1,218 research outputs found
Mining gold from implicit models to improve likelihood-free inference
Simulators often provide the best description of real-world phenomena.
However, they also lead to challenging inverse problems because the density
they implicitly define is often intractable. We present a new suite of
simulation-based inference techniques that go beyond the traditional
Approximate Bayesian Computation approach, which struggles in a
high-dimensional setting, and extend methods that use surrogate models based on
neural networks. We show that additional information, such as the joint
likelihood ratio and the joint score, can often be extracted from simulators
and used to augment the training data for these surrogate models. Finally, we
demonstrate that these new techniques are more sample efficient and provide
higher-fidelity inference than traditional methods.Comment: Code available at
https://github.com/johannbrehmer/simulator-mining-example . v2: Fixed typos.
v3: Expanded discussion, added Lotka-Volterra example. v4: Improved clarit
Towards realistic meteorological predictive learning using conditional GAN
Meteorological imagery prediction is an important and challenging problem for weather forecasting. It can also be seen as a video frame prediction problem that estimates future frames based on observed meteorological imageries. Despite it is a widely-investigated problem, it is still far from being solved. Current state-of-the-art deep learning based approaches mainly optimise the mean square error loss resulting in blurry predictions. We address this problem by introducing a Meteorological Predictive Learning GAN model (in short MPL-GAN) that utilises the conditional GAN along with the predictive learning module in order to handle the uncertainty in future frame prediction. Experiments on a real-world dataset demonstrate the superior performance of our proposed model. Our proposed model is able to map the blurry predictions produced by traditional mean square error loss based predictive learning methods back to their original data distributions, hence it is able to improve and sharpen the prediction. In particular, our MPL-GAN achieves an average sharpness of 52.82, which is 14% better than the baseline method. Furthermore, our model correctly detects the meteorological movement patterns that traditional unconditional GANs fail to do
A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories
We propose a unified deep learning framework for generation and analysis of
driving scenario trajectories, and validate its effectiveness in a principled
way. In order to model and generate scenarios of trajectories with different
length, we develop two approaches. First, we adapt the Recurrent Conditional
Generative Adversarial Networks (RC-GAN) by conditioning on the length of the
trajectories. This provides us flexibility to generate variable-length driving
trajectories, a desirable feature for scenario test case generation in the
verification of self-driving cars. Second, we develop an architecture based on
Recurrent Autoencoder with GANs in order to obviate the variable length issue,
wherein we train a GAN to learn/generate the latent representations of original
trajectories. In this approach, we train an integrated feed-forward neural
network to estimate the length of the trajectories to be able to bring them
back from the latent space representation. In addition to trajectory
generation, we employ the trained autoencoder as a feature extractor, for the
purpose of clustering and anomaly detection, in order to obtain further
insights on the collected scenario dataset. We experimentally investigate the
performance of the proposed framework on real-world scenario trajectories
obtained from in-field data collection
DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding
Reducing the radiation dose in computed tomography (CT) is important to
mitigate radiation-induced risks. One option is to employ a well-trained model
to compensate for incomplete information and map sparse-view measurements to
the CT reconstruction. However, reconstruction from sparsely sampled
measurements is insufficient to uniquely characterize an object in CT, and a
learned prior model may be inadequate for unencountered cases. Medical modal
translation from magnetic resonance imaging (MRI) to CT is an alternative but
may introduce incorrect information into the synthesized CT images in addition
to the fact that there exists no explicit transformation describing their
relationship. To address these issues, we propose a novel framework called the
denoising diffusion model for medical image synthesis (DDMM-Synth) to close the
performance gaps described above. This framework combines an MRI-guided
diffusion model with a new CT measurement embedding reverse sampling scheme.
Specifically, the null-space content of the one-step denoising result is
refined by the MRI-guided data distribution prior, and its range-space
component derived from an explicit operator matrix and the sparse-view CT
measurements is directly integrated into the inference stage. DDMM-Synth can
adjust the projection number of CT a posteriori for a particular clinical
application and its modified version can even improve the results significantly
for noisy cases. Our results show that DDMM-Synth outperforms other
state-of-the-art supervised-learning-based baselines under fair experimental
conditions.Comment: llncs.cls v2.20,12 pages with 6 figure
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
To capture the relationship between samples and labels, conditional
generative models often inherit spurious correlations from the training
dataset. This can result in label-conditional distributions that are imbalanced
with respect to another latent attribute. To mitigate this issue, which we call
spurious causality of conditional generation, we propose a general two-step
strategy. (a) Fairness Intervention (FI): emphasize the minority samples that
are hard to generate due to the spurious correlation in the training dataset.
(b) Corrective Sampling (CS): explicitly filter the generated samples and
ensure that they follow the desired latent attribute distribution. We have
designed the fairness intervention to work for various degrees of supervision
on the spurious attribute, including unsupervised, weakly-supervised, and
semi-supervised scenarios. Our experimental results demonstrate that FICS can
effectively resolve spurious causality of conditional generation across various
datasets.Comment: TMLR 202
Can Generative Adversarial Networks Help Us Fight Financial Fraud?
Transactional fraud datasets exhibit extreme class imbalance. Learners cannot make accurate generalizations without sufficient data. Researchers can account for imbalance at the data level, algorithmic level or both. This paper focuses on techniques at the data level. We evaluate the evidence of the optimal technique and potential enhancements. Global fraud losses totalled more than 80 % of the UK’s GDP in 2019. The improvement of preprocessing is inherently valuable in fighting these losses. Synthetic minority oversampling technique (SMOTE) and extensions of SMOTE are currently the most common preprocessing strategies. SMOTE oversamples the minority classes by randomly generating a point between a minority instance and its nearest neighbour. Recent papers adopt generative adversarial networks (GAN) for data synthetic creation. Since 2014 there had been several GAN extensions, from improved training mechanisms to frameworks specifically for tabular data. The primary aim of the research is to understand the benefits of GANs built specifically for tabular data on supervised classifiers performance. We determine if this framework will outperform traditional methods and more common GAN frameworks. Secondly, we propose a framework that allows individuals to test the impact of imbalance ratios on classifier performance. Finally, we investigate the use of clustering and determine if this information can help GANs create better synthetic information. We explore this in the context of commonly used supervised classifiers and ensemble methods
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