34 research outputs found
Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks
Recent approaches for dialogue act recognition have shown that context from
preceding utterances is important to classify the subsequent one. It was shown
that the performance improves rapidly when the context is taken into account.
We propose an utterance-level attention-based bidirectional recurrent neural
network (Utt-Att-BiRNN) model to analyze the importance of preceding utterances
to classify the current one. In our setup, the BiRNN is given the input set of
current and preceding utterances. Our model outperforms previous models that
use only preceding utterances as context on the used corpus. Another
contribution of the article is to discover the amount of information in each
utterance to classify the subsequent one and to show that context-based
learning not only improves the performance but also achieves higher confidence
in the classification. We use character- and word-level features to represent
the utterances. The results are presented for character and word feature
representations and as an ensemble model of both representations. We found that
when classifying short utterances, the closest preceding utterances contributes
to a higher degree.Comment: Proceedings of INTERSPEECH 201
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks
Ensemble methods, traditionally built with independently trained
de-correlated models, have proven to be efficient methods for reducing the
remaining residual generalization error, which results in robust and accurate
methods for real-world applications. In the context of deep learning, however,
training an ensemble of deep networks is costly and generates high redundancy
which is inefficient. In this paper, we present experiments on Ensembles with
Shared Representations (ESRs) based on convolutional networks to demonstrate,
quantitatively and qualitatively, their data processing efficiency and
scalability to large-scale datasets of facial expressions. We show that
redundancy and computational load can be dramatically reduced by varying the
branching level of the ESR without loss of diversity and generalization power,
which are both important for ensemble performance. Experiments on large-scale
datasets suggest that ESRs reduce the remaining residual generalization error
on the AffectNet and FER+ datasets, reach human-level performance, and
outperform state-of-the-art methods on facial expression recognition in the
wild using emotion and affect concepts.Comment: Accepted at the Thirty-Fourth AAAI Conference on Artificial
Intelligence (AAAI-20), 1-1, New York, US
Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks
The complexity of bipedal locomotion may be attributed to the difficulty in
synchronizing joint movements while at the same time achieving high-level
objectives such as walking in a particular direction. Artificial central
pattern generators (CPGs) can produce synchronized joint movements and have
been used in the past for bipedal locomotion. However, most existing CPG-based
approaches do not address the problem of high-level control explicitly. We
propose a novel hierarchical control mechanism for bipedal locomotion where an
optimized CPG network is used for joint control and a neural network acts as a
high-level controller for modulating the CPG network. By separating motion
generation from motion modulation, the high-level controller does not need to
control individual joints directly but instead can develop to achieve a higher
goal using a low-dimensional control signal. The feasibility of the
hierarchical controller is demonstrated through simulation experiments using
the Neuro-Inspired Companion (NICO) robot. Experimental results demonstrate the
controller's ability to function even without the availability of an exact
robot model.Comment: In: Proceedings of the Joint IEEE International Conference on
Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), Oslo,
Norway, Aug. 19-22, 201
Improving interactive reinforcement learning: What makes a good teacher?
Interactive reinforcement learning has become an important apprenticeship approach to speed up convergence in classic reinforcement learning problems. In this regard, a variant of interactive reinforcement learning is policy shaping which uses a parent-like trainer to propose the next action to be performed and by doing so reduces the search space by advice. On some occasions, the trainer may be another artificial agent which in turn was trained using reinforcement learning methods to afterward becoming an advisor for other learner-agents. In this work, we analyze internal representations and characteristics of artificial agents to determine which agent may outperform others to become a better trainer-agent. Using a polymath agent, as compared to a specialist agent, an advisor leads to a larger reward and faster convergence of the reward signal and also to a more stable behavior in terms of the state visit frequency of the learner-agents. Moreover, we analyze system interaction parameters in order to determine how influential they are in the apprenticeship process, where the consistency of feedback is much more relevant when dealing with different learner obedience parameters.Sociedad Argentina de Inform谩tica e Investigaci贸n Operativ