@article{3320,
abstract = {Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints. This monograph introduces the reader to the most popular classes of structured models in computer vision. Our focus is discrete undirected graphical models which we cover in detail together with a description of algorithms for both probabilistic inference and maximum a posteriori inference. We discuss separately recently successful techniques for prediction in general structured models. In the second part of this monograph we describe methods for parameter learning where we distinguish the classic maximum likelihood based methods from the more recent prediction-based parameter learning methods. We highlight developments to enhance current models and discuss kernelized models and latent variable models. To make the monograph more practical and to provide links to further study we provide examples of successful application of many methods in the computer vision literature.},
author = {Nowozin, Sebastian and Lampert, Christoph},
journal = {Foundations and Trends in Computer Graphics and Vision},
number = {3-4},
pages = {185 -- 365},
publisher = {now},
title = {{Structured learning and prediction in computer vision}},
doi = {10.1561/0600000033},
volume = {6},
year = {2011},
}
@misc{3322,
abstract = {We study multi-label prediction for structured output spaces, a problem that occurs, for example, in object detection in images, secondary structure prediction in computational biology, and graph matching with symmetries. Conventional multi-label classification techniques are typically not applicable in this situation, because they require explicit enumeration of the label space, which is infeasible in case of structured outputs. Relying on techniques originally designed for single- label structured prediction, in particular structured support vector machines, results in reduced prediction accuracy, or leads to infeasible optimization problems. In this work we derive a maximum-margin training formulation for multi-label structured prediction that remains computationally tractable while achieving high prediction accuracy. It also shares most beneficial properties with single-label maximum-margin approaches, in particular a formulation as a convex optimization problem, efficient working set training, and PAC-Bayesian generalization bounds.},
author = {Lampert, Christoph},
booktitle = {NIPS: Neural Information Processing Systems},
publisher = {Neural Information Processing Systems},
title = {{Maximum margin multi label structured prediction}},
year = {2011},
}
@inproceedings{3323,
abstract = {We present a new decidable logic called TREX for expressing constraints about imperative tree data structures. In particular, TREX supports a transitive closure operator that can express reachability constraints, which often appear in data structure invariants. We show that our logic is closed under weakest precondition computation, which enables its use for automated software verification. We further show that satisfiability of formulas in TREX is decidable in NP. The low complexity makes it an attractive alternative to more expensive logics such as monadic second-order logic (MSOL) over trees, which have been traditionally used for reasoning about tree data structures.},
author = {Wies, Thomas and Muñiz, Marco and Kuncak, Viktor},
location = {Wrocław, Poland},
pages = {476 -- 491},
publisher = {Springer},
title = {{An efficient decision procedure for imperative tree data structures}},
doi = {10.1007/978-3-642-22438-6_36},
volume = {6803},
year = {2011},
}
@inproceedings{3324,
abstract = {Automated termination provers often use the following schema to prove that a program terminates: construct a relational abstraction of the program's transition relation and then show that the relational abstraction is well-founded. The focus of current tools has been on developing sophisticated techniques for constructing the abstractions while relying on known decidable logics (such as linear arithmetic) to express them. We believe we can significantly increase the class of programs that are amenable to automated termination proofs by identifying more expressive decidable logics for reasoning about well-founded relations. We therefore present a new decision procedure for reasoning about multiset orderings, which are among the most powerful orderings used to prove termination. We show that, using our decision procedure, one can automatically prove termination of natural abstractions of programs.},
author = {Piskac, Ruzica and Wies, Thomas},
editor = {Jhala, Ranjit and Schmidt, David},
location = {Texas, USA},
pages = {371 -- 386},
publisher = {Springer},
title = {{Decision procedures for automating termination proofs}},
doi = {10.1007/978-3-642-18275-4_26},
volume = {6538},
year = {2011},
}
@inproceedings{3325,
abstract = {We introduce streaming data string transducers that map input data strings to output data strings in a single left-to-right pass in linear time. Data strings are (unbounded) sequences of data values, tagged with symbols from a finite set, over a potentially infinite data do- main that supports only the operations of equality and ordering. The transducer uses a finite set of states, a finite set of variables ranging over the data domain, and a finite set of variables ranging over data strings. At every step, it can make decisions based on the next in- put symbol, updating its state, remembering the input data value in its data variables, and updating data-string variables by concatenat- ing data-string variables and new symbols formed from data vari- ables, while avoiding duplication. We establish that the problems of checking functional equivalence of two streaming transducers, and of checking whether a streaming transducer satisfies pre/post verification conditions specified by streaming acceptors over in- put/output data-strings, are in PSPACE. We identify a class of imperative and a class of functional pro- grams, manipulating lists of data items, which can be effectively translated to streaming data-string transducers. The imperative pro- grams dynamically modify a singly-linked heap by changing next- pointers of heap-nodes and by adding new nodes. The main re- striction specifies how the next-pointers can be used for traversal. We also identify an expressively equivalent fragment of functional programs that traverse a list using syntactically restricted recursive calls. Our results lead to algorithms for assertion checking and for checking functional equivalence of two programs, written possibly in different programming styles, for commonly used routines such as insert, delete, and reverse.},
author = {Alur, Rajeev and Cerny, Pavol},
location = {Texas, USA},
number = {1},
pages = {599 -- 610},
publisher = {ACM},
title = {{Streaming transducers for algorithmic verification of single pass list processing programs}},
doi = {10.1145/1926385.1926454},
volume = {46},
year = {2011},
}