57 research outputs found
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
Predicting protein properties such as solvent accessibility and secondary
structure from its primary amino acid sequence is an important task in
bioinformatics. Recently, a few deep learning models have surpassed the
traditional window based multilayer perceptron. Taking inspiration from the
image classification domain we propose a deep convolutional neural network
architecture, MUST-CNN, to predict protein properties. This architecture uses a
novel multilayer shift-and-stitch (MUST) technique to generate fully dense
per-position predictions on protein sequences. Our model is significantly
simpler than the state-of-the-art, yet achieves better results. By combining
MUST and the efficient convolution operation, we can consider far more
parameters while retaining very fast prediction speeds. We beat the
state-of-the-art performance on two large protein property prediction datasets.Comment: 8 pages ; 3 figures ; deep learning based sequence-sequence
prediction. in AAAI 201
Value Propagation Networks
We present Value Propagation (VProp), a set of parameter-efficient
differentiable planning modules built on Value Iteration which can successfully
be trained using reinforcement learning to solve unseen tasks, has the
capability to generalize to larger map sizes, and can learn to navigate in
dynamic environments. We show that the modules enable learning to plan when the
environment also includes stochastic elements, providing a cost-efficient
learning system to build low-level size-invariant planners for a variety of
interactive navigation problems. We evaluate on static and dynamic
configurations of MazeBase grid-worlds, with randomly generated environments of
several different sizes, and on a StarCraft navigation scenario, with more
complex dynamics, and pixels as input.Comment: Updated to match ICLR 2019 OpenReview's versio
Growing Action Spaces
In complex tasks, such as those with large combinatorial action spaces,
random exploration may be too inefficient to achieve meaningful learning
progress. In this work, we use a curriculum of progressively growing action
spaces to accelerate learning. We assume the environment is out of our control,
but that the agent may set an internal curriculum by initially restricting its
action space. Our approach uses off-policy reinforcement learning to estimate
optimal value functions for multiple action spaces simultaneously and
efficiently transfers data, value estimates, and state representations from
restricted action spaces to the full task. We show the efficacy of our approach
in proof-of-concept control tasks and on challenging large-scale StarCraft
micromanagement tasks with large, multi-agent action spaces
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Many point-based 3D detectors adopt point-feature sampling strategies to drop
some points for efficient inference. These strategies are typically based on
fixed and handcrafted rules, making difficult to handle complicated scenes.
Different from them, we propose a Dynamic Ball Query (DBQ) network to
adaptively select a subset of input points according to the input features, and
assign the feature transform with suitable receptive field for each selected
point. It can be embedded into some state-of-the-art 3D detectors and trained
in an end-to-end manner, which significantly reduces the computational cost.
Extensive experiments demonstrate that our method can reduce latency by 30%-60%
on KITTI and Waymo datasets. Specifically, the inference speed of our detector
can reach 162 FPS and 30 FPS with negligible performance degradation on KITTI
and Waymo datasets, respectively
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