35 research outputs found
Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning
Bayesian network structure learning algorithms with limited data are being
used in domains such as systems biology and neuroscience to gain insight into
the underlying processes that produce observed data. Learning reliable networks
from limited data is difficult, therefore transfer learning can improve the
robustness of learned networks by leveraging data from related tasks. Existing
transfer learning algorithms for Bayesian network structure learning give a
single maximum a posteriori estimate of network models. Yet, many other models
may be equally likely, and so a more informative result is provided by Bayesian
structure discovery. Bayesian structure discovery algorithms estimate posterior
probabilities of structural features, such as edges. We present transfer
learning for Bayesian structure discovery which allows us to explore the shared
and unique structural features among related tasks. Efficient computation
requires that our transfer learning objective factors into local calculations,
which we prove is given by a broad class of transfer biases. Theoretically, we
show the efficiency of our approach. Empirically, we show that compared to
single task learning, transfer learning is better able to positively identify
true edges. We apply the method to whole-brain neuroimaging data.Comment: 10 page
Interactive Exploration of Multitask Dependency Networks
Scientists increasingly depend on machine learning algorithms to discover patterns in complex data. Two examples addressed in this dissertation are identifying how information sharing among regions of the brain develops due to learning; and, learning dependency networks of blood proteins associated with cancer. Dependency networks, or graphical models, are learned from the observed data in order to make comparisons between the sub-populations of the dataset. Rarely is there sufficient data to infer robust individual networks for each sub-population. The multiple networks must be considered simultaneously; exploding the hypothesis space of the learning problem. Exploring this complex solution space requires input from the domain scientist to refine the objective function. This dissertation introduces a framework to incorporate domain knowledge in transfer learning to facilitate the exploration of solutions. The framework is a generalization of existing algorithms for multiple network structure identification. Solutions produced with human input narrow down the variance of solutions to those that answer questions of interest to domain scientists. Patterns, such as identifying differences between networks, are learned with higher confidence using transfer learning than through the standard method of bootstrapping. Transfer learning may be the ideal method for making comparisons among dependency networks, whether looking for similarities or differences. Domain knowledge input and visualization of solutions are combined in an interactive tool that enables domain scientists to explore the space of solutions efficiently
Abstract Images Have Different Levels of Retrievability Per Reverse Image Search Engine
Much computer vision research has focused on natural images, but technical
documents typically consist of abstract images, such as charts, drawings,
diagrams, and schematics. How well do general web search engines discover
abstract images? Recent advancements in computer vision and machine learning
have led to the rise of reverse image search engines. Where conventional search
engines accept a text query and return a set of document results, including
images, a reverse image search accepts an image as a query and returns a set of
images as results. This paper evaluates how well common reverse image search
engines discover abstract images. We conducted an experiment leveraging images
from Wikimedia Commons, a website known to be well indexed by Baidu, Bing,
Google, and Yandex. We measure how difficult an image is to find again
(retrievability), what percentage of images returned are relevant (precision),
and the average number of results a visitor must review before finding the
submitted image (mean reciprocal rank). When trying to discover the same image
again among similar images, Yandex performs best. When searching for pages
containing a specific image, Google and Yandex outperform the others when
discovering photographs with precision scores ranging from 0.8191 to 0.8297,
respectively. In both of these cases, Google and Yandex perform better with
natural images than with abstract ones achieving a difference in retrievability
as high as 54\% between images in these categories. These results affect anyone
applying common web search engines to search for technical documents that use
abstract images.Comment: 20 pages; 7 figures; to be published in the proceedings of the
Drawings and abstract Imagery: Representation and Analysis (DIRA) Workshop
from ECCV 202
Discovering Image Usage Online: A Case Study With "Flatten the Curve''
Understanding the spread of images across the web helps us understand the
reuse of scientific visualizations and their relationship with the public. The
"Flatten the Curve" graphic was heavily used during the COVID-19 pandemic to
convey a complex concept in a simple form. It displays two curves comparing the
impact on case loads for medical facilities if the populace either adopts or
fails to adopt protective measures during a pandemic. We use five variants of
the "Flatten the Curve" image as a case study for viewing the spread of an
image online. To evaluate its spread, we leverage three information channels:
reverse image search engines, social media, and web archives. Reverse image
searches give us a current view into image reuse. Social media helps us
understand a variant's popularity over time. Web archives help us see when it
was preserved, highlighting a view of popularity for future researchers. Our
case study leverages document URLs can be used as a proxy for images when
studying the spread of images online.Comment: 6 pages, 5 figures, Presented as poster at JCDL 202
Controlling the Precision-Recall Tradeoff in Differential Dependency Network Analysis
Graphical models have gained a lot of attention recently as a tool for
learning and representing dependencies among variables in multivariate data.
