64,448 research outputs found
Observational Data-Driven Modeling and Optimization of Manufacturing Processes
The dramatic increase of observational data across industries provides
unparalleled opportunities for data-driven decision making and management,
including the manufacturing industry. In the context of production, data-driven
approaches can exploit observational data to model, control and improve the
process performance. When supplied by observational data with adequate coverage
to inform the true process performance dynamics, they can overcome the cost
associated with intrusive controlled designed experiments and can be applied
for both monitoring and improving process quality. We propose a novel
integrated approach that uses observational data for process parameter design
while simultaneously identifying the significant control variables. We evaluate
our method using simulated experiments and also apply it to a real-world case
setting from a tire manufacturing company
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective
Recent advances in the field of network embedding have shown the
low-dimensional network representation is playing a critical role in network
analysis. However, most of the existing principles of network embedding do not
incorporate auxiliary information such as content and labels of nodes flexibly.
In this paper, we take a matrix factorization perspective of network embedding,
and incorporate structure, content and label information of the network
simultaneously. For structure, we validate that the matrix we construct
preserves high-order proximities of the network. Label information can be
further integrated into the matrix via the process of random walk sampling to
enhance the quality of embedding in an unsupervised manner, i.e., without
leveraging downstream classifiers. In addition, we generalize the Skip-Gram
Negative Sampling model to integrate the content of the network in a matrix
factorization framework. As a consequence, network embedding can be learned in
a unified framework integrating network structure and node content as well as
label information simultaneously. We demonstrate the efficacy of the proposed
model with the tasks of semi-supervised node classification and link prediction
on a variety of real-world benchmark network datasets.Comment: DASFAA 201
Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation
Accurate relative pose is one of the key components in visual odometry (VO)
and simultaneous localization and mapping (SLAM). Recently, the self-supervised
learning framework that jointly optimizes the relative pose and target image
depth has attracted the attention of the community. Previous works rely on the
photometric error generated from depths and poses between adjacent frames,
which contains large systematic error under realistic scenes due to reflective
surfaces and occlusions. In this paper, we bridge the gap between geometric
loss and photometric loss by introducing the matching loss constrained by
epipolar geometry in a self-supervised framework. Evaluated on the KITTI
dataset, our method outperforms the state-of-the-art unsupervised ego-motion
estimation methods by a large margin. The code and data are available at
https://github.com/hlzz/DeepMatchVO.Comment: Accepted by ICRA 201
Learning to Design Circuits
Analog IC design relies on human experts to search for parameters that
satisfy circuit specifications with their experience and intuitions, which is
highly labor intensive, time consuming and suboptimal. Machine learning is a
promising tool to automate this process. However, supervised learning is
difficult for this task due to the low availability of training data: 1)
Circuit simulation is slow, thus generating large-scale dataset is
time-consuming; 2) Most circuit designs are propitiatory IPs within individual
IC companies, making it expensive to collect large-scale datasets. We propose
Learning to Design Circuits (L2DC) to leverage reinforcement learning that
learns to efficiently generate new circuits data and to optimize circuits. We
fix the schematic, and optimize the parameters of the transistors automatically
by training an RL agent with no prior knowledge about optimizing circuits.
After iteratively getting observations, generating a new set of transistor
parameters, getting a reward, and adjusting the model, L2DC is able to optimize
circuits. We evaluate L2DC on two transimpedance amplifiers. Trained for a day,
our RL agent can achieve comparable or better performance than human experts
trained for a quarter. It first learns to meet hard-constraints (eg. gain,
bandwidth), and then learns to optimize good-to-have targets (eg. area, power).
