152 research outputs found
Using the online cross-entropy method to learn relational policies for playing different games
By defining a video-game environment as a collection of objects, relations, actions and rewards, the relational reinforcement learning algorithm presented in this paper generates and optimises a set of concise, human-readable relational rules for achieving maximal reward. Rule learning is achieved using a combination of incremental specialisation of rules and a modified online cross-entropy method, which dynamically adjusts the rate of learning as the agent progresses. The algorithm is tested on the Ms. Pac-Man and Mario environments, with results indicating the agent learns an effective policy for acting within each environment
Workshop on Rich Representations for Reinforcement Learning:Held in conjunction with the 22nd International Conference on Machine Learning, August 7, 2005, Bonn, Germany
Contextual Encoder-Decoder Network for Visual Saliency Prediction
Predicting salient regions in natural images requires the detection of
objects that are present in a scene. To develop robust representations for this
challenging task, high-level visual features at multiple spatial scales must be
extracted and augmented with contextual information. However, existing models
aimed at explaining human fixation maps do not incorporate such a mechanism
explicitly. Here we propose an approach based on a convolutional neural network
pre-trained on a large-scale image classification task. The architecture forms
an encoder-decoder structure and includes a module with multiple convolutional
layers at different dilation rates to capture multi-scale features in parallel.
Moreover, we combine the resulting representations with global scene
information for accurately predicting visual saliency. Our model achieves
competitive and consistent results across multiple evaluation metrics on two
public saliency benchmarks and we demonstrate the effectiveness of the
suggested approach on five datasets and selected examples. Compared to state of
the art approaches, the network is based on a lightweight image classification
backbone and hence presents a suitable choice for applications with limited
computational resources, such as (virtual) robotic systems, to estimate human
fixations across complex natural scenes.Comment: Accepted Manuscrip
Searching for ring-like structures in the Cosmic Microwave Background
In this research we present a new methodology to search for ring-like structures in the CMB. The particular context of this work is to investigate the presence of possible observational effects associated with Conformal Cyclic Cosmology (CCC), known as Hawking points. Although our results are not conclusive due to the statistical disagreement between the CMB sky map and the simulated sky maps in accordance to , we are able to retrieve ring-like anomalies from an artificial data at confidence level. Once this discrepancy has been assessed, our method may be able to provide evidence of the presence or absence of Hawking points in the CMB. Hence, we stress the need to continue the theoretical and experimental research in this direction
Learning Structural Kernels for Natural Language Processing
Structural kernels are a flexible learning
paradigm that has been widely used in Natural
Language Processing. However, the problem
of model selection in kernel-based methods
is usually overlooked. Previous approaches
mostly rely on setting default values for kernel
hyperparameters or using grid search,
which is slow and coarse-grained. In contrast,
Bayesian methods allow efficient model
selection by maximizing the evidence on the
training data through gradient-based methods.
In this paper we show how to perform this
in the context of structural kernels by using
Gaussian Processes. Experimental results on
tree kernels show that this procedure results
in better prediction performance compared to
hyperparameter optimization via grid search.
The framework proposed in this paper can be
adapted to other structures besides trees, e.g.,
strings and graphs, thereby extending the utility
of kernel-based methods
Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data
The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains.</p
Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data
The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains.</p
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