726 research outputs found
Parametric t-Distributed Stochastic Exemplar-centered Embedding
Parametric embedding methods such as parametric t-SNE (pt-SNE) have been
widely adopted for data visualization and out-of-sample data embedding without
further computationally expensive optimization or approximation. However, the
performance of pt-SNE is highly sensitive to the hyper-parameter batch size due
to conflicting optimization goals, and often produces dramatically different
embeddings with different choices of user-defined perplexities. To effectively
solve these issues, we present parametric t-distributed stochastic
exemplar-centered embedding methods. Our strategy learns embedding parameters
by comparing given data only with precomputed exemplars, resulting in a cost
function with linear computational and memory complexity, which is further
reduced by noise contrastive samples. Moreover, we propose a shallow embedding
network with high-order feature interactions for data visualization, which is
much easier to tune but produces comparable performance in contrast to a deep
neural network employed by pt-SNE. We empirically demonstrate, using several
benchmark datasets, that our proposed methods significantly outperform pt-SNE
in terms of robustness, visual effects, and quantitative evaluations.Comment: fixed typo
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations
T-distributed stochastic neighbour embedding (t-SNE) is a widely used data
visualisation technique. It differs from its predecessor SNE by the
low-dimensional similarity kernel: the Gaussian kernel was replaced by the
heavy-tailed Cauchy kernel, solving the "crowding problem" of SNE. Here, we
develop an efficient implementation of t-SNE for a -distribution kernel with
an arbitrary degree of freedom , with corresponding to SNE
and corresponding to the standard t-SNE. Using theoretical analysis and
toy examples, we show that can further reduce the crowding problem and
reveal finer cluster structure that is invisible in standard t-SNE. We further
demonstrate the striking effect of heavier-tailed kernels on large real-life
data sets such as MNIST, single-cell RNA-sequencing data, and the HathiTrust
library. We use domain knowledge to confirm that the revealed clusters are
meaningful. Overall, we argue that modifying the tail heaviness of the t-SNE
kernel can yield additional insight into the cluster structure of the data
Unsupervised user behavior representation for fraud review detection with cold-start problem
© Springer Nature Switzerland AG 2019. Detecting fraud review is becoming extremely important in order to provide reliable information in cyberspace, in which, however, handling cold-start problem is a critical and urgent challenge since the case of cold-start fraud review rarely provides sufficient information for further assessing its authenticity. Existing work on detecting cold-start cases relies on the limited contents of the review posted by the user and a traditional classifier to make the decision. However, simply modeling review is not reliable since reviews can be easily manipulated. Also, it is hard to obtain high-quality labeled data for training the classifier. In this paper, we tackle cold-start problems by (1) using a user’s behavior representation rather than review contents to measure authenticity, which further (2) consider user social relations with other existing users when posting reviews. The method is completely (3) unsupervised. Comprehensive experiments on Yelp data sets demonstrate our method significantly outperforms the state-of-the-art methods
Control of an AUV from thruster actuated hover to control surface actuated flight
An autonomous underwater vehicle (AUV) capable of both low speed hovering and high speed flight-style operation is introduced. To have this capability the AUV is over-actuated with a rear propeller, four control surfaces and four through-body tunnel thrusters. In this work the actuators are modelled and the non-linearities and uncertainties are identified and discussed with specific regard to operation at different speeds. A thruster-actuated depth control algorithm and a flight-style control-surface actuated depth controller are presented. These controllers are then coupled using model reference feedback to enable transition between the two controllers to enable vehicle stability throughout the speed range. Results from 3 degrees-of-freedom simulations of the AUV using the new controller are presented, showing that the controller works well to smoothly transition between controllers. The performance of the depth controller appears asymmetric with better performance whilst diving than ascendin
Probabilistic movement modeling for intention inference in human-robot interaction.
Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from observed movements using Bayes ’ theorem. The IDDM simultaneously finds a latent state representation of noisy and highdimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.