Compared with grid search-aided human design, L2DC can achieve
higher sample efficiency with comparable
performance. Under the same runtime constraint, the performance of L2DC is also
better than Bayesian Optimization.Comment: NeurIPS 2018 Workshop on Machine Learning for System
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Machine learning for cognitive networks : technology assessment and research challenges
The field of machine learning has made major strides over the last 20 years. This document summarizes the major problem formulations that the discipline has studied, then reviews three tasks in cognitive networking and briefly discusses how aspects of those tasks fit these formulations. After this, it discusses challenges for machine learning research raised by Knowledge Plane applications and closes with proposals for the evaluation of learning systems developed for these problems
Hand Pose Estimation through Semi-Supervised and Weakly-Supervised Learning
We propose a method for hand pose estimation based on a deep regressor
trained on two different kinds of input. Raw depth data is fused with an
intermediate representation in the form of a segmentation of the hand into
parts. This intermediate representation contains important topological
information and provides useful cues for reasoning about joint locations. The
mapping from raw depth to segmentation maps is learned in a
semi/weakly-supervised way from two different datasets: (i) a synthetic dataset
created through a rendering pipeline including densely labeled ground truth
(pixelwise segmentations); and (ii) a dataset with real images for which ground
truth joint positions are available, but not dense segmentations. Loss for
training on real images is generated from a patch-wise restoration process,
which aligns tentative segmentation maps with a large dictionary of synthetic
poses. The underlying premise is that the domain shift between synthetic and
real data is smaller in the intermediate representation, where labels carry
geometric and topological meaning, than in the raw input domain. Experiments on
the NYU dataset show that the proposed training method decreases error on
joints over direct regression of joints from depth data by 15.7%.Comment: 13 pages, 10 figures, 4 table
Semantic Hierarchical Priors for Intrinsic Image Decomposition
Intrinsic Image Decomposition (IID) is a challenging and interesting computer
vision problem with various applications in several fields. We present novel
semantic priors and an integrated approach for single image IID that involves
analyzing image at three hierarchical context levels. Local context priors
capture scene properties at each pixel within a small neighbourhood. Mid-level
context priors encode object level semantics. Global context priors establish
correspondences at the scene level. Our semantic priors are designed on both
fixed and flexible regions, using selective search method and Convolutional
Neural Network features. Our IID method is an iterative multistage optimization
scheme and consists of two complementary formulations: smoothing for
shading and sparsity for reflectance. Experiments and analysis of our
method indicate the utility of our semantic priors and structured hierarchical
analysis in an IID framework. We compare our method with other contemporary IID
solutions and show results with lesser artifacts. Finally, we highlight that
proper choice and encoding of prior knowledge can produce competitive results
even when compared to end-to-end deep learning IID methods, signifying the
importance of such priors. We believe that the insights and techniques
presented in this paper would be useful in the future IID research
Unseen Object Segmentation in Videos via Transferable Representations
In order to learn object segmentation models in videos, conventional methods
require a large amount of pixel-wise ground truth annotations. However,
collecting such supervised data is time-consuming and labor-intensive. In this
paper, we exploit existing annotations in source images and transfer such
visual information to segment videos with unseen object categories. Without
using any annotations in the target video, we propose a method to jointly mine
useful segments and learn feature representations that better adapt to the
target frames. The entire process is decomposed into two tasks: 1) solving a
submodular function for selecting object-like segments, and 2) learning a CNN
model with a transferable module for adapting seen categories in the source
domain to the unseen target video. We present an iterative update scheme
between two tasks to self-learn the final solution for object segmentation.
Experimental results on numerous benchmark datasets show that the proposed
method performs favorably against the state-of-the-art algorithms.Comment: Accepted in ACCV'18 (oral). Code is available at
https://github.com/wenz116/TransferSe
Discriminative Similarity for Clustering and Semi-Supervised Learning
Similarity-based clustering and semi-supervised learning methods separate the
data into clusters or classes according to the pairwise similarity between the
data, and the pairwise similarity is crucial for their performance. In this
paper, we propose a novel discriminative similarity learning framework which
learns discriminative similarity for either data clustering or semi-supervised
learning. The proposed framework learns classifier from each hypothetical
labeling, and searches for the optimal labeling by minimizing the
generalization error of the learned classifiers associated with the
hypothetical labeling. Kernel classifier is employed in our framework. By
generalization analysis via Rademacher complexity, the generalization error
bound for the kernel classifier learned from hypothetical labeling is expressed
as the sum of pairwise similarity between the data from different classes,
parameterized by the weights of the kernel classifier. Such pairwise similarity
serves as the discriminative similarity for the purpose of clustering and
semi-supervised learning, and discriminative similarity with similar form can
also be induced by the integrated squared error bound for kernel density
classification. Based on the discriminative similarity induced by the kernel
classifier, we propose new clustering and semi-supervised learning methods
Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
In unsupervised learning, there is no apparent straightforward cost function
that can capture the significant factors of variations and similarities. Since
natural systems have smooth dynamics, an opportunity is lost if an unsupervised
objective function remains static during the training process. The absence of
concrete supervision suggests that smooth dynamics should be integrated.
Compared to classical static cost functions, dynamic objective functions allow
to better make use of the gradual and uncertain knowledge acquired through
pseudo-supervision. In this paper, we propose Dynamic Autoencoder (DynAE), a
novel model for deep clustering that overcomes a clustering-reconstruction
trade-off, by gradually and smoothly eliminating the reconstruction objective
function in favor of a construction one. Experimental evaluations on benchmark
datasets show that our approach achieves state-of-the-art results compared to
the most relevant deep clustering methods
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