231 research outputs found
Two-Component, Four Reaction Domino Sequence toward Novel Tricyclic 1,4-dihydro-2H-benzo(f)sochromenes
The synthesis of complex molecules usuall
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Click-Through Rate prediction is an important task in recommender systems,
which aims to estimate the probability of a user to click on a given item.
Recently, many deep models have been proposed to learn low-order and high-order
feature interactions from original features. However, since useful interactions
are always sparse, it is difficult for DNN to learn them effectively under a
large number of parameters. In real scenarios, artificial features are able to
improve the performance of deep models (such as Wide & Deep Learning), but
feature engineering is expensive and requires domain knowledge, making it
impractical in different scenarios. Therefore, it is necessary to augment
feature space automatically. In this paper, We propose a novel Feature
Generation by Convolutional Neural Network (FGCNN) model with two components:
Feature Generation and Deep Classifier. Feature Generation leverages the
strength of CNN to generate local patterns and recombine them to generate new
features. Deep Classifier adopts the structure of IPNN to learn interactions
from the augmented feature space. Experimental results on three large-scale
datasets show that FGCNN significantly outperforms nine state-of-the-art
models. Moreover, when applying some state-of-the-art models as Deep
Classifier, better performance is always achieved, showing the great
compatibility of our FGCNN model. This work explores a novel direction for CTR
predictions: it is quite useful to reduce the learning difficulties of DNN by
automatically identifying important features
LFGCN: Levitating over Graphs with Levy Flights
Due to high utility in many applications, from social networks to blockchain
to power grids, deep learning on non-Euclidean objects such as graphs and
manifolds, coined Geometric Deep Learning (GDL), continues to gain an ever
increasing interest. We propose a new L\'evy Flights Graph Convolutional
Networks (LFGCN) method for semi-supervised learning, which casts the L\'evy
Flights into random walks on graphs and, as a result, allows both to accurately
account for the intrinsic graph topology and to substantially improve
classification performance, especially for heterogeneous graphs. Furthermore,
we propose a new preferential P-DropEdge method based on the Girvan-Newman
argument. That is, in contrast to uniform removing of edges as in DropEdge,
following the Girvan-Newman algorithm, we detect network periphery structures
using information on edge betweenness and then remove edges according to their
betweenness centrality. Our experimental results on semi-supervised node
classification tasks demonstrate that the LFGCN coupled with P-DropEdge
accelerates the training task, increases stability and further improves
predictive accuracy of learned graph topology structure. Finally, in our case
studies we bring the machinery of LFGCN and other deep networks tools to
analysis of power grid networks - the area where the utility of GDL remains
untapped.Comment: To Appear in the 2020 IEEE International Conference on Data Mining
(ICDM
Diffusion of AI Governance
Artificial intelligence (AI) has the potential to address social, economic, and environmental challenges. However, effective use of AI in organizations relies on the establishment of an AI governance framework. Although existing studies have discussed a variety of issues raised by AI-based systems and proposed AI governance frameworks to overcome those issues, organizations face challenges in adopting AI governance. Informed by innovation diffusion theory, this research evaluates the impact of internal and external influences on AI governance adoption between highly regulated and less regulated industries. We also assess the effect of adopting AI governance on organizational performance. Findings from this study will not only provide a nuanced understanding of the source of AI governance adoption, but also provide implications and guidelines for implementing AI governance in organizations
Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence
Learning time-evolving objects such as multivariate time series and dynamic
networks requires the development of novel knowledge representation mechanisms
and neural network architectures, which allow for capturing implicit
time-dependent information contained in the data. Such information is typically
not directly observed but plays a key role in the learning task performance. In
turn, lack of time dimension in knowledge encoding mechanisms for
time-dependent data leads to frequent model updates, poor learning performance,
and, as a result, subpar decision-making. Here we propose a new approach to a
time-aware knowledge representation mechanism that notably focuses on implicit
time-dependent topological information along multiple geometric dimensions. In
particular, we propose a new approach, named \textit{Temporal MultiPersistence}
(TMP), which produces multidimensional topological fingerprints of the data by
using the existing single parameter topological summaries. The main idea behind
TMP is to merge the two newest directions in topological representation
learning, that is, multi-persistence which simultaneously describes data shape
evolution along multiple key parameters, and zigzag persistence to enable us to
extract the most salient data shape information over time. We derive
theoretical guarantees of TMP vectorizations and show its utility, in
application to forecasting on benchmark traffic flow, Ethereum blockchain, and
electrocardiogram datasets, demonstrating the competitive performance,
especially, in scenarios of limited data records. In addition, our TMP method
improves the computational efficiency of the state-of-the-art multipersistence
summaries up to 59.5 times
SpOctA: A 3D Sparse Convolution Accelerator with Octree-Encoding-Based Map Search and Inherent Sparsity-Aware Processing
Point-cloud-based 3D perception has attracted great attention in various
applications including robotics, autonomous driving and AR/VR. In particular,
the 3D sparse convolution (SpConv) network has emerged as one of the most
popular backbones due to its excellent performance. However, it poses severe
challenges to real-time perception on general-purpose platforms, such as
lengthy map search latency, high computation cost, and enormous memory
footprint. In this paper, we propose SpOctA, a SpConv accelerator that enables
high-speed and energy-efficient point cloud processing. SpOctA parallelizes the
map search by utilizing algorithm-architecture co-optimization based on octree
encoding, thereby achieving 8.8-21.2x search speedup. It also attenuates the
heavy computational workload by exploiting inherent sparsity of each voxel,
which eliminates computation redundancy and saves 44.4-79.1% processing
latency. To optimize on-chip memory management, a SpConv-oriented non-uniform
caching strategy is introduced to reduce external memory access energy by 57.6%
on average. Implemented on a 40nm technology and extensively evaluated on
representative benchmarks, SpOctA rivals the state-of-the-art SpConv
accelerators by 1.1-6.9x speedup with 1.5-3.1x energy efficiency improvement.Comment: Accepted to ICCAD 202
Efficient Planning of Multi-Robot Collective Transport using Graph Reinforcement Learning with Higher Order Topological Abstraction
Efficient multi-robot task allocation (MRTA) is fundamental to various
time-sensitive applications such as disaster response, warehouse operations,
and construction. This paper tackles a particular class of these problems that
we call MRTA-collective transport or MRTA-CT -- here tasks present varying
workloads and deadlines, and robots are subject to flight range, communication
range, and payload constraints. For large instances of these problems involving
100s-1000's of tasks and 10s-100s of robots, traditional non-learning solvers
are often time-inefficient, and emerging learning-based policies do not scale
well to larger-sized problems without costly retraining. To address this gap,
we use a recently proposed encoder-decoder graph neural network involving
Capsule networks and multi-head attention mechanism, and innovatively add
topological descriptors (TD) as new features to improve transferability to
unseen problems of similar and larger size. Persistent homology is used to
derive the TD, and proximal policy optimization is used to train our
TD-augmented graph neural network. The resulting policy model compares
favorably to state-of-the-art non-learning baselines while being much faster.
The benefit of using TD is readily evident when scaling to test problems of
size larger than those used in training.Comment: This paper has been accepted to be presented at the IEEE
International Conference on Robotics and Automation, 202
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