1,899 research outputs found
Consistency of Feature Markov Processes
We are studying long term sequence prediction (forecasting). We approach this
by investigating criteria for choosing a compact useful state representation.
The state is supposed to summarize useful information from the history. We want
a method that is asymptotically consistent in the sense it will provably
eventually only choose between alternatives that satisfy an optimality property
related to the used criterion. We extend our work to the case where there is
side information that one can take advantage of and, furthermore, we briefly
discuss the active setting where an agent takes actions to achieve desirable
outcomes.Comment: 16 LaTeX page
A conversion between utility and information
Rewards typically express desirabilities or preferences over a set of
alternatives. Here we propose that rewards can be defined for any probability
distribution based on three desiderata, namely that rewards should be
real-valued, additive and order-preserving, where the latter implies that more
probable events should also be more desirable. Our main result states that
rewards are then uniquely determined by the negative information content. To
analyze stochastic processes, we define the utility of a realization as its
reward rate. Under this interpretation, we show that the expected utility of a
stochastic process is its negative entropy rate. Furthermore, we apply our
results to analyze agent-environment interactions. We show that the expected
utility that will actually be achieved by the agent is given by the negative
cross-entropy from the input-output (I/O) distribution of the coupled
interaction system and the agent's I/O distribution. Thus, our results allow
for an information-theoretic interpretation of the notion of utility and the
characterization of agent-environment interactions in terms of entropy
dynamics.Comment: AGI-2010. 6 pages, 1 figur
Compositional Semantic Parsing on Semi-Structured Tables
Two important aspects of semantic parsing for question answering are the
breadth of the knowledge source and the depth of logical compositionality.
While existing work trades off one aspect for another, this paper
simultaneously makes progress on both fronts through a new task: answering
complex questions on semi-structured tables using question-answer pairs as
supervision. The central challenge arises from two compounding factors: the
broader domain results in an open-ended set of relations, and the deeper
compositionality results in a combinatorial explosion in the space of logical
forms. We propose a logical-form driven parsing algorithm guided by strong
typing constraints and show that it obtains significant improvements over
natural baselines. For evaluation, we created a new dataset of 22,033 complex
questions on Wikipedia tables, which is made publicly available
The bees algorithm: Modelling nature to solve complex optimisation problems
The Bees Algorithm models the foraging behaviour of honey bees in order to solve optimisation problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This paper describes the Bees Algorithm and presents two application examples: the training of neural networks to predict the energy efficiency of buildings, and the solution of the protein folding problem. The Bees Algorithm proved its effectiveness and speed, and obtained very competitive modelling accuracies compared with other state-of-the-art methods
Reasoning About Pragmatics with Neural Listeners and Speakers
We present a model for pragmatically describing scenes, in which contrastive
behavior results from a combination of inference-driven pragmatics and learned
semantics. Like previous learned approaches to language generation, our model
uses a simple feature-driven architecture (here a pair of neural "listener" and
"speaker" models) to ground language in the world. Like inference-driven
approaches to pragmatics, our model actively reasons about listener behavior
when selecting utterances. For training, our approach requires only ordinary
captions, annotated _without_ demonstration of the pragmatic behavior the model
ultimately exhibits. In human evaluations on a referring expression game, our
approach succeeds 81% of the time, compared to a 69% success rate using
existing techniques
Online Learning of k-CNF Boolean Functions
This paper revisits the problem of learning a k-CNF Boolean function from
examples in the context of online learning under the logarithmic loss. In doing
so, we give a Bayesian interpretation to one of Valiant's celebrated PAC
learning algorithms, which we then build upon to derive two efficient, online,
probabilistic, supervised learning algorithms for predicting the output of an
unknown k-CNF Boolean function. We analyze the loss of our methods, and show
that the cumulative log-loss can be upper bounded, ignoring logarithmic
factors, by a polynomial function of the size of each example.Comment: 20 LaTeX pages. 2 Algorithms. Some Theorem
- âŠ