380,026 research outputs found
fMRI Investigation of Cortical and Subcortical Networks in the Learning of Abstract and Effector-Specific Representations of Motor Sequences
A visuomotor sequence can be learned as a series of visuo-spatial cues or as a sequence of effector movements. Earlier imaging studies have revealed that a network of brain areas is activated in the course of motor sequence learning. However these studies do not address the question of the type of representation being established at various stages of visuomotor sequence learning. In an earlier behavioral study, we demonstrated that acquisition of visuo-spatial sequence representation enables rapid learning in the early stage and progressive establishment of somato-motor representation helps speedier execution by the late stage. We conducted functional magnetic resonance imaging (fMRI) experiments wherein subjects learned and practiced the same sequence alternately in normal and rotated settings. In one rotated setting (visual), subjects learned a new motor sequence in response to an identical sequence of visual cues as in normal. In another rotated setting (motor), the display sequence was altered as compared to normal, but the same sequence of effector movements were used to perform the sequence. Comparison of different rotated settings revealed analogous transitions both in the cortical and subcortical sites during visuomotor sequence learning  a transition of activity from parietal to parietal-premotor and then to premotor cortex and a concomitant shift was observed from anterior putamen to a combined activity in both anterior and posterior putamen and finally to posterior putamen. These results suggest a putative role for engagement of different cortical and subcortical networks at various stages of learning in supporting distinct sequence representations
TSO: Curriculum Generation using continuous optimization
The training of deep learning models poses vast challenges of including
parameter tuning and ordering of training data. Significant research has been
done in Curriculum learning for optimizing the sequence of training data.
Recent works have focused on using complex reinforcement learning techniques to
find the optimal data ordering strategy to maximize learning for a given
network. In this paper, we present a simple and efficient technique based on
continuous optimization. We call this new approach Training Sequence
Optimization (TSO). There are three critical components in our proposed
approach: (a) An encoder network maps/embeds training sequence into continuous
space. (b) A predictor network uses the continuous representation of a strategy
as input and predicts the accuracy for fixed network architecture. (c) A
decoder further maps a continuous representation of a strategy to the ordered
training dataset. The performance predictor and encoder enable us to perform
gradient-based optimization in the continuous space to find the embedding of
optimal training data ordering with potentially better accuracy. Experiments
show that we can gain 2AP with our generated optimal curriculum strategy over
the random strategy using the CIFAR-100 dataset and have better boosts than the
state of the art CL algorithms. We do an ablation study varying the
architecture, dataset and sample sizes showcasing our approach's robustness.Comment: 10 pages, along with all experiment detail
Learning Audio Sequence Representations for Acoustic Event Classification
Acoustic Event Classification (AEC) has become a significant task for
machines to perceive the surrounding auditory scene. However, extracting
effective representations that capture the underlying characteristics of the
acoustic events is still challenging. Previous methods mainly focused on
designing the audio features in a 'hand-crafted' manner. Interestingly,
data-learnt features have been recently reported to show better performance. Up
to now, these were only considered on the frame-level. In this paper, we
propose an unsupervised learning framework to learn a vector representation of
an audio sequence for AEC. This framework consists of a Recurrent Neural
Network (RNN) encoder and a RNN decoder, which respectively transforms the
variable-length audio sequence into a fixed-length vector and reconstructs the
input sequence on the generated vector. After training the encoder-decoder, we
feed the audio sequences to the encoder and then take the learnt vectors as the
audio sequence representations. Compared with previous methods, the proposed
method can not only deal with the problem of arbitrary-lengths of audio
streams, but also learn the salient information of the sequence. Extensive
evaluation on a large-size acoustic event database is performed, and the
empirical results demonstrate that the learnt audio sequence representation
yields a significant performance improvement by a large margin compared with
other state-of-the-art hand-crafted sequence features for AEC
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