658 research outputs found
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Neural networks are among the most accurate supervised learning methods in
use today, but their opacity makes them difficult to trust in critical
applications, especially when conditions in training differ from those in test.
Recent work on explanations for black-box models has produced tools (e.g. LIME)
to show the implicit rules behind predictions, which can help us identify when
models are right for the wrong reasons. However, these methods do not scale to
explaining entire datasets and cannot correct the problems they reveal. We
introduce a method for efficiently explaining and regularizing differentiable
models by examining and selectively penalizing their input gradients, which
provide a normal to the decision boundary. We apply these penalties both based
on expert annotation and in an unsupervised fashion that encourages diverse
models with qualitatively different decision boundaries for the same
classification problem. On multiple datasets, we show our approach generates
faithful explanations and models that generalize much better when conditions
differ between training and test
Using Sat solvers for synchronization issues in partial deterministic automata
We approach the task of computing a carefully synchronizing word of minimum
length for a given partial deterministic automaton, encoding the problem as an
instance of SAT and invoking a SAT solver. Our experimental results demonstrate
that this approach gives satisfactory results for automata with up to 100
states even if very modest computational resources are used.Comment: 15 pages, 3 figure
A framework for dialogue detection in movies
In this paper, we investigate a novel framework for dialogue detection that is based on indicator functions. An indicator function defines that a particular actor is present at each time instant. Two dialogue detection rules are developed and assessed. The first rule relies on the value of the cross-correlation function at zero time lag that is compared to a threshold. The second rule is based on the cross-power in a particular frequency band that is also compared to a threshold. Experiments are carried out in order to validate the feasibility of the aforementioned dialogue detection rules by using ground-truth indicator functions determined by human observers from six different movies. A total of 25 dialogue scenes and another 8 non-dialogue scenes are employed. The probabilities of false alarm and detection are estimated by cross-validation, where 70% of the available scenes are used to learn the thresholds employed in the dialogue detection rules and the remaining 30% of the scenes are used for testing. An almost perfect dialogue detection is reported for every distinct threshold. © Springer-Verlag Berlin Heidelberg 2006
Named Entity Extraction and Disambiguation: The Reinforcement Effect.
Named entity extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. Although these topics are highly dependent, almost no existing works examine this dependency. It is the aim of this paper to examine the dependency and show how one affects the other, and vice versa. We conducted experiments with a set of descriptions of holiday homes with the aim to extract and disambiguate toponyms as a representative example of named entities. We experimented with three approaches for disambiguation with the purpose to infer the country of the holiday home. We examined how the effectiveness of extraction influences the effectiveness of disambiguation, and reciprocally, how filtering out ambiguous names (an activity that depends on the disambiguation process) improves the effectiveness of extraction. Since this, in turn, may improve the effectiveness of disambiguation again, it shows that extraction and disambiguation may reinforce each other.\u
A Theory of Formal Synthesis via Inductive Learning
Formal synthesis is the process of generating a program satisfying a
high-level formal specification. In recent times, effective formal synthesis
methods have been proposed based on the use of inductive learning. We refer to
this class of methods that learn programs from examples as formal inductive
synthesis. In this paper, we present a theoretical framework for formal
inductive synthesis. We discuss how formal inductive synthesis differs from
traditional machine learning. We then describe oracle-guided inductive
synthesis (OGIS), a framework that captures a family of synthesizers that
operate by iteratively querying an oracle. An instance of OGIS that has had
much practical impact is counterexample-guided inductive synthesis (CEGIS). We
present a theoretical characterization of CEGIS for learning any program that
computes a recursive language. In particular, we analyze the relative power of
CEGIS variants where the types of counterexamples generated by the oracle
varies. We also consider the impact of bounded versus unbounded memory
available to the learning algorithm. In the special case where the universe of
candidate programs is finite, we relate the speed of convergence to the notion
of teaching dimension studied in machine learning theory. Altogether, the
results of the paper take a first step towards a theoretical foundation for the
emerging field of formal inductive synthesis
Bounds on Depth of Decision Trees Derived from Decision Rule Systems
Systems of decision rules and decision trees are widely used as a means for
knowledge representation, as classifiers, and as algorithms. They are among the
most interpretable models for classifying and representing knowledge. The study
of relationships between these two models is an important task of computer
science. It is easy to transform a decision tree into a decision rule system.
The inverse transformation is a more difficult task. In this paper, we study
unimprovable upper and lower bounds on the minimum depth of decision trees
derived from decision rule systems depending on the various parameters of these
systems
Comparative Analysis of Deterministic and Nondeterministic Decision Trees for Decision Tables from Closed Classes
In this paper, we consider classes of decision tables with many-valued
decisions closed under operations of removal of columns, changing of decisions,
permutation of columns, and duplication of columns. We study relationships
among three parameters of these tables: the complexity of a decision table (if
we consider the depth of decision trees, then the complexity of a decision
table is the number of columns in it), the minimum complexity of a
deterministic decision tree, and the minimum complexity of a nondeterministic
decision tree. We consider rough classification of functions characterizing
relationships and enumerate all possible seven types of the relationships
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