313 research outputs found
A polar prediction model for learning to represent visual transformations
All organisms make temporal predictions, and their evolutionary fitness level
depends on the accuracy of these predictions. In the context of visual
perception, the motions of both the observer and objects in the scene structure
the dynamics of sensory signals, allowing for partial prediction of future
signals based on past ones. Here, we propose a self-supervised
representation-learning framework that extracts and exploits the regularities
of natural videos to compute accurate predictions. We motivate the polar
architecture by appealing to the Fourier shift theorem and its group-theoretic
generalization, and we optimize its parameters on next-frame prediction.
Through controlled experiments, we demonstrate that this approach can discover
the representation of simple transformation groups acting in data. When trained
on natural video datasets, our framework achieves better prediction performance
than traditional motion compensation and rivals conventional deep networks,
while maintaining interpretability and speed. Furthermore, the polar
computations can be restructured into components resembling normalized simple
and direction-selective complex cell models of primate V1 neurons. Thus, polar
prediction offers a principled framework for understanding how the visual
system represents sensory inputs in a form that simplifies temporal prediction
Long-term Forecasting using Tensor-Train RNNs
We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics. Long-term forecasting in such systems is highly challenging, since there exist long-term temporal dependencies, higher-order correlations and sensitivity to error propagation. Our proposed tensor recurrent architecture addresses these issues by learning the nonlinear dynamics directly using higher order moments and high-order state transition functions. Furthermore, we decompose the higher-order structure using the tensor-train (TT) decomposition to reduce the number of parameters while preserving the model performance. We theoretically establish the approximation properties of Tensor-Train RNNs for general sequence inputs, and such guarantees are not available for usual RNNs. We also demonstrate significant long-term prediction improvements over general RNN and LSTM architectures on a range of simulated environments with nonlinear dynamics, as well on real-world climate and traffic data
A Review of Physical Human Activity Recognition Chain Using Sensors
In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.
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