9 research outputs found
PMLR
We propose a neural information processing system obtained by re-purposing the function of a biological neural circuit model to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce ordinary neural circuits (ONCs), defined as the model of biological neural circuits reparameterized for the control of alternative tasks. We first demonstrate that ONCs realize networks with higher maximum flow compared to arbitrary wired networks. We then learn instances of ONCs to control a series of robotic tasks, including the autonomous parking of a real-world rover robot. For reconfiguration of the purpose of the neural circuit, we adopt a search-based optimization algorithm. Ordinary neural circuits perform on par and, in some cases, significantly surpass the performance of contemporary deep learning models. ONC networks are compact, 77% sparser than their counterpart neural controllers, and their neural dynamics are fully interpretable at the cell-level
Liquid Time-constant Networks
We introduce a new class of time-continuous recurrent neural network models.
Instead of declaring a learning system's dynamics by implicit nonlinearities,
we construct networks of linear first-order dynamical systems modulated via
nonlinear interlinked gates. The resulting models represent dynamical systems
with varying (i.e., liquid) time-constants coupled to their hidden state, with
outputs being computed by numerical differential equation solvers. These neural
networks exhibit stable and bounded behavior, yield superior expressivity
within the family of neural ordinary differential equations, and give rise to
improved performance on time-series prediction tasks. To demonstrate these
properties, we first take a theoretical approach to find bounds over their
dynamics and compute their expressive power by the trajectory length measure in
latent trajectory space. We then conduct a series of time-series prediction
experiments to manifest the approximation capability of Liquid Time-Constant
Networks (LTCs) compared to classical and modern RNNs. Code and data are
available at https://github.com/raminmh/liquid_time_constant_networksComment: Accepted to the Thirty-Fifth AAAI Conference on Artificial
Intelligence (AAAI-21
International Navigation Market
Economic record of human society in the last period has involved an unprecedented growth of world trade, trafficking of basic raw materials needed for industry and agriculture, and trade in industrial products or food. To the huge volume of movement of goods, shipping takes back the role of first order both quantitatively as well as efficiency. This situation is encouraged by factors
such as diversification of trade, number of participants in this process and the increasingly complex international trade
International Navigation Market
Economic record of human society in the last period has involved an unprecedented growth of world trade, trafficking of basic raw materials needed for industry and agriculture, and trade in industrial products or food. To the huge volume of movement of goods, shipping takes back the role of first order both quantitatively as well as efficiency. This situation is encouraged by factors
such as diversification of trade, number of participants in this process and the increasingly complex international trade