198,745 research outputs found
Self-Adapting Goals Allow Transfer of Predictive Models to New Tasks
A long-standing challenge in Reinforcement Learning is enabling agents to
learn a model of their environment which can be transferred to solve other
problems in a world with the same underlying rules. One reason this is
difficult is the challenge of learning accurate models of an environment. If
such a model is inaccurate, the agent's plans and actions will likely be
sub-optimal, and likely lead to the wrong outcomes. Recent progress in
model-based reinforcement learning has improved the ability for agents to learn
and use predictive models. In this paper, we extend a recent deep learning
architecture which learns a predictive model of the environment that aims to
predict only the value of a few key measurements, which are be indicative of an
agent's performance. Predicting only a few measurements rather than the entire
future state of an environment makes it more feasible to learn a valuable
predictive model. We extend this predictive model with a small, evolving neural
network that suggests the best goals to pursue in the current state. We
demonstrate that this allows the predictive model to transfer to new scenarios
where goals are different, and that the adaptive goals can even adjust agent
behavior on-line, changing its strategy to fit the current context.Comment: Accepted for publication in the proceedings of the 2019 Symposium of
the Norwegian AI Societ
Representation Learning with Contrastive Predictive Coding
While supervised learning has enabled great progress in many applications,
unsupervised learning has not seen such widespread adoption, and remains an
important and challenging endeavor for artificial intelligence. In this work,
we propose a universal unsupervised learning approach to extract useful
representations from high-dimensional data, which we call Contrastive
Predictive Coding. The key insight of our model is to learn such
representations by predicting the future in latent space by using powerful
autoregressive models. We use a probabilistic contrastive loss which induces
the latent space to capture information that is maximally useful to predict
future samples. It also makes the model tractable by using negative sampling.
While most prior work has focused on evaluating representations for a
particular modality, we demonstrate that our approach is able to learn useful
representations achieving strong performance on four distinct domains: speech,
images, text and reinforcement learning in 3D environments
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
Deciphering human behaviors to predict their future paths/trajectories and
what they would do from videos is important in many applications. Motivated by
this idea, this paper studies predicting a pedestrian's future path jointly
with future activities. We propose an end-to-end, multi-task learning system
utilizing rich visual features about human behavioral information and
interaction with their surroundings. To facilitate the training, the network is
learned with an auxiliary task of predicting future location in which the
activity will happen. Experimental results demonstrate our state-of-the-art
performance over two public benchmarks on future trajectory prediction.
Moreover, our method is able to produce meaningful future activity prediction
in addition to the path. The result provides the first empirical evidence that
joint modeling of paths and activities benefits future path prediction.Comment: In CVPR 2019. Code, models and more results are available at:
https://next.cs.cmu.edu
Predicting Model Failure using Saliency Maps in Autonomous Driving Systems
While machine learning systems show high success rate in many complex tasks,
research shows they can also fail in very unexpected situations. Rise of
machine learning products in safety-critical industries cause an increase in
attention in evaluating model robustness and estimating failure probability in
machine learning systems. In this work, we propose a design to train a student
model -- a failure predictor -- to predict the main model's error for input
instances based on their saliency map. We implement and review the preliminary
results of our failure predictor model on an autonomous vehicle steering
control system as an example of safety-critical applications.Comment: Presented at ICML 2019 Workshop on Uncertainty and Robustness in Deep
Learnin
Driving Policy Transfer via Modularity and Abstraction
End-to-end approaches to autonomous driving have high sample complexity and
are difficult to scale to realistic urban driving. Simulation can help
end-to-end driving systems by providing a cheap, safe, and diverse training
environment. Yet training driving policies in simulation brings up the problem
of transferring such policies to the real world. We present an approach to
transferring driving policies from simulation to reality via modularity and
abstraction. Our approach is inspired by classic driving systems and aims to
combine the benefits of modular architectures and end-to-end deep learning
approaches. The key idea is to encapsulate the driving policy such that it is
not directly exposed to raw perceptual input or low-level vehicle dynamics. We
evaluate the presented approach in simulated urban environments and in the real
world. In particular, we transfer a driving policy trained in simulation to a
1/5-scale robotic truck that is deployed in a variety of conditions, with no
finetuning, on two continents. The supplementary video can be viewed at
https://youtu.be/BrMDJqI6H5UComment: Accepted at Conference on Robotic Learning (CoRL'18)
http://proceedings.mlr.press/v87/mueller18a.htm
Bellwethers: A Baseline Method For Transfer Learning
Software analytics builds quality prediction models for software projects.
