178,251 research outputs found
Analytical learning and term-rewriting systems
Analytical learning is a set of machine learning techniques for revising the representation of a theory based on a small set of examples of that theory. When the representation of the theory is correct and complete but perhaps inefficient, an important objective of such analysis is to improve the computational efficiency of the representation. Several algorithms with this purpose have been suggested, most of which are closely tied to a first order logical language and are variants of goal regression, such as the familiar explanation based generalization (EBG) procedure. But because predicate calculus is a poor representation for some domains, these learning algorithms are extended to apply to other computational models. It is shown that the goal regression technique applies to a large family of programming languages, all based on a kind of term rewriting system. Included in this family are three language families of importance to artificial intelligence: logic programming, such as Prolog; lambda calculus, such as LISP; and combinatorial based languages, such as FP. A new analytical learning algorithm, AL-2, is exhibited that learns from success but is otherwise quite different from EBG. These results suggest that term rewriting systems are a good framework for analytical learning research in general, and that further research should be directed toward developing new techniques
Unconstrained Face Detection and Open-Set Face Recognition Challenge
Face detection and recognition benchmarks have shifted toward more difficult
environments. The challenge presented in this paper addresses the next step in
the direction of automatic detection and identification of people from outdoor
surveillance cameras. While face detection has shown remarkable success in
images collected from the web, surveillance cameras include more diverse
occlusions, poses, weather conditions and image blur. Although face
verification or closed-set face identification have surpassed human
capabilities on some datasets, open-set identification is much more complex as
it needs to reject both unknown identities and false accepts from the face
detector. We show that unconstrained face detection can approach high detection
rates albeit with moderate false accept rates. By contrast, open-set face
recognition is currently weak and requires much more attention.Comment: This is an ERRATA version of the paper originally presented at the
International Joint Conference on Biometrics. Due to a bug in our evaluation
code, the results of the participants changed. The final conclusion, however,
is still the sam
dARTMAP: A Neural Network for Fast Distributed Supervised Learning
Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, ART stability typically requires winner-take-all coding, which may cause category proliferation in a noisy input environment. Distributed ARTMAP (dARTMAP) seeks to combine the computational advantages of MLP and ART systems in a real-time neural network for supervised learning, An implementation algorithm here describes one class of dARTMAP networks. This system incorporates elements of the unsupervised dART model as well as new features, including a content-addressable memory (CAM) rule for improved contrast control at the coding field. A dARTMAP system reduces to fuzzy ARTMAP when coding is winner-take-all. Simulations show that dARTMAP retains fuzzy ARTMAP accuracy while significantly improving memory compression.National Science Foundation (IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-0657
Verified AIG Algorithms in ACL2
And-Inverter Graphs (AIGs) are a popular way to represent Boolean functions
(like circuits). AIG simplification algorithms can dramatically reduce an AIG,
and play an important role in modern hardware verification tools like
equivalence checkers. In practice, these tricky algorithms are implemented with
optimized C or C++ routines with no guarantee of correctness. Meanwhile, many
interactive theorem provers can now employ SAT or SMT solvers to automatically
solve finite goals, but no theorem prover makes use of these advanced,
AIG-based approaches.
We have developed two ways to represent AIGs within the ACL2 theorem prover.
One representation, Hons-AIGs, is especially convenient to use and reason
about. The other, Aignet, is the opposite; it is styled after modern AIG
packages and allows for efficient algorithms. We have implemented functions for
converting between these representations, random vector simulation, conversion
to CNF, etc., and developed reasoning strategies for verifying these
algorithms.
Aside from these contributions towards verifying AIG algorithms, this work
has an immediate, practical benefit for ACL2 users who are using GL to
bit-blast finite ACL2 theorems: they can now optionally trust an off-the-shelf
SAT solver to carry out the proof, instead of using the built-in BDD package.
Looking to the future, it is a first step toward implementing verified AIG
simplification algorithms that might further improve GL performance.Comment: In Proceedings ACL2 2013, arXiv:1304.712
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