370,983 research outputs found
ON LEARNING FROM AMBIGUOUS INFORMATION
We investigate a variant of Probably Almost Correct learning model where the learner
has to learn from ambiguous information. The ambiguity is introduced by assuming that
the learner does not receive single instances with their correct labels as training data,
but that the learner receives tuples of instances where a tuple has a negative label if all
instances of the tuple should be labeled as negative and a tuple has a positive label if
at least one instance of the tuple should be labeled as positive. Thus, a positive tuple is
ambiguous since it is not known which of its instances is a positive instance.
Such ambiguous information is, for example, relevant in learning problems for drug
design. We present an improved algorithm for learning axis-parallel rectangles in this
model of ambiguous information. In the drug design domain such rectangles represent the
shapes of molecules with certain properties
Learning Under Ambiguity
This paper considers learning when the distinction between risk and ambiguity (Knightian uncertainty) matters. Working within the framework of recursive multiple-priors utility, the paper formulates a counterpart of the Bayesian model of learning about an uncertain parameter from conditionally i.i.d. signals. Ambiguous signals capture responses to information that cannot be captured by noisy signals. They induce nonmonotonic changes in agent confidence and prevent ambiguity from vanishing in the limit. In a dynamic portfolio choice model, learning about ambiguous returns leads to endogenous stock market participation costs that depend on past market performance. Hedging of ambiguity provides a new reason why the investment horizon matters for portfolio choice.ambiguity, learning, noisy signals, ambiguous signals, quality information, portfolio choice, portfolio diversification, Ellsberg Paradox
Imperfect Central Bank Communication: Information versus Distraction
Much of the information communicated by central banks is noisy or imperfect. This paper considers the potential benefits and limitations of central bank communications in a model of imperfect knowledge and learning. It is shown that the value of communicating imperfect information is ambiguous. If the public is able to assess accurately the quality of the imperfect information communicated by a central bank, such communication can inform and improve the publicās decisions and expectations. But if not, communicating imperfect communication has the potential to mislead and distract. The risk that imperfect communication may detract from the publicās understanding should be considered in the context of a central bankās communications strategy. The risk of distraction means the central bank may prefer to focus its communication policies on the information it knows most about. Indeed, conveying more certain information may improve the publicās understanding to the extent that it "crowds out" a role for communicating imperfect information.Transparency, forecasts, learning
Opposite Influence of Perceptual Memory on Initial and Prolonged Perception of Sensory Ambiguity
Observers continually make unconscious inferences about the state of the world based on ambiguous sensory information. This process of perceptual decision-making may be optimized by learning from experience. We investigated the influence of previous perceptual experience on the interpretation of ambiguous visual information. Observers were pre-exposed to a perceptually stabilized sequence of an ambiguous structure-from-motion stimulus by means of intermittent presentation. At the subsequent re-appearance of the same ambiguous stimulus perception was initially biased toward the previously stabilized perceptual interpretation. However, prolonged viewing revealed a bias toward the alternative perceptual interpretation. The prevalence of the alternative percept during ongoing viewing was largely due to increased durations of this percept, as there was no reliable decrease in the durations of the pre-exposed percept. Moreover, the duration of the alternative percept was modulated by the specific characteristics of the pre-exposure, whereas the durations of the pre-exposed percept were not. The increase in duration of the alternative percept was larger when the pre-exposure had lasted longer and was larger after ambiguous pre-exposure than after unambiguous pre-exposure. Using a binocular rivalry stimulus we found analogous perceptual biases, while pre-exposure did not affect eye-bias. We conclude that previously perceived interpretations dominate at the onset of ambiguous sensory information, whereas alternative interpretations dominate prolonged viewing. Thus, at first instance ambiguous information seems to be judged using familiar percepts, while re-evaluation later on allows for alternative interpretations
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
In the field of connectomics, neuroscientists seek to identify cortical
connectivity comprehensively. Neuronal boundary detection from the Electron
Microscopy (EM) images is often done to assist the automatic reconstruction of
neuronal circuit. But the segmentation of EM images is a challenging problem,
as it requires the detector to be able to detect both filament-like thin and
blob-like thick membrane, while suppressing the ambiguous intracellular
structure. In this paper, we propose multi-stage multi-recursive-input fully
convolutional networks to address this problem. The multiple recursive inputs
for one stage, i.e., the multiple side outputs with different receptive field
sizes learned from the lower stage, provide multi-scale contextual boundary
information for the consecutive learning. This design is
biologically-plausible, as it likes a human visual system to compare different
possible segmentation solutions to address the ambiguous boundary issue. Our
multi-stage networks are trained end-to-end. It achieves promising results on
two public available EM segmentation datasets, the mouse piriform cortex
dataset and the ISBI 2012 EM dataset.Comment: Accepted by ICCV201
The kindergarten-path effect revisited: childrenās use of context in processing structural ambiguities
Research with adults has shown that ambiguous spoken sentences are resolved efficiently, exploiting multiple cuesāincluding referential contextāto select the intended meaning. Paradoxically, children appear to be insensitive to referential cues when resolving ambiguous sentences, relying instead on statistical properties intrinsic to the language such as verb biases. The possibility that childrenās insensitivity to referential context may be an artifact of the experimental design used in previous work was explored with 60 4- to 11-year-olds. An act-out task was designed to discourage children from making incorrect pragmatic inferences and to prevent premature and ballistic responses by enforcing delayed actions. Performance on this task was compared directly with the standard act-out task used in previous studies. The results suggest that young children (5 years) do not use contextual information, even under conditions designed to maximize their use of such cues, but that adult-like processing is evident by approximately 8 years of age. These results support and extend previous findings by Trueswell and colleagues (Cognition (1999), Vol. 73, pp. 89ā134) and are consistent with a constraint-based learning account of childrenās linguistic development.</p
Evaluation on knowledge extraction and machine learning in resolving Malay word ambiguity
The involvement of linguistic professionals in resolving the ambiguity of a word within a particular context will produce a concise meaning of the words that are found in the lexical knowledge based collection. Motivated from that issue, we employed lexical knowledge and machine learning approach which includes the integration of data or/and information from the lexical knowledge based, that is Malay collections which linked to the ambiguous words. We used the most open class word and removed the stop words from the targeted sentences. Experiments have been conducted with and without lexical knowledge on 50 ambiguous words. The Word Sense Disambiguation (WSD) method is determined by machine learning, corpus based approaches namely Malay-Malay corpus and English-Malay corpus. The results show that the proposed method has improved the precision in resolving ambiguity.Keywords: ambiguity; lexical knowledge; machine learning; Malay wor
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