207159665
The relationship between Precision-Recall and ROC curves
Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm's performance. We show that a deep connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve. Finally, we also note differences in the two types of curves are significant for algorithm design. For example, in PR space it is incorrect to linearly interpolate between points. Furthermore, algorithms that optimize the area under the ROC curve are not guaranteed to optimize the area under the PR curve.
16674358
Change of Representation for Statistical Relational Learning
Statistical relational learning (SRL) algorithms learn statistical models from relational data, such as that stored in a relational database. We previously introduced view learning for SRL, in which the view of a relational database can be automatically modified, yielding more accurate statistical models. The present paper presents SAYU-VISTA, an algorithm which advances beyond the initial view learning approach in three ways. First, it learns views that introduce new relational tables, rather than merely new fields for an existing table of the database. Second, new tables or new fields are not limited to being approximations to some target concept; instead, the new approach performs a type of predicate invention. The new approach avoids the classical problem with predicate invention, of learning many useless predicates, by keeping only new fields or tables (i.e., new predicates) that immediately improve the performance of the statistical model. Third, retained fields or tables can then be used in the definitions of further new fields or tables. We evaluate the new view learning approach on three relational classification tasks.
964453
An Efficient Approximation to Lookahead in Relational Learners
Greedy machine learning algorithms suffer from shortsightedness, potentially returning suboptimal models due to limited exploration of the search space. Greedy search misses useful refinements that yield a significant gain only in conjunction with other conditions. Relational learners, such as inductive logic programming algorithms, are especially susceptible to this problem. Lookahead helps greedy search overcome myopia; unfortunately it causes an exponential increase in execution time. Furthermore, it may lead to overfitting. We propose a heuristic for greedy relational learning algorithms that can be seen as an efficient, limited form of lookahead. Our experimental evaluation shows that the proposed heuristic yields models that are as accurate as models generated using lookahead. It is also considerably faster than lookahead.
14449649
The RASE (Rapid, Accurate Simulation Environment) for chip multiprocessors
We present RASE, a full system high performance simulation methodology for simulating complex server applications and server class chip multiprocessors enabled with fine-grain multithreading (CMTs). RASE combines application knowledge, operating system information, and data access patterns with an instruction stream from a highly-tuned, scalable steady-state benchmark [5] [22] to generate multiple representative instruction streams that can be mapped to a variety of CMT configurations. We use execution-driven simulation to generate instruction streams for M processors and store them as instruction trace files (several billion instructions per processor) that can be post-processed and augmented for larger than M processor system simulation. We use SPEC JBB2000, TPC-C, and an XML server benchmark to compare the performance estimates of RASE to a reference prototype CMT system. By varying M, we find that our trace-driven simulation methodology predicts within 5% of the instructions per cycle (IPC) of the reference hardware for the applications. Without post-processing the traces, in the best cases, the performance prediction accuracy degrades to 20-40% of the real IPC for instruction traces that require a high replication factor.
2294366
The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces
We present an unsupervised algorithm for registering 3D surface scans of an object undergoing significant deformations. Our algorithm does not need markers, nor does it assume prior knowledge about object shape, the dynamics of its deformation, or scan alignment. The algorithm registers two meshes by optimizing a joint probabilistic model over all point-to-point correspondences between them. This model enforces preservation of local mesh geometry, as well as more global constraints that capture the preservation of geodesic distance between corresponding point pairs. The algorithm applies even when one of the meshes is an incomplete range scan; thus, it can be used to automatically fill in the remaining surfaces for this partial scan, even if those surfaces were previously only seen in a different configuration. We evaluate the algorithm on several real-world datasets, where we demonstrate good results in the presence of significant movement of articulated parts and non-rigid surface deformation. Finally, we show that the output of the algorithm can be used for compelling computer graphics tasks such as interpolation between two scans of a non-rigid object and automatic recovery of articulated object models.
9694396
Assigning Intonational Features in Synthesized Spoken Directions
Speakers convey much of the information hearers use to interpret discourse by varying prosodic features such as PHRASING, PITCH ACCENT placement, TUNE, and PITCH RANGE. The ability to emulate such variation is crucial to effective (synthetic) speech generation. While text-to-speech synthesis must rely primarily upon structural information to determine appropriate intonational features, speech synthesized from an abstract representation of the message to be conveyed may employ much richer sources. The implementation of an intonation assignment component for Direction Assistance, a program which generates spoken directions, provides a first approximation of how recent models of discourse structure can be used to control intonational variation in ways that build upon recent research in intonational meaning. The implementation further suggests ways in which these discourse models might be augmented to permit the assignment of appropriate intonational features.
15437979
Why direct LDA is not equivalent to LDA
In this paper, we present counter arguments against the direct LDA algorithm (D-LDA), which was previously claimed to be equivalent to Linear Discriminant Analysis (LDA). We show from Bayesian decision theory that D-LDA is actually a special case of LDA by directly taking the linear space of class means as the LDA solution. The pooled covariance estimate is completely ignored. Furthermore, we demonstrate that D-LDA is not equivalent to traditional subspace-based LDA in dealing with the Small Sample Size problem. As a result, D-LDA may impose a significant performance limitation in general applications.
5870313
BRDF Invariant Stereo Using Light Transport Constancy
Nearly all existing methods for stereo reconstruction assume that scene reflectance is Lambertian and make use of brightness constancy as a matching invariant. We introduce a new invariant for stereo reconstruction called light transport constancy (LTC), which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions (BRDFs)). This invariant can be used to formulate a rank constraint on multiview stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies. In addition, we show that this multiview constraint can be used with as few as two cameras and two lighting configurations. Unlike previous methods for BRDF invariant stereo, LTC does not require precisely configured or calibrated light sources or calibration objects in the scene. Importantly, the new constraint can be used to provide BRDF invariance to any existing stereo method whenever appropriate lighting variation is available.