67 research outputs found
Power packet transferability via symbol propagation matrix
Power packet is a unit of electric power transferred by a power pulse with an
information tag. In Shannon's information theory, messages are represented by
symbol sequences in a digitized manner. Referring to this formulation, we
define symbols in power packetization as a minimum unit of power transferred by
a tagged pulse. Here, power is digitized and quantized. In this paper, we
consider packetized power in networks for a finite duration, giving symbols and
their energies to the networks. A network structure is defined using a graph
whose nodes represent routers, sources, and destinations. First, we introduce
symbol propagation matrix (SPM) in which symbols are transferred at links
during unit times. Packetized power is described as a network flow in a
spatio-temporal structure. Then, we study the problem of selecting an SPM in
terms of transferability, that is, the possibility to represent given energies
at sources and destinations during the finite duration. To select an SPM, we
consider a network flow problem of packetized power. The problem is formulated
as an M-convex submodular flow problem which is known as generalization of the
minimum cost flow problem and solvable. Finally, through examples, we verify
that this formulation provides reasonable packetized power.Comment: Submitted to Proceedings of the Royal Society A: Mathematical,
Physical and Engineering Science
Towards a Unified View of Affinity-Based Knowledge Distillation
Knowledge transfer between artificial neural networks has become an important
topic in deep learning. Among the open questions are what kind of knowledge
needs to be preserved for the transfer, and how it can be effectively achieved.
Several recent work have shown good performance of distillation methods using
relation-based knowledge. These algorithms are extremely attractive in that
they are based on simple inter-sample similarities. Nevertheless, a proper
metric of affinity and use of it in this context is far from well understood.
In this paper, by explicitly modularising knowledge distillation into a
framework of three components, i.e. affinity, normalisation, and loss, we give
a unified treatment of these algorithms as well as study a number of unexplored
combinations of the modules. With this framework we perform extensive
evaluations of numerous distillation objectives for image classification, and
obtain a few useful insights for effective design choices while demonstrating
how relation-based knowledge distillation could achieve comparable performance
to the state of the art in spite of the simplicity
Deep Predictive Policy Training using Reinforcement Learning
Skilled robot task learning is best implemented by predictive action policies
due to the inherent latency of sensorimotor processes. However, training such
predictive policies is challenging as it involves finding a trajectory of motor
activations for the full duration of the action. We propose a data-efficient
deep predictive policy training (DPPT) framework with a deep neural network
policy architecture which maps an image observation to a sequence of motor
activations. The architecture consists of three sub-networks referred to as the
perception, policy and behavior super-layers. The perception and behavior
super-layers force an abstraction of visual and motor data trained with
synthetic and simulated training samples, respectively. The policy super-layer
is a small sub-network with fewer parameters that maps data in-between the
abstracted manifolds. It is trained for each task using methods for policy
search reinforcement learning. We demonstrate the suitability of the proposed
architecture and learning framework by training predictive policies for skilled
object grasping and ball throwing on a PR2 robot. The effectiveness of the
method is illustrated by the fact that these tasks are trained using only about
180 real robot attempts with qualitative terminal rewards.Comment: This work is submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems 2017 (IROS2017
Marginal Thresholding in Noisy Image Segmentation
This work presents a study on label noise in medical image segmentation by
considering a noise model based on Gaussian field deformations. Such noise is
of interest because it yields realistic looking segmentations and because it is
unbiased in the sense that the expected deformation is the identity mapping.
Efficient methods for sampling and closed form solutions for the marginal
probabilities are provided. Moreover, theoretically optimal solutions to the
loss functions cross-entropy and soft-Dice are studied and it is shown how they
diverge as the level of noise increases. Based on recent work on loss function
characterization, it is shown that optimal solutions to soft-Dice can be
recovered by thresholding solutions to cross-entropy with a particular a priori
unknown threshold that efficiently can be computed. This raises the question
whether the decrease in performance seen when using cross-entropy as compared
to soft-Dice is caused by using the wrong threshold. The hypothesis is
validated in 5-fold studies on three organ segmentation problems from the
TotalSegmentor data set, using 4 different strengths of noise. The results show
that changing the threshold leads the performance of cross-entropy to go from
systematically worse than soft-Dice to similar or better results than
soft-Dice
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