519 research outputs found
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
In applications of Gaussian processes where quantification of uncertainty is
of primary interest, it is necessary to accurately characterize the posterior
distribution over covariance parameters. This paper proposes an adaptation of
the Stochastic Gradient Langevin Dynamics algorithm to draw samples from the
posterior distribution over covariance parameters with negligible bias and
without the need to compute the marginal likelihood. In Gaussian process
regression, this has the enormous advantage that stochastic gradients can be
computed by solving linear systems only. A novel unbiased linear systems solver
based on parallelizable covariance matrix-vector products is developed to
accelerate the unbiased estimation of gradients. The results demonstrate the
possibility to enable scalable and exact (in a Monte Carlo sense)
quantification of uncertainty in Gaussian processes without imposing any
special structure on the covariance or reducing the number of input vectors.Comment: 10 pages - paper accepted at ICML 201
Better Exploration with Optimistic Actor-Critic
Actor-critic methods, a type of model-free Reinforcement Learning, have been
successfully applied to challenging tasks in continuous control, often
achieving state-of-the art performance. However, wide-scale adoption of these
methods in real-world domains is made difficult by their poor sample
efficiency. We address this problem both theoretically and empirically. On the
theoretical side, we identify two phenomena preventing efficient exploration in
existing state-of-the-art algorithms such as Soft Actor Critic. First,
combining a greedy actor update with a pessimistic estimate of the critic leads
to the avoidance of actions that the agent does not know about, a phenomenon we
call pessimistic underexploration. Second, current algorithms are directionally
uninformed, sampling actions with equal probability in opposite directions from
the current mean. This is wasteful, since we typically need actions taken along
certain directions much more than others. To address both of these phenomena,
we introduce a new algorithm, Optimistic Actor Critic, which approximates a
lower and upper confidence bound on the state-action value function. This
allows us to apply the principle of optimism in the face of uncertainty to
perform directed exploration using the upper bound while still using the lower
bound to avoid overestimation. We evaluate OAC in several challenging
continuous control tasks, achieving state-of the art sample efficiency.Comment: 20 pages (including supplement
Weight Compander: A Simple Weight Reparameterization for Regularization
Regularization is a set of techniques that are used to improve the
generalization ability of deep neural networks. In this paper, we introduce
weight compander (WC), a novel effective method to improve generalization by
reparameterizing each weight in deep neural networks using a nonlinear
function. It is a general, intuitive, cheap and easy to implement method, which
can be combined with various other regularization techniques. Large weights in
deep neural networks are a sign of a more complex network that is overfitted to
the training data. Moreover, regularized networks tend to have a greater range
of weights around zero with fewer weights centered at zero. We introduce a
weight reparameterization function which is applied to each weight and
implicitly reduces overfitting by restricting the magnitude of the weights
while forcing them away from zero at the same time. This leads to a more
democratic decision-making in the network. Firstly, individual weights cannot
have too much influence in the prediction process due to the restriction of
their magnitude. Secondly, more weights are used in the prediction process,
since they are forced away from zero during the training. This promotes the
extraction of more features from the input data and increases the level of
weight redundancy, which makes the network less sensitive to statistical
differences between training and test data. We extend our method to learn the
hyperparameters of the introduced weight reparameterization function. This
avoids hyperparameter search and gives the network the opportunity to align the
weight reparameterization with the training progress. We show experimentally
that using weight compander in addition to standard regularization methods
improves the performance of neural networks.Comment: Accepted by The International Joint Conference on Neural Network
(IJCNN) 202
Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes
I argue that data becomes temporarily interesting by itself to some
self-improving, but computationally limited, subjective observer once he learns
to predict or compress the data in a better way, thus making it subjectively
simpler and more beautiful. Curiosity is the desire to create or discover more
non-random, non-arbitrary, regular data that is novel and surprising not in the
traditional sense of Boltzmann and Shannon but in the sense that it allows for
compression progress because its regularity was not yet known. This drive
maximizes interestingness, the first derivative of subjective beauty or
compressibility, that is, the steepness of the learning curve. It motivates
exploring infants, pure mathematicians, composers, artists, dancers, comedians,
yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007
joint invited lectur
Neural Network Approaches to Medical Toponym Recognition
Toponym identification, or place name recognition, within epidemiology articles is a crucial task for phylogeographers, as it allows them to analyze the development, spread, and migration of viruses. Although, public databases, such as GenBank (Benson et al., November 2012), contain the geographical information, this information is typically restricted to country and state levels. In order to identify more fine-grained localization information, epidemiologists need to read relevant scientific articles and manually extract place name mentions.
In this thesis, we investigate the use of various neural network architectures and language representations to automatically segment and label toponyms within biomedical texts. We demonstrate how our language model based toponym recognizer relying on transformer architecture can achieve state-of-the-art performance. This model uses pre-trained BERT as the backbone and fine tunes on two domains of datasets (general articles and medical articles) in order to measure the generalizability of the approach and cross-domain transfer learning.
Using BERT as the backbone of the model, resulted in a large highly parameterized model (340M parameters). In order to obtain a light model architecture we experimented with parameter pruning techniques, specifically we experimented with Lottery Ticket Hypothesis (Frankle and Carbin, May 2019) (LTH), however as indicated by Frankle and Carbin (May 2019), their pruning technique does not scale well to highly parametrized models and loses stability. We proposed a novel technique to augment LTH in order to increase the scalability and stability of this technique to highly parametrized models such as BERT and tested our technique on toponym identification task.
The evaluation of the model was performed using a collection of 105 epidemiology articles from PubMed Central (Weissenbacher et al., June 2015). Our proposed model significantly improves the state-of-the-art model by achieving an F-measure of 90.85% compared to 89.13%
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