24,670 research outputs found
Deep AutoRegressive Networks
We introduce a deep, generative autoencoder capable of learning hierarchies
of distributed representations from data. Successive deep stochastic hidden
layers are equipped with autoregressive connections, which enable the model to
be sampled from quickly and exactly via ancestral sampling. We derive an
efficient approximate parameter estimation method based on the minimum
description length (MDL) principle, which can be seen as maximising a
variational lower bound on the log-likelihood, with a feedforward neural
network implementing approximate inference. We demonstrate state-of-the-art
generative performance on a number of classic data sets: several UCI data sets,
MNIST and Atari 2600 games.Comment: Appears in Proceedings of the 31st International Conference on
Machine Learning (ICML), Beijing, China, 201
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
Probabilistic graphical models are a central tool in AI; however, they are
generally not as expressive as deep neural models, and inference is notoriously
hard and slow. In contrast, deep probabilistic models such as sum-product
networks (SPNs) capture joint distributions in a tractable fashion, but still
lack the expressive power of intractable models based on deep neural networks.
Therefore, we introduce conditional SPNs (CSPNs), conditional density
estimators for multivariate and potentially hybrid domains which allow
harnessing the expressive power of neural networks while still maintaining
tractability guarantees. One way to implement CSPNs is to use an existing SPN
structure and condition its parameters on the input, e.g., via a deep neural
network. This approach, however, might misrepresent the conditional
independence structure present in data. Consequently, we also develop a
structure-learning approach that derives both the structure and parameters of
CSPNs from data. Our experimental evidence demonstrates that CSPNs are
competitive with other probabilistic models and yield superior performance on
multilabel image classification compared to mean field and mixture density
networks. Furthermore, they can successfully be employed as building blocks for
structured probabilistic models, such as autoregressive image models.Comment: 13 pages, 6 figure
Domain Randomization and Generative Models for Robotic Grasping
Deep learning-based robotic grasping has made significant progress thanks to
algorithmic improvements and increased data availability. However,
state-of-the-art models are often trained on as few as hundreds or thousands of
unique object instances, and as a result generalization can be a challenge.
In this work, we explore a novel data generation pipeline for training a deep
neural network to perform grasp planning that applies the idea of domain
randomization to object synthesis. We generate millions of unique, unrealistic
procedurally generated objects, and train a deep neural network to perform
grasp planning on these objects.
Since the distribution of successful grasps for a given object can be highly
multimodal, we propose an autoregressive grasp planning model that maps sensor
inputs of a scene to a probability distribution over possible grasps. This
model allows us to sample grasps efficiently at test time (or avoid sampling
entirely).
We evaluate our model architecture and data generation pipeline in simulation
and the real world. We find we can achieve a 90% success rate on previously
unseen realistic objects at test time in simulation despite having only been
trained on random objects. We also demonstrate an 80% success rate on
real-world grasp attempts despite having only been trained on random simulated
objects.Comment: 8 pages, 11 figures. Submitted to 2018 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2018
- …