3,703 research outputs found
Supervised Dictionary Learning
It is now well established that sparse signal models are well suited to
restoration tasks and can effectively be learned from audio, image, and video
data. Recent research has been aimed at learning discriminative sparse models
instead of purely reconstructive ones. This paper proposes a new step in that
direction, with a novel sparse representation for signals belonging to
different classes in terms of a shared dictionary and multiple class-decision
functions. The linear variant of the proposed model admits a simple
probabilistic interpretation, while its most general variant admits an
interpretation in terms of kernels. An optimization framework for learning all
the components of the proposed model is presented, along with experimental
results on standard handwritten digit and texture classification tasks
Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling
Conventional methods of estimating latent behaviour generally use attitudinal
questions which are subjective and these survey questions may not always be
available. We hypothesize that an alternative approach can be used for latent
variable estimation through an undirected graphical models. For instance,
non-parametric artificial neural networks. In this study, we explore the use of
generative non-parametric modelling methods to estimate latent variables from
prior choice distribution without the conventional use of measurement
indicators. A restricted Boltzmann machine is used to represent latent
behaviour factors by analyzing the relationship information between the
observed choices and explanatory variables. The algorithm is adapted for latent
behaviour analysis in discrete choice scenario and we use a graphical approach
to evaluate and understand the semantic meaning from estimated parameter vector
values. We illustrate our methodology on a financial instrument choice dataset
and perform statistical analysis on parameter sensitivity and stability. Our
findings show that through non-parametric statistical tests, we can extract
useful latent information on the behaviour of latent constructs through machine
learning methods and present strong and significant influence on the choice
process. Furthermore, our modelling framework shows robustness in input
variability through sampling and validation
Hybrid Predictive Coding: Inferring, Fast and Slow
Predictive coding is an influential model of cortical neural activity. It
proposes that perceptual beliefs are furnished by sequentially minimising
"prediction errors" - the differences between predicted and observed data.
Implicit in this proposal is the idea that perception requires multiple cycles
of neural activity. This is at odds with evidence that several aspects of
visual perception - including complex forms of object recognition - arise from
an initial "feedforward sweep" that occurs on fast timescales which preclude
substantial recurrent activity. Here, we propose that the feedforward sweep can
be understood as performing amortized inference and recurrent processing can be
understood as performing iterative inference. We propose a hybrid predictive
coding network that combines both iterative and amortized inference in a
principled manner by describing both in terms of a dual optimization of a
single objective function. We show that the resulting scheme can be implemented
in a biologically plausible neural architecture that approximates Bayesian
inference utilising local Hebbian update rules. We demonstrate that our hybrid
predictive coding model combines the benefits of both amortized and iterative
inference -- obtaining rapid and computationally cheap perceptual inference for
familiar data while maintaining the context-sensitivity, precision, and sample
efficiency of iterative inference schemes. Moreover, we show how our model is
inherently sensitive to its uncertainty and adaptively balances iterative and
amortized inference to obtain accurate beliefs using minimum computational
expense. Hybrid predictive coding offers a new perspective on the functional
relevance of the feedforward and recurrent activity observed during visual
perception and offers novel insights into distinct aspects of visual
phenomenology.Comment: 05/04/22 initial upload. 06/04/22 added acknowledgements sectio
Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model
Real-time marker-less hand tracking is of increasing importance in
human-computer interaction. Robust and accurate tracking of arbitrary hand
motion is a challenging problem due to the many degrees of freedom, frequent
self-occlusions, fast motions, and uniform skin color. In this paper, we
propose a new approach that tracks the full skeleton motion of the hand from
multiple RGB cameras in real-time. The main contributions include a new
generative tracking method which employs an implicit hand shape representation
based on Sum of Anisotropic Gaussians (SAG), and a pose fitting energy that is
smooth and analytically differentiable making fast gradient based pose
optimization possible. This shape representation, together with a full
perspective projection model, enables more accurate hand modeling than a
related baseline method from literature. Our method achieves better accuracy
than previous methods and runs at 25 fps. We show these improvements both
qualitatively and quantitatively on publicly available datasets.Comment: 8 pages, Accepted version of paper published at 3DV 201
- …