Experience shows that (a) the more projects studied, the more varied are the
conclusions; and (b) project managers lose faith in the results of software
analytics if those results keep changing. To reduce this conclusion
instability, we propose the use of "bellwethers": given N projects from a
community the bellwether is the project whose data yields the best predictions
on all others. The bellwethers offer a way to mitigate conclusion instability
because conclusions about a community are stable as long as this bellwether
continues as the best oracle. Bellwethers are also simple to discover (just
wrap a for-loop around standard data miners). When compared to other transfer
learning methods (TCA+, transfer Naive Bayes, value cognitive boosting), using
just the bellwether data to construct a simple transfer learner yields
comparable predictions. Further, bellwethers appear in many SE tasks such as
defect prediction, effort estimation, and bad smell detection. We hence
recommend using bellwethers as a baseline method for transfer learning against
which future work should be comparedComment: 23 Page
Predicting the Co-Evolution of Event and Knowledge Graphs
Embedding learning, a.k.a. representation learning, has been shown to be able
to model large-scale semantic knowledge graphs. A key concept is a mapping of
the knowledge graph to a tensor representation whose entries are predicted by
models using latent representations of generalized entities. Knowledge graphs
are typically treated as static: A knowledge graph grows more links when more
facts become available but the ground truth values associated with links is
considered time invariant. In this paper we address the issue of knowledge
graphs where triple states depend on time. We assume that changes in the
knowledge graph always arrive in form of events, in the sense that the events
are the gateway to the knowledge graph. We train an event prediction model
which uses both knowledge graph background information and information on
recent events. By predicting future events, we also predict likely changes in
the knowledge graph and thus obtain a model for the evolution of the knowledge
graph as well. Our experiments demonstrate that our approach performs well in a
clinical application, a recommendation engine and a sensor network application
Personalized Dynamics Models for Adaptive Assistive Navigation Systems
Consider an assistive system that guides visually impaired users through
speech and haptic feedback to their destination. Existing robotic and
ubiquitous navigation technologies (e.g., portable, ground, or wearable
systems) often operate in a generic, user-agnostic manner. However, to minimize
confusion and navigation errors, our real-world analysis reveals a crucial need
to adapt the instructional guidance across different end-users with diverse
mobility skills. To address this practical issue in scalable system design, we
propose a novel model-based reinforcement learning framework for personalizing
the system-user interaction experience. When incrementally adapting the system
to new users, we propose to use a weighted experts model for addressing
data-efficiency limitations in transfer learning with deep models. A real-world
dataset of navigation by blind users is used to show that the proposed approach
allows for (1) more accurate long-term human behavior prediction (up to 20
seconds into the future) through improved reasoning over personal mobility
characteristics, interaction with surrounding obstacles, and the current
navigation goal, and (2) quick adaptation at the onset of learning, when data
is limited.Comment: Oral Presentation in 2nd Conference on Robot Learning (CoRL, 2018
Unsupervised Severe Weather Detection Via Joint Representation Learning Over Textual and Weather Data
When observing a phenomenon, severe cases or anomalies are often
characterised by deviation from the expected data distribution. However,
non-deviating data samples may also implicitly lead to severe outcomes. In the
case of unsupervised severe weather detection, these data samples can lead to
mispredictions, since the predictors of severe weather are often not directly
observed as features. We posit that incorporating external or auxiliary
information, such as the outcome of an external task or an observation, can
improve the decision boundaries of an unsupervised detection algorithm. In this
paper, we increase the effectiveness of a clustering method to detect cases of
severe weather by learning augmented and linearly separable latent
representations.We evaluate our solution against three individual cases of
severe weather, namely windstorms, floods and tornado outbreaks
Neural Allocentric Intuitive Physics Prediction from Real Videos
Humans are able to make rich predictions about the future dynamics of
physical objects from a glance. On the other hand, most existing computer
vision approaches require strong assumptions about the underlying system,
ad-hoc modeling, or annotated datasets, to carry out even simple predictions.
To tackle this gap, we propose a new perspective on the problem of learning
intuitive physics that is inspired by the spatial memory representation of
objects and spaces in human brains, in particular the co-existence of
egocentric and allocentric spatial representations. We present a generic
framework that learns a layered representation of the physical world, using a
cascade of invertible modules. In this framework, real images are first
converted to a synthetic domain representation that reduces complexity arising
from lighting and texture. Then, an allocentric viewpoint transformer removes
viewpoint complexity by projecting images to a canonical view. Finally, a novel
Recurrent Latent Variation Network (RLVN) architecture learns the dynamics of
the objects interacting with the environment and predicts future motion,
leveraging the availability of unlimited synthetic simulations. Predicted
frames are then projected back to the original camera view and translated back
to the real world domain. Experimental results show the ability of the
framework to consistently and accurately predict several frames in the future
and the ability to adapt to real images.Comment: Added references, minor changes. arXiv admin note: text overlap with
arXiv:1506.02025 by other author
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