3,984 research outputs found
Modeling user navigation
This paper proposes the use of neural networks as a tool for studying navigation within virtual worlds. Results indicate that the network learned to predict the next step for a given trajectory. The analysis of hidden layer shows that the network was able to differentiate between two groups of users identified on the basis of their performance for a spatial task. Time series analysis of hidden node activation values and input vectors suggested that certain hidden units become specialised for place and heading, respectively. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction applications are discussed
Connectionist Temporal Modeling for Weakly Supervised Action Labeling
We propose a weakly-supervised framework for action labeling in video, where
only the order of occurring actions is required during training time. The key
challenge is that the per-frame alignments between the input (video) and label
(action) sequences are unknown during training. We address this by introducing
the Extended Connectionist Temporal Classification (ECTC) framework to
efficiently evaluate all possible alignments via dynamic programming and
explicitly enforce their consistency with frame-to-frame visual similarities.
This protects the model from distractions of visually inconsistent or
degenerated alignments without the need of temporal supervision. We further
extend our framework to the semi-supervised case when a few frames are sparsely
annotated in a video. With less than 1% of labeled frames per video, our method
is able to outperform existing semi-supervised approaches and achieve
comparable performance to that of fully supervised approaches.Comment: To appear in ECCV 201
A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems
Decision-making is a process of choosing among alternative courses of action
for solving complicated problems where multi-criteria objectives are involved.
The past few years have witnessed a growing recognition of Soft Computing
technologies that underlie the conception, design and utilization of
intelligent systems. Several works have been done where engineers and
scientists have applied intelligent techniques and heuristics to obtain optimal
decisions from imprecise information. In this paper, we present a concurrent
fuzzy-neural network approach combining unsupervised and supervised learning
techniques to develop the Tactical Air Combat Decision Support System (TACDSS).
Experiment results clearly demonstrate the efficiency of the proposed
technique
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization
An efficient algorithm for recurrent neural network training is presented.
The approach increases the training speed for tasks where a length of the input
sequence may vary significantly. The proposed approach is based on the optimal
batch bucketing by input sequence length and data parallelization on multiple
graphical processing units. The baseline training performance without sequence
bucketing is compared with the proposed solution for a different number of
buckets. An example is given for the online handwriting recognition task using
an LSTM recurrent neural network. The evaluation is performed in terms of the
wall clock time, number of epochs, and validation loss value.Comment: 4 pages, 5 figures, Comments, 2016 IEEE First International
Conference on Data Stream Mining & Processing (DSMP), Lviv, 201
The Many Functions of Discourse Particles: A Computational Model of Pragmatic Interpretation
We present a connectionist model for the interpretation of discourse\ud
particles in real dialogues that is based on neuronal\ud
principles of categorization (categorical perception, prototype\ud
formation, contextual interpretation). It can be shown that\ud
discourse particles operate just like other morphological and\ud
lexical items with respect to interpretation processes. The description\ud
proposed locates discourse particles in an elaborate\ud
model of communication which incorporates many different\ud
aspects of the communicative situation. We therefore also\ud
attempt to explore the content of the category discourse particle.\ud
We present a detailed analysis of the meaning assignment\ud
problem and show that 80%– 90% correctness for unseen discourse\ud
particles can be reached with the feature analysis provided.\ud
Furthermore, we show that ‘analogical transfer’ from\ud
one discourse particle to another is facilitated if prototypes\ud
are computed and used as the basis for generalization. We\ud
conclude that the interpretation processes which are a part of\ud
the human cognitive system are very similar with respect to\ud
different linguistic items. However, the analysis of discourse\ud
particles shows clearly that any explanatory theory of language\ud
needs to incorporate a theory of communication processes
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