1,941 research outputs found
Generating 3D faces using Convolutional Mesh Autoencoders
Learned 3D representations of human faces are useful for computer vision
problems such as 3D face tracking and reconstruction from images, as well as
graphics applications such as character generation and animation. Traditional
models learn a latent representation of a face using linear subspaces or
higher-order tensor generalizations. Due to this linearity, they can not
capture extreme deformations and non-linear expressions. To address this, we
introduce a versatile model that learns a non-linear representation of a face
using spectral convolutions on a mesh surface. We introduce mesh sampling
operations that enable a hierarchical mesh representation that captures
non-linear variations in shape and expression at multiple scales within the
model. In a variational setting, our model samples diverse realistic 3D faces
from a multivariate Gaussian distribution. Our training data consists of 20,466
meshes of extreme expressions captured over 12 different subjects. Despite
limited training data, our trained model outperforms state-of-the-art face
models with 50% lower reconstruction error, while using 75% fewer parameters.
We also show that, replacing the expression space of an existing
state-of-the-art face model with our autoencoder, achieves a lower
reconstruction error. Our data, model and code are available at
http://github.com/anuragranj/com
Quantitative toxicity prediction using topology based multi-task deep neural networks
The understanding of toxicity is of paramount importance to human health and
environmental protection. Quantitative toxicity analysis has become a new
standard in the field. This work introduces element specific persistent
homology (ESPH), an algebraic topology approach, for quantitative toxicity
prediction. ESPH retains crucial chemical information during the topological
abstraction of geometric complexity and provides a representation of small
molecules that cannot be obtained by any other method. To investigate the
representability and predictive power of ESPH for small molecules, ancillary
descriptors have also been developed based on physical models. Topological and
physical descriptors are paired with advanced machine learning algorithms, such
as deep neural network (DNN), random forest (RF) and gradient boosting decision
tree (GBDT), to facilitate their applications to quantitative toxicity
predictions. A topology based multi-task strategy is proposed to take the
advantage of the availability of large data sets while dealing with small data
sets. Four benchmark toxicity data sets that involve quantitative measurements
are used to validate the proposed approaches. Extensive numerical studies
indicate that the proposed topological learning methods are able to outperform
the state-of-the-art methods in the literature for quantitative toxicity
analysis. Our online server for computing element-specific topological
descriptors (ESTDs) is available at http://weilab.math.msu.edu/TopTox/Comment: arXiv admin note: substantial text overlap with arXiv:1703.1095
Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media
Imaging through scattering is an important yet challenging problem. Tremendous progress has been made by exploiting the deterministic input–output “transmission matrix” for a fixed medium. However, this “one-to-one” mapping is highly susceptible to speckle decorrelations – small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical “one-to-all” deep learning (DL) technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable DL approach for imaging through scattering media.National Science Foundation (NSF) (1711156); Directorate for Engineering (ENG). (1711156 - National Science Foundation (NSF); Directorate for Engineering (ENG))First author draf
Facial Point Graphs for Amyotrophic Lateral Sclerosis Identification
Identifying Amyotrophic Lateral Sclerosis (ALS) in its early stages is
essential for establishing the beginning of treatment, enriching the outlook,
and enhancing the overall well-being of those affected individuals. However,
early diagnosis and detecting the disease's signs is not straightforward. A
simpler and cheaper way arises by analyzing the patient's facial expressions
through computational methods. When a patient with ALS engages in specific
actions, e.g., opening their mouth, the movement of specific facial muscles
differs from that observed in a healthy individual. This paper proposes Facial
Point Graphs to learn information from the geometry of facial images to
identify ALS automatically. The experimental outcomes in the Toronto Neuroface
dataset show the proposed approach outperformed state-of-the-art results,
fostering promising developments in the area.Comment: 7 pages and 7 figure
Do Large Scale Molecular Language Representations Capture Important Structural Information?
Predicting the chemical properties of a molecule is of great importance in
many applications, including drug discovery and material design. Machine
learning based molecular property prediction holds the promise of enabling
accurate predictions at much less computationally complex cost when compared
to, for example, Density Functional Theory (DFT) calculations. Various
representation learning methods in a supervised setting, including the features
extracted using graph neural nets, have emerged for such tasks. However, the
vast chemical space and the limited availability of labels make supervised
learning challenging, calling for learning a general-purpose molecular
representation. Recently, pre-trained transformer-based language models on
large unlabeled corpus have produced state-of-the-art results in many
downstream natural language processing tasks. Inspired by this development, we
present molecular embeddings obtained by training an efficient transformer
encoder model, MoLFormer. This model employs a linear attention mechanism
coupled with highly parallelized training on SMILES sequences of 1.1 billion
unlabeled molecules from the PubChem and ZINC datasets. Experiments show that
the learned molecular representation outperforms supervised and unsupervised
graph neural net baselines on several regression and classification tasks from
10 benchmark datasets, while performing competitively on others. Further
analyses, specifically through the lens of attention, demonstrate that
MoLFormer indeed learns a molecule's local and global structural aspects. These
results provide encouraging evidence that large-scale molecular language models
can capture sufficient structural information to be able to predict diverse
molecular properties, including quantum-chemical propertie
Applying Deep Learning to Calibrate Stochastic Volatility Models
Stochastic volatility models, where the volatility is a stochastic process,
can capture most of the essential stylized facts of implied volatility surfaces
and give more realistic dynamics of the volatility smile/skew. However, they
come with the significant issue that they take too long to calibrate.
Alternative calibration methods based on Deep Learning (DL) techniques have
been recently used to build fast and accurate solutions to the calibration
problem. Huge and Savine developed a Differential Machine Learning (DML)
approach, where Machine Learning models are trained on samples of not only
features and labels but also differentials of labels to features. The present
work aims to apply the DML technique to price vanilla European options (i.e.
the calibration instruments), more specifically, puts when the underlying asset
follows a Heston model and then calibrate the model on the trained network. DML
allows for fast training and accurate pricing. The trained neural network
dramatically reduces Heston calibration's computation time.
In this work, we also introduce different regularisation techniques, and we
apply them notably in the case of the DML. We compare their performance in
reducing overfitting and improving the generalisation error. The DML
performance is also compared to the classical DL (without differentiation) one
in the case of Feed-Forward Neural Networks. We show that the DML outperforms
the DL.
The complete code for our experiments is provided in the GitHub repository:
https://github.com/asridi/DML-Calibration-Heston-Mode
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