49 research outputs found
Autonomous Cleaning of Corrupted Scanned Documents - A Generative Modeling Approach
We study the task of cleaning scanned text documents that are strongly
corrupted by dirt such as manual line strokes, spilled ink etc. We aim at
autonomously removing dirt from a single letter-size page based only on the
information the page contains. Our approach, therefore, has to learn character
representations without supervision and requires a mechanism to distinguish
learned representations from irregular patterns. To learn character
representations, we use a probabilistic generative model parameterizing pattern
features, feature variances, the features' planar arrangements, and pattern
frequencies. The latent variables of the model describe pattern class, pattern
position, and the presence or absence of individual pattern features. The model
parameters are optimized using a novel variational EM approximation. After
learning, the parameters represent, independently of their absolute position,
planar feature arrangements and their variances. A quality measure defined
based on the learned representation then allows for an autonomous
discrimination between regular character patterns and the irregular patterns
making up the dirt. The irregular patterns can thus be removed to clean the
document. For a full Latin alphabet we found that a single page does not
contain sufficiently many character examples. However, even if heavily
corrupted by dirt, we show that a page containing a lower number of character
types can efficiently and autonomously be cleaned solely based on the
structural regularity of the characters it contains. In different examples
using characters from different alphabets, we demonstrate generality of the
approach and discuss its implications for future developments.Comment: oral presentation and Google Student Travel Award; IEEE conference on
Computer Vision and Pattern Recognition 201
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Unsupervised learning on imbalanced data is challenging because, when given
imbalanced data, current model is often dominated by the major category and
ignores the categories with small amount of data. We develop a latent variable
model that can cope with imbalanced data by dividing the latent space into a
shared space and a private space. Based on Gaussian Process Latent Variable
Models, we propose a new kernel formulation that enables the separation of
latent space and derives an efficient variational inference method. The
performance of our model is demonstrated with an imbalanced medical image
dataset.Comment: ICLR 2016 Worksho
Batch Bayesian Optimization via Local Penalization
The popularity of Bayesian optimization methods for efficient exploration of
parameter spaces has lead to a series of papers applying Gaussian processes as
surrogates in the optimization of functions. However, most proposed approaches
only allow the exploration of the parameter space to occur sequentially. Often,
it is desirable to simultaneously propose batches of parameter values to
explore. This is particularly the case when large parallel processing
facilities are available. These facilities could be computational or physical
facets of the process being optimized. E.g. in biological experiments many
experimental set ups allow several samples to be simultaneously processed.
Batch methods, however, require modeling of the interaction between the
evaluations in the batch, which can be expensive in complex scenarios. We
investigate a simple heuristic based on an estimate of the Lipschitz constant
that captures the most important aspect of this interaction (i.e. local
repulsion) at negligible computational overhead. The resulting algorithm
compares well, in running time, with much more elaborate alternatives. The
approach assumes that the function of interest, , is a Lipschitz continuous
function. A wrap-loop around the acquisition function is used to collect
batches of points of certain size minimizing the non-parallelizable
computational effort. The speed-up of our method with respect to previous
approaches is significant in a set of computationally expensive experiments.Comment: 11 pages, 10 figure
In-context Exploration-Exploitation for Reinforcement Learning
In-context learning is a promising approach for online policy learning of
offline reinforcement learning (RL) methods, which can be achieved at inference
time without gradient optimization. However, this method is hindered by
significant computational costs resulting from the gathering of large training
trajectory sets and the need to train large Transformer models. We address this
challenge by introducing an In-context Exploration-Exploitation (ICEE)
algorithm, designed to optimize the efficiency of in-context policy learning.
Unlike existing models, ICEE performs an exploration-exploitation trade-off at
inference time within a Transformer model, without the need for explicit
Bayesian inference. Consequently, ICEE can solve Bayesian optimization problems
as efficiently as Gaussian process biased methods do, but in significantly less
time. Through experiments in grid world environments, we demonstrate that ICEE
can learn to solve new RL tasks using only tens of episodes, marking a
substantial improvement over the hundreds of episodes needed by the previous
in-context learning method.Comment: Published at ICLR 202