition of the input space, extracting different, specialized subsets of features. CSCC extends CSTC and builds a linear cascade, instead of a tree, to cope with class-imbalanced binary classification tasks. Since both CSTC and CSCC extract different features for different inputs, the amortized test-time cost is greatly reduced while maintaining high accuracy. Both approaches out-perform the current state-of-the-art on real-world data sets. To trade-off accuracy and high classifier evaluation cost of nonparametric classifiers, we propose a model compression strategy and develop Compressed Vector Machines (CVM). CVM focuses on the nonparametric kernel Support Vector Machines (SVM), whose test-time evaluation cost is typically substantial when learned from large training sets. CVM is a post-processing algorithm which compresses the learned SVM model by reducing and optimizing support vectors. On several benchmark data sets, CVM maintains high test accuracy while reducing the test-time evaluation cost by several orders of magnitude