100 research outputs found
HyperVAE: A Minimum Description Length Variational Hyper-Encoding Network
We propose a framework called HyperVAE for encoding distributions of
distributions. When a target distribution is modeled by a VAE, its neural
network parameters \theta is drawn from a distribution p(\theta) which is
modeled by a hyper-level VAE. We propose a variational inference using Gaussian
mixture models to implicitly encode the parameters \theta into a low
dimensional Gaussian distribution. Given a target distribution, we predict the
posterior distribution of the latent code, then use a matrix-network decoder to
generate a posterior distribution q(\theta). HyperVAE can encode the parameters
\theta in full in contrast to common hyper-networks practices, which generate
only the scale and bias vectors as target-network parameters. Thus HyperVAE
preserves much more information about the model for each task in the latent
space. We discuss HyperVAE using the minimum description length (MDL) principle
and show that it helps HyperVAE to generalize. We evaluate HyperVAE in density
estimation tasks, outlier detection and discovery of novel design classes,
demonstrating its efficacy
Recognising faces in unseen modes: a tensor based approach
This paper addresses the limitation of current multilinear techniques (multilinear PCA, multilinear ICA) when applied to face recognition for handling faces in unseen illumination and viewpoints. We propose a new recognition method, exploiting the interaction of all the subspaces resulting from multilinear decomposition (for both multilinear PCA and ICA), to produce a new basis called multilinear-eigenmodes. This basis offers the flexibility to handle face images at unseen illumination or viewpoints. Experiments on benchmarked datasets yield superior performance in terms of both accuracy and computational cost
EMOTE: An Explainable architecture for Modelling the Other Through Empathy
We can usually assume others have goals analogous to our own. This assumption
can also, at times, be applied to multi-agent games - e.g. Agent 1's attraction
to green pellets is analogous to Agent 2's attraction to red pellets. This
"analogy" assumption is tied closely to the cognitive process known as empathy.
Inspired by empathy, we design a simple and explainable architecture to model
another agent's action-value function. This involves learning an "Imagination
Network" to transform the other agent's observed state in order to produce a
human-interpretable "empathetic state" which, when presented to the learning
agent, produces behaviours that mimic the other agent. Our approach is
applicable to multi-agent scenarios consisting of a single learning agent and
other (independent) agents acting according to fixed policies. This
architecture is particularly beneficial for (but not limited to) algorithms
using a composite value or reward function. We show our method produces better
performance in multi-agent games, where it robustly estimates the other's model
in different environment configurations. Additionally, we show that the
empathetic states are human interpretable, and thus verifiable
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