118 research outputs found
Second order isomorphism: A reinterpretation and its implications in brain and cognitive sciences
Shepard and Chipman's second order isomorphism describes how
the brain may represent the relations in the world.
However, a common interpretation of the theory can cause difficulties.
The problem originates from the static nature
of representations. In an alternative interpretation, I propose that
we assign an active role to the internal representations and
relations. It turns out that a collection of such active units can
perform analogical tasks. The new interpretation is supported
by the existence of neural circuits that may be implementing such a function.
Within this framework, perception, cognition, and motor function
can be understood under a unifying principle of analogy
Processing of analogy in the thalamocortical circuit
The corticothalamic feedback and the thalamic reticular nucleus have gained
much attention lately because of their integrative and modulatory functions.
A previous study by the author suggested that
this circuitry can process analogies (i.e., the {\em analogy hypothesis}).
In this paper, the proposed model was implemented as a network of leaky
integrate-and-fire neurons to test the {\em analogy hypothesis}.
The previous proposal required specific delay and
temporal dynamics, and the implemented network tuned
accordingly functioned as predicted. Furthermore, these specific
conditions turn out to be consistent with experimental data, suggesting
that a further investigation of the thalamocortical circuit within the {\em
analogical framework} may be worthwhile
Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks
Architecture optimization, which is a technique for finding an efficient
neural network that meets certain requirements, generally reduces to a set of
multiple-choice selection problems among alternative sub-structures or
parameters. The discrete nature of the selection problem, however, makes this
optimization difficult. To tackle this problem we introduce a novel concept of
a trainable gate function. The trainable gate function, which confers a
differentiable property to discretevalued variables, allows us to directly
optimize loss functions that include non-differentiable discrete values such as
0-1 selection. The proposed trainable gate can be applied to pruning. Pruning
can be carried out simply by appending the proposed trainable gate functions to
each intermediate output tensor followed by fine-tuning the overall model,
using any gradient-based training methods. So the proposed method can jointly
optimize the selection of the pruned channels while fine-tuning the weights of
the pruned model at the same time. Our experimental results demonstrate that
the proposed method efficiently optimizes arbitrary neural networks in various
tasks such as image classification, style transfer, optical flow estimation,
and neural machine translation.Comment: Accepted to AAAI 2020 (Poster
Action Recognition and State Change Prediction in a Recipe Understanding Task Using a Lightweight Neural Network Model
Consider a natural language sentence describing a specific step in a food
recipe. In such instructions, recognizing actions (such as press, bake, etc.)
and the resulting changes in the state of the ingredients (shape molded,
custard cooked, temperature hot, etc.) is a challenging task. One way to cope
with this challenge is to explicitly model a simulator module that applies
actions to entities and predicts the resulting outcome (Bosselut et al. 2018).
However, such a model can be unnecessarily complex. In this paper, we propose a
simplified neural network model that separates action recognition and state
change prediction, while coupling the two through a novel loss function. This
allows learning to indirectly influence each other. Our model, although
simpler, achieves higher state change prediction performance (67% average
accuracy for ours vs. 55% in (Bosselut et al. 2018)) and takes fewer samples to
train (10K ours vs. 65K+ by (Bosselut et al. 2018)).Comment: AAAI-2020 Student Abstract and Poster Program (Accept
Dynamical pathway analysis
<p>Abstract</p> <p>Background</p> <p>Although a great deal is known about one gene or protein and its functions under different environmental conditions, little information is available about the complex behaviour of biological networks subject to different environmental perturbations. Observing differential expressions of one or more genes between normal and abnormal cells has been a mainstream method of discovering pertinent genes in diseases and therefore valuable drug targets. However, to date, no such method exists for elucidating and quantifying the differential dynamical behaviour of genetic regulatory networks, which can have greater impact on phenotypes than individual genes.</p> <p>Results</p> <p>We propose to redress the deficiency by formulating the functional study of biological networks as a control problem of dynamical systems. We developed mathematical methods to study the stability, the controllability, and the steady-state behaviour, as well as the transient responses of biological networks under different environmental perturbations. We applied our framework to three real-world datasets: the SOS DNA repair network in <it>E. coli </it>under different dosages of radiation, the GSH redox cycle in mice lung exposed to either poisonous air or normal air, and the MAPK pathway in mammalian cell lines exposed to three types of HIV type I Vpr, a wild type and two mutant types; and we found that the three genetic networks exhibited fundamentally different dynamical properties in normal and abnormal cells.</p> <p>Conclusion</p> <p>Difference in stability, relative stability, degrees of controllability, and transient responses between normal and abnormal cells means considerable difference in dynamical behaviours and different functioning of cells. Therefore differential dynamical properties can be a valuable tool in biomedical research.</p
Comparing Sample-wise Learnability Across Deep Neural Network Models
Estimating the relative importance of each sample in a training set has
important practical and theoretical value, such as in importance sampling or
curriculum learning. This kind of focus on individual samples invokes the
concept of sample-wise learnability: How easy is it to correctly learn each
sample (cf. PAC learnability)? In this paper, we approach the sample-wise
learnability problem within a deep learning context. We propose a measure of
the learnability of a sample with a given deep neural network (DNN) model. The
basic idea is to train the given model on the training set, and for each
sample, aggregate the hits and misses over the entire training epochs. Our
experiments show that the sample-wise learnability measure collected this way
is highly linearly correlated across different DNN models (ResNet-20, VGG-16,
and MobileNet), suggesting that such a measure can provide deep general
insights on the data's properties. We expect our method to help develop better
curricula for training, and help us better understand the data itself.Comment: Accepted to AAAI 2019 Student Abstrac
- β¦