20 research outputs found
Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines
Tackling pattern recognition problems in areas such as computer vision,
bioinformatics, speech or text recognition is often done best by taking into
account task-specific statistical relations between output variables. In
structured prediction, this internal structure is used to predict multiple
outputs simultaneously, leading to more accurate and coherent predictions.
Structural support vector machines (SSVMs) are nonprobabilistic models that
optimize a joint input-output function through margin-based learning. Because
SSVMs generally disregard the interplay between unary and interaction factors
during the training phase, final parameters are suboptimal. Moreover, its
factors are often restricted to linear combinations of input features, limiting
its generalization power. To improve prediction accuracy, this paper proposes:
(i) Joint inference and learning by integration of back-propagation and
loss-augmented inference in SSVM subgradient descent; (ii) Extending SSVM
factors to neural networks that form highly nonlinear functions of input
features. Image segmentation benchmark results demonstrate improvements over
conventional SSVM training methods in terms of accuracy, highlighting the
feasibility of end-to-end SSVM training with neural factors
Benchmarking Deep Reinforcement Learning for Continuous Control
Recently, researchers have made significant progress combining the advances
in deep learning for learning feature representations with reinforcement
learning. Some notable examples include training agents to play Atari games
based on raw pixel data and to acquire advanced manipulation skills using raw
sensory inputs. However, it has been difficult to quantify progress in the
domain of continuous control due to the lack of a commonly adopted benchmark.
In this work, we present a benchmark suite of continuous control tasks,
including classic tasks like cart-pole swing-up, tasks with very high state and
action dimensionality such as 3D humanoid locomotion, tasks with partial
observations, and tasks with hierarchical structure. We report novel findings
based on the systematic evaluation of a range of implemented reinforcement
learning algorithms. Both the benchmark and reference implementations are
released at https://github.com/rllab/rllab in order to facilitate experimental
reproducibility and to encourage adoption by other researchers.Comment: 14 pages, ICML 201
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
This paper describes InfoGAN, an information-theoretic extension to the
Generative Adversarial Network that is able to learn disentangled
representations in a completely unsupervised manner. InfoGAN is a generative
adversarial network that also maximizes the mutual information between a small
subset of the latent variables and the observation. We derive a lower bound to
the mutual information objective that can be optimized efficiently, and show
that our training procedure can be interpreted as a variation of the Wake-Sleep
algorithm. Specifically, InfoGAN successfully disentangles writing styles from
digit shapes on the MNIST dataset, pose from lighting of 3D rendered images,
and background digits from the central digit on the SVHN dataset. It also
discovers visual concepts that include hair styles, presence/absence of
eyeglasses, and emotions on the CelebA face dataset. Experiments show that
InfoGAN learns interpretable representations that are competitive with
representations learned by existing fully supervised methods
VIME: Variational Information Maximizing Exploration
Scalable and effective exploration remains a key challenge in reinforcement
learning (RL). While there are methods with optimality guarantees in the
setting of discrete state and action spaces, these methods cannot be applied in
high-dimensional deep RL scenarios. As such, most contemporary RL relies on
simple heuristics such as epsilon-greedy exploration or adding Gaussian noise
to the controls. This paper introduces Variational Information Maximizing
Exploration (VIME), an exploration strategy based on maximization of
information gain about the agent's belief of environment dynamics. We propose a
practical implementation, using variational inference in Bayesian neural
networks which efficiently handles continuous state and action spaces. VIME
modifies the MDP reward function, and can be applied with several different
underlying RL algorithms. We demonstrate that VIME achieves significantly
better performance compared to heuristic exploration methods across a variety
of continuous control tasks and algorithms, including tasks with very sparse
rewards.Comment: Published in Advances in Neural Information Processing Systems 29
(NIPS), pages 1109-111
Robust geometric forest routing with tunable load balancing
Although geometric routing is proposed as a memory-efficient alternative to traditional lookup-based routing and forwarding algorithms, it still lacks: i) adequate mechanisms to trade stretch against load balancing, and ii) robustness to cope with network topology change.
The main contribution of this paper involves the proposal of a family of routing schemes, called Forest Routing. These are based on the principles of geometric routing, adding flexibility in its load balancing characteristics. This is achieved by using an aggregation of greedy embeddings along with a configurable distance function. Incorporating link load information in the forwarding layer enables load balancing behavior while still attaining low path stretch. In addition, the proposed schemes are validated regarding their resilience towards network failures
Structured output prediction for semantic perception in autonomous vehicles
A key challenge in the realization of autonomous vehicles is the machine's ability to perceive its surrounding environment. This task is tackled through a model that partitions vehicle camera input into distinct semantic classes, by taking into account visual contextual cues. The use of structured machine learning models is investigated, which not only allow for complex input, but also arbitrarily structured output. Towards this goal, an outdoor road scene dataset is constructed with accompanying fine-grained image labelings. For coherent segmentation, a structured predictor is modeled to encode label distributions conditioned on the input images. After optimizing this model through max-margin learning, based on an ontological loss function, efficient classification is realized via graph cuts inference using alpha-expansion. Both quantitative and qualitative analyses demonstrate that by taking into account contextual relations between pixel segmentation regions within a second-degree neighborhood, spurious label assignments are filtered out, leading to highly accurate semantic segmentations for outdoor scenes