41,605 research outputs found
Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation
We introduce Equilibrium Propagation, a learning framework for energy-based
models. It involves only one kind of neural computation, performed in both the
first phase (when the prediction is made) and the second phase of training
(after the target or prediction error is revealed). Although this algorithm
computes the gradient of an objective function just like Backpropagation, it
does not need a special computation or circuit for the second phase, where
errors are implicitly propagated. Equilibrium Propagation shares similarities
with Contrastive Hebbian Learning and Contrastive Divergence while solving the
theoretical issues of both algorithms: our algorithm computes the gradient of a
well defined objective function. Because the objective function is defined in
terms of local perturbations, the second phase of Equilibrium Propagation
corresponds to only nudging the prediction (fixed point, or stationary
distribution) towards a configuration that reduces prediction error. In the
case of a recurrent multi-layer supervised network, the output units are
slightly nudged towards their target in the second phase, and the perturbation
introduced at the output layer propagates backward in the hidden layers. We
show that the signal 'back-propagated' during this second phase corresponds to
the propagation of error derivatives and encodes the gradient of the objective
function, when the synaptic update corresponds to a standard form of
spike-timing dependent plasticity. This work makes it more plausible that a
mechanism similar to Backpropagation could be implemented by brains, since
leaky integrator neural computation performs both inference and error
back-propagation in our model. The only local difference between the two phases
is whether synaptic changes are allowed or not
Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture
We describe a new deep learning architecture for learning to rank question
answer pairs. Our approach extends the long short-term memory (LSTM) network
with holographic composition to model the relationship between question and
answer representations. As opposed to the neural tensor layer that has been
adopted recently, the holographic composition provides the benefits of scalable
and rich representational learning approach without incurring huge parameter
costs. Overall, we present Holographic Dual LSTM (HD-LSTM), a unified
architecture for both deep sentence modeling and semantic matching.
Essentially, our model is trained end-to-end whereby the parameters of the LSTM
are optimized in a way that best explains the correlation between question and
answer representations. In addition, our proposed deep learning architecture
requires no extensive feature engineering. Via extensive experiments, we show
that HD-LSTM outperforms many other neural architectures on two popular
benchmark QA datasets. Empirical studies confirm the effectiveness of
holographic composition over the neural tensor layer.Comment: SIGIR 2017 Full Pape
Managing Dynamic Enterprise and Urgent Workloads on Clouds Using Layered Queuing and Historical Performance Models
The automatic allocation of enterprise workload to resources can be enhanced by being able to make what-if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic-urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: i.) comparatively evaluate the layered queuing and historical techniques; ii.) evaluate the effectiveness of the management algorithm in different operating scenarios; and iii.) provide guidance on using prediction-based workload and resource management
Hallucinating dense optical flow from sparse lidar for autonomous vehicles
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper we propose a novel approach to estimate dense optical flow from sparse lidar data acquired on an autonomous vehicle. This is intended to be used as a drop-in replacement of any image-based optical flow system when images are not reliable due to e.g. adverse weather conditions or at night. In order to infer high resolution 2D flows from discrete range data we devise a three-block architecture of multiscale filters that combines multiple intermediate objectives, both in the lidar and image domain. To train this network we introduce a dataset with approximately 20K lidar samples of the Kitti dataset which we have augmented with a pseudo ground-truth image-based optical flow computed using FlowNet2. We demonstrate the effectiveness of our approach on Kitti, and show that despite using the low-resolution and sparse measurements of the lidar, we can regress dense optical flow maps which are at par with those estimated with image-based methods.Peer ReviewedPostprint (author's final draft
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