Often, domain scientists are looking specifically for differences among the
dependency networks of different conditions or populations (e.g. differences
between regulatory networks of different species, or differences between
dependency networks of diseased versus healthy populations). The standard
method for finding these differences is to learn the dependency networks for
each condition independently and compare them. We show that this approach is
prone to high false discovery rates (low precision) that can render the
analysis useless. We then show that by imposing a bias towards learning similar
dependency networks for each condition the false discovery rates can be reduced
to acceptable levels, at the cost of finding a reduced number of differences.
Algorithms developed in the transfer learning literature can be used to vary
the strength of the imposed similarity bias and provide a natural mechanism to
smoothly adjust this differential precision-recall tradeoff to cater to the
requirements of the analysis conducted. We present real case studies
(oncological and neurological) where domain experts use the proposed technique
to extract useful differential networks that shed light on the biological
processes involved in cancer and brain function
Learning Spatial Relationships between Samples of Patent Image Shapes
Binary image based classification and retrieval of documents of an
intellectual nature is a very challenging problem. Variations in the binary
image generation mechanisms which are subject to the document artisan designer
including drawing style, view-point, inclusion of multiple image components are
plausible causes for increasing the complexity of the problem. In this work, we
propose a method suitable to binary images which bridges some of the successes
of deep learning (DL) to alleviate the problems introduced by the
aforementioned variations. The method consists on extracting the shape of
interest from the binary image and applying a non-Euclidean geometric
neural-net architecture to learn the local and global spatial relationships of
the shape. Empirical results show that our method is in some sense invariant to
the image generation mechanism variations and achieves results outperforming
existing methods in a patent image dataset benchmark
Visual Descriptor Extraction From Patent Figure Captions: A Case Study of Data Efficiency Between BiLSTM and Transformer
Technical drawings used for illustrating designs are ubiquitous in patent documents, especially design patents. Different from natural images, these drawings are usually made using black strokes with little color information, making it challenging for models trained on natural images to recognize objects. To facilitate indexing and searching, we propose an effective and efficient visual descriptor model that extracts object names and aspects from patent captions to annotate benchmark patent figure datasets. We compared two state-of-the-art named entity recognition (NER) models and found that with a limited number of annotated samples, the BiLSTM-CRF model outperforms the Transformer model by a significant margin, achieving an overall F1=96.60%. We further conducted a data efficiency study by varying the number of training samples and found that BiLSTM consistently beats the transformer model on our task. The proposed model is used to annotate a benchmark patent figure dataset
Recognizing Figure Labels in Patents
Scientific documents often contain significant information in figures. The United States Patent and Trademark Office (USPTO) awards thousands of patents each week, with each patent containing on the order of a dozen figures. The information conveyed by these figures typically include a drawing or diagram, a label, caption and reference text within the document. Yet associating the short bits of text to the figure is challenging when labels are embedded within the figure, as they typically are in patents. Using patents as a testbench, this paper highlights an open challenge in analyzing all of the information presented in scientific/technical documents - namely, there is a technological gap in recognizing characters embedded in drawings, which leads to difficulties in processing the text associated with scientific figures. We demonstrate that automatically reading the figure label in patent diagram figures is an open challenge, as we evaluate several state-of-the-art optical character recognition (OCR) methods on recent patents. Because the visual characteristics of drawings/diagrams are quite similar to that of text (high contrast, width of strokes, etc), separating the diagram from the text is challenging and leads to both (a) false detection of characters from pixels that are not text and (b) missed text that is critical for identifying the figure number. We develop a method for automatically reading the patent figure labels by first identifying the bounding box containing the label using a novel non-convex hull approach, and then demonstrate the success of OCR when the text is isolated from the diagram
Semi-supervised Learning of Pushforwards For Domain Translation & Adaptation
Given two probability densities on related data spaces, we seek a map pushing
one density to the other while satisfying application-dependent constraints.
For maps to have utility in a broad application space (including domain
translation, domain adaptation, and generative modeling), the map must be
available to apply on out-of-sample data points and should correspond to a
probabilistic model over the two spaces. Unfortunately, existing approaches,
which are primarily based on optimal transport, do not address these needs. In
this paper, we introduce a novel pushforward map learning algorithm that
utilizes normalizing flows to parameterize the map. We first re-formulate the
classical optimal transport problem to be map-focused and propose a learning
algorithm to select from all possible maps under the constraint that the map
minimizes a probability distance and application-specific regularizers; thus,
our method can be seen as solving a modified optimal transport problem. Once
the map is learned, it can be used to map samples from a source domain to a
target domain. In addition, because the map is parameterized as a composition
of normalizing flows, it models the empirical distributions over the two data
spaces and allows both sampling and likelihood evaluation for both data sets.
We compare our method (parOT) to related optimal transport approaches in the
context of domain adaptation and domain translation on benchmark data sets.
Finally, to illustrate the impact of our work on applied problems, we apply
parOT to a real scientific application: spectral calibration for
high-dimensional measurements from two vastly different environmentsComment: 19 pages, 7 figure