193,509 research outputs found
Syntactic structure and artificial grammar learning : The learnability of embedded hierarchical structures
Embedded hierarchical structures, such as ‘‘the rat the cat ate was brown’’, constitute a core generative property of a natural language theory. Several recent studies have reported learning of hierarchical embeddings in artificial grammar learning (AGL) tasks, and described the functional specificity of Broca’s area for processing such structures. In two experiments, we investigated whether alternative strategies can explain the learning success in these studies. We trained participants on hierarchical sequences, and found no evidence for the learning of hierarchical embeddings in test situations identical to those from other studies in the literature. Instead, participants appeared to solve the task by exploiting surface distinctions between legal and illegal sequences, and applying strategies such as counting or repetition detection. We suggest alternative interpretations for the observed activation of Broca’s area, in terms of the application of calculation rules or of a differential role of working memory. We claim that the learnability of hierarchical embeddings in AGL tasks remains to be demonstrated
Learning with Latent Language
The named concepts and compositional operators present in natural language
provide a rich source of information about the kinds of abstractions humans use
to navigate the world. Can this linguistic background knowledge improve the
generality and efficiency of learned classifiers and control policies? This
paper aims to show that using the space of natural language strings as a
parameter space is an effective way to capture natural task structure. In a
pretraining phase, we learn a language interpretation model that transforms
inputs (e.g. images) into outputs (e.g. labels) given natural language
descriptions. To learn a new concept (e.g. a classifier), we search directly in
the space of descriptions to minimize the interpreter's loss on training
examples. Crucially, our models do not require language data to learn these
concepts: language is used only in pretraining to impose structure on
subsequent learning. Results on image classification, text editing, and
reinforcement learning show that, in all settings, models with a linguistic
parameterization outperform those without
Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression
This paper addresses the problem of localizing audio sources using binaural
measurements. We propose a supervised formulation that simultaneously localizes
multiple sources at different locations. The approach is intrinsically
efficient because, contrary to prior work, it relies neither on source
separation, nor on monaural segregation. The method starts with a training
stage that establishes a locally-linear Gaussian regression model between the
directional coordinates of all the sources and the auditory features extracted
from binaural measurements. While fixed-length wide-spectrum sounds (white
noise) are used for training to reliably estimate the model parameters, we show
that the testing (localization) can be extended to variable-length
sparse-spectrum sounds (such as speech), thus enabling a wide range of
realistic applications. Indeed, we demonstrate that the method can be used for
audio-visual fusion, namely to map speech signals onto images and hence to
spatially align the audio and visual modalities, thus enabling to discriminate
between speaking and non-speaking faces. We release a novel corpus of real-room
recordings that allow quantitative evaluation of the co-localization method in
the presence of one or two sound sources. Experiments demonstrate increased
accuracy and speed relative to several state-of-the-art methods.Comment: 15 pages, 8 figure
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