13,945 research outputs found
Empirical Study of Deep Learning for Text Classification in Legal Document Review
Predictive coding has been widely used in legal matters to find relevant or
privileged documents in large sets of electronically stored information. It
saves the time and cost significantly. Logistic Regression (LR) and Support
Vector Machines (SVM) are two popular machine learning algorithms used in
predictive coding. Recently, deep learning received a lot of attentions in many
industries. This paper reports our preliminary studies in using deep learning
in legal document review. Specifically, we conducted experiments to compare
deep learning results with results obtained using a SVM algorithm on the four
datasets of real legal matters. Our results showed that CNN performed better
with larger volume of training dataset and should be a fit method in the text
classification in legal industry.Comment: 2018 IEEE International Conference on Big Data (Big Data
Event-Triggered Algorithms for Leader-Follower Consensus of Networked Euler-Lagrange Agents
This paper proposes three different distributed event-triggered control
algorithms to achieve leader-follower consensus for a network of Euler-Lagrange
agents. We firstly propose two model-independent algorithms for a subclass of
Euler-Lagrange agents without the vector of gravitational potential forces. By
model-independent, we mean that each agent can execute its algorithm with no
knowledge of the agent self-dynamics. A variable-gain algorithm is employed
when the sensing graph is undirected; algorithm parameters are selected in a
fully distributed manner with much greater flexibility compared to all previous
work concerning event-triggered consensus problems. When the sensing graph is
directed, a constant-gain algorithm is employed. The control gains must be
centrally designed to exceed several lower bounding inequalities which require
limited knowledge of bounds on the matrices describing the agent dynamics,
bounds on network topology information and bounds on the initial conditions.
When the Euler-Lagrange agents have dynamics which include the vector of
gravitational potential forces, an adaptive algorithm is proposed which
requires more information about the agent dynamics but can estimate uncertain
agent parameters.
For each algorithm, a trigger function is proposed to govern the event update
times. At each event, the controller is updated, which ensures that the control
input is piecewise constant and saves energy resources. We analyse each
controllers and trigger function and exclude Zeno behaviour. Extensive
simulations show 1) the advantages of our proposed trigger function as compared
to those in existing literature, and 2) the effectiveness of our proposed
controllers.Comment: Extended manuscript of journal submission, containing omitted proofs
and simulation
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
In this paper, we develop a reinforcement learning (RL) based system to learn
an effective policy for carpooling that maximizes transportation efficiency so
that fewer cars are required to fulfill the given amount of trip demand. For
this purpose, first, we develop a deep neural network model, called ST-NN
(Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS
trip data. Secondly, we develop a carpooling simulation environment for RL
training, with the output of ST-NN and using the NYC taxi trip dataset. In
order to maximize transportation efficiency and minimize traffic congestion, we
choose the effective distance covered by the driver on a carpool trip as the
reward. Therefore, the more effective distance a driver achieves over a trip
(i.e. to satisfy more trip demand) the higher the efficiency and the less will
be the traffic congestion. We compared the performance of RL learned policy to
a fixed policy (which always accepts carpool) as a baseline and obtained
promising results that are interpretable and demonstrate the advantage of our
RL approach. We also compare the performance of ST-NN to that of
state-of-the-art travel time estimation methods and observe that ST-NN
significantly improves the prediction performance and is more robust to
outliers.Comment: Accepted at IEEE International Conference on Big Data 2018. arXiv
admin note: text overlap with arXiv:1710.0435
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