1,367 research outputs found
Group Sparse CNNs for Question Classification with Answer Sets
Question classification is an important task with wide applications. However,
traditional techniques treat questions as general sentences, ignoring the
corresponding answer data. In order to consider answer information into
question modeling, we first introduce novel group sparse autoencoders which
refine question representation by utilizing group information in the answer
set. We then propose novel group sparse CNNs which naturally learn question
representation with respect to their answers by implanting group sparse
autoencoders into traditional CNNs. The proposed model significantly outperform
strong baselines on four datasets.Comment: 6, ACL 201
Dependency-based Convolutional Neural Networks for Sentence Embedding
In sentence modeling and classification, convolutional neural network
approaches have recently achieved state-of-the-art results, but all such
efforts process word vectors sequentially and neglect long-distance
dependencies. To exploit both deep learning and linguistic structures, we
propose a tree-based convolutional neural network model which exploit various
long-distance relationships between words. Our model improves the sequential
baselines on all three sentiment and question classification tasks, and
achieves the highest published accuracy on TREC.Comment: this paper has been accepted by ACL 201
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent
Existing convergence analyses of Q-learning mostly focus on the vanilla
stochastic gradient descent (SGD) type of updates. Despite the Adaptive Moment
Estimation (Adam) has been commonly used for practical Q-learning algorithms,
there has not been any convergence guarantee provided for Q-learning with such
type of updates. In this paper, we first characterize the convergence rate for
Q-AMSGrad, which is the Q-learning algorithm with AMSGrad update (a commonly
adopted alternative of Adam for theoretical analysis). To further improve the
performance, we propose to incorporate the momentum restart scheme to
Q-AMSGrad, resulting in the so-called Q-AMSGradR algorithm. The convergence
rate of Q-AMSGradR is also established. Our experiments on a linear quadratic
regulator problem show that the two proposed Q-learning algorithms outperform
the vanilla Q-learning with SGD updates. The two algorithms also exhibit
significantly better performance than the DQN learning method over a batch of
Atari 2600 games.Comment: This paper extends the work presented at the 2020 International Joint
Conferences on Artificial Intelligence with supplementary material
TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor
Trajectory generation and prediction are two interwoven tasks that play
important roles in planner evaluation and decision making for intelligent
vehicles. Most existing methods focus on one of the two and are optimized to
directly output the final generated/predicted trajectories, which only contain
limited information for critical scenario augmentation and safe planning. In
this work, we propose a novel behavior-aware Trajectory Autoencoder (TAE) that
explicitly models drivers' behavior such as aggressiveness and intention in the
latent space, using semi-supervised adversarial autoencoder and domain
knowledge in transportation. Our model addresses trajectory generation and
prediction in a unified architecture and benefits both tasks: the model can
generate diverse, controllable and realistic trajectories to enhance planner
optimization in safety-critical and long-tailed scenarios, and it can provide
prediction of critical behavior in addition to the final trajectories for
decision making. Experimental results demonstrate that our method achieves
promising performance on both trajectory generation and prediction.Comment: an updated version, change figures and references. 8 pages, robotics
conference, about trajectory augmentation and prediction for intelligent
vehicle system
Safety-driven Interactive Planning for Neural Network-based Lane Changing
Neural network-based driving planners have shown great promises in improving
task performance of autonomous driving. However, it is critical and yet very
challenging to ensure the safety of systems with neural network based
components, especially in dense and highly interactive traffic environments. In
this work, we propose a safety-driven interactive planning framework for neural
network-based lane changing. To prevent over conservative planning, we identify
the driving behavior of surrounding vehicles and assess their aggressiveness,
and then adapt the planned trajectory for the ego vehicle accordingly in an
interactive manner. The ego vehicle can proceed to change lanes if a safe
evasion trajectory exists even in the predicted worst case; otherwise, it can
stay around the current lateral position or return back to the original lane.
We quantitatively demonstrate the effectiveness of our planner design and its
advantage over baseline methods through extensive simulations with diverse and
comprehensive experimental settings, as well as in real-world scenarios
collected by an autonomous vehicle company
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