517 research outputs found
Niche Pseudo-Parallel Genetic Algorithms for Path Optimization of Autonomous Mobile Robot - A Specific Application of TSP
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
To relieve the pain of manually selecting machine learning algorithms and
tuning hyperparameters, automated machine learning (AutoML) methods have been
developed to automatically search for good models. Due to the huge model search
space, it is impossible to try all models. Users tend to distrust automatic
results and increase the search budget as much as they can, thereby undermining
the efficiency of AutoML. To address these issues, we design and implement
ATMSeer, an interactive visualization tool that supports users in refining the
search space of AutoML and analyzing the results. To guide the design of
ATMSeer, we derive a workflow of using AutoML based on interviews with machine
learning experts. A multi-granularity visualization is proposed to enable users
to monitor the AutoML process, analyze the searched models, and refine the
search space in real time. We demonstrate the utility and usability of ATMSeer
through two case studies, expert interviews, and a user study with 13 end
users.Comment: Published in the ACM Conference on Human Factors in Computing Systems
(CHI), 2019, Glasgow, Scotland U
Learning Second Order Local Anomaly for General Face Forgery Detection
In this work, we propose a novel method to improve the generalization ability
of CNN-based face forgery detectors. Our method considers the feature anomalies
of forged faces caused by the prevalent blending operations in face forgery
algorithms. Specifically, we propose a weakly supervised Second Order Local
Anomaly (SOLA) learning module to mine anomalies in local regions using deep
feature maps. SOLA first decomposes the neighborhood of local features by
different directions and distances and then calculates the first and second
order local anomaly maps which provide more general forgery traces for the
classifier. We also propose a Local Enhancement Module (LEM) to improve the
discrimination between local features of real and forged regions, so as to
ensure accuracy in calculating anomalies. Besides, an improved Adaptive Spatial
Rich Model (ASRM) is introduced to help mine subtle noise features via
learnable high pass filters. With neither pixel level annotations nor external
synthetic data, our method using a simple ResNet18 backbone achieves
competitive performances compared with state-of-the-art works when evaluated on
unseen forgeries
Text2Bundle: Towards Personalized Query-based Bundle Generation
Bundle generation aims to provide a bundle of items for the user, and has
been widely studied and applied on online service platforms. Existing bundle
generation methods mainly utilized user's preference from historical
interactions in common recommendation paradigm, and ignored the potential
textual query which is user's current explicit intention. There can be a
scenario in which a user proactively queries a bundle with some natural
language description, the system should be able to generate a bundle that
exactly matches the user's intention through the user's query and preferences.
In this work, we define this user-friendly scenario as Query-based Bundle
Generation task and propose a novel framework Text2Bundle that leverages both
the user's short-term interests from the query and the user's long-term
preferences from the historical interactions. Our framework consists of three
modules: (1) a query interest extractor that mines the user's fine-grained
interests from the query; (2) a unified state encoder that learns the current
bundle context state and the user's preferences based on historical interaction
and current query; and (3) a bundle generator that generates personalized and
complementary bundles using a reinforcement learning with specifically designed
rewards. We conduct extensive experiments on three real-world datasets and
demonstrate the effectiveness of our framework compared with several
state-of-the-art methods
Solving Robotic Trajectory Sequential Writing Problem via Learning Character’s Structural and Sequential Information
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