Diagnostic accuracy of evaluation of suspected syncope in the emergency department:usual practice vs. ESC guidelines
Background: Syncope is a frequent reason for referral to the emergency department. After excluding a potentially life-threatening condition, the second objective is to find the cause of syncope. The objective of this study was to assess the diagnostic accuracy of the treating physician in usual practice and to compare this to the diagnostic accuracy of a standardised evaluation, consisting of thorough history taking and physical examination by a research physician. Methods: This prospective cohort study included suspected (pre) syncope patients without an identified serious underlying condition who were assessed in the emergency department. Patients were initially seen by the initial treating physician and the usual evaluation was performed. A research physician, blinded to the findings of the initial treating physician, then performed a standardised evaluation according to the ESC syncope guidelines. Diagnostic accuracy (proportion of correct diagnoses) was determined by expert consensus after long-term follow-up. Results: One hundred and one suspected (pre) syncope patients were included (mean age 59 ± 20 years). The usual practice of the initial treating physicians did not in most cases follow ESC syncope guidelines, with orthostatic blood pressure measurements made in only 40% of the patients. Diagnostic accuracy by the initial treating physicians was 65% (95% CI 56-74%), while standardised evaluation resulted in a diagnostic accuracy of 80% (95% CI 71-87%; p = 0.009). No life-threatening causes were missed. Conclusions: Usual practice of the initial treating physician resulted in a diagnostic accuracy of 65%, while standardised practice, with an emphasis on thorough history taking, increased diagnostic accuracy to 80%. Results suggest that the availability of additional resources does not result in a higher diagnostic accuracy than standardised evaluation, and that history taking is the most important diagnostic test in suspected syncope patients. Netherlands Trial Registration: NTR5651. Registered 29 January 2016, https://www.trialregister.nl/trial/553
Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset
Current dataset collection methods typically scrape large amounts of data
from the web. While this technique is extremely scalable, data collected in
this way tends to reinforce stereotypical biases, can contain personally
identifiable information, and typically originates from Europe and North
America. In this work, we rethink the dataset collection paradigm and introduce
GeoDE, a geographically diverse dataset with 61,940 images from 40 classes and
6 world regions, and no personally identifiable information, collected through
crowd-sourcing. We analyse GeoDE to understand differences in images collected
in this manner compared to web-scraping. Despite the smaller size of this
dataset, we demonstrate its use as both an evaluation and training dataset,
highlight shortcomings in current models, as well as show improved performances
when even small amounts of GeoDE (1000 - 2000 images per region) are added to a
training dataset. We release the full dataset and code at
https://geodiverse-data-collection.cs.princeton.edu
Generic 3D Representation via Pose Estimation and Matching
Though a large body of computer vision research has investigated developing
generic semantic representations, efforts towards developing a similar
representation for 3D has been limited. In this paper, we learn a generic 3D
representation through solving a set of foundational proxy 3D tasks:
object-centric camera pose estimation and wide baseline feature matching. Our
method is based upon the premise that by providing supervision over a set of
carefully selected foundational tasks, generalization to novel tasks and
abstraction capabilities can be achieved. We empirically show that the internal
representation of a multi-task ConvNet trained to solve the above core problems
generalizes to novel 3D tasks (e.g., scene layout estimation, object pose
estimation, surface normal estimation) without the need for fine-tuning and
shows traits of abstraction abilities (e.g., cross-modality pose estimation).
In the context of the core supervised tasks, we demonstrate our representation
achieves state-of-the-art wide baseline feature matching results without
requiring apriori rectification (unlike SIFT and the majority of learned
features). We also show 6DOF camera pose estimation given a pair local image
patches. The accuracy of both supervised tasks come comparable to humans.
Finally, we contribute a large-scale dataset composed of object-centric street
view scenes along with point correspondences and camera pose information, and
conclude with a discussion on the learned representation and open research
questions.Comment: Published in ECCV16. See the project website
http://3drepresentation.stanford.edu/ and dataset website
https://github.com/amir32002/3D_Street_Vie
Dynamic Key-Value Memory Networks for Knowledge Tracing
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of
students with respect to one or more concepts as they engage in a sequence of
learning activities. One important purpose of KT is to personalize the practice
sequence to help students learn knowledge concepts efficiently. However,
existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing
either model knowledge state for each predefined concept separately or fail to
pinpoint exactly which concepts a student is good at or unfamiliar with. To
solve these problems, this work introduces a new model called Dynamic Key-Value
Memory Networks (DKVMN) that can exploit the relationships between underlying
concepts and directly output a student's mastery level of each concept. Unlike
standard memory-augmented neural networks that facilitate a single memory
matrix or two static memory matrices, our model has one static matrix called
key, which stores the knowledge concepts and the other dynamic matrix called
value, which stores and updates the mastery levels of corresponding concepts.
Experiments show that our model consistently outperforms the state-of-the-art
model in a range of KT datasets. Moreover, the DKVMN model can automatically
discover underlying concepts of exercises typically performed by human
annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW),
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Can Genetic Programming Do Manifold Learning Too?
Exploratory data analysis is a fundamental aspect of knowledge discovery that
aims to find the main characteristics of a dataset. Dimensionality reduction,
such as manifold learning, is often used to reduce the number of features in a
dataset to a manageable level for human interpretation. Despite this, most
manifold learning techniques do not explain anything about the original
features nor the true characteristics of a dataset. In this paper, we propose a
genetic programming approach to manifold learning called GP-MaL which evolves
functional mappings from a high-dimensional space to a lower dimensional space
through the use of interpretable trees. We show that GP-MaL is competitive with
existing manifold learning algorithms, while producing models that can be
interpreted and re-used on unseen data. A number of promising future directions
of research are found in the process.Comment: 16 pages, accepted in EuroGP '1
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