26,975 research outputs found
Integral Equations and Machine Learning
As both light transport simulation and reinforcement learning are ruled by
the same Fredholm integral equation of the second kind, reinforcement learning
techniques may be used for photorealistic image synthesis: Efficiency may be
dramatically improved by guiding light transport paths by an approximate
solution of the integral equation that is learned during rendering. In the
light of the recent advances in reinforcement learning for playing games, we
investigate the representation of an approximate solution of an integral
equation by artificial neural networks and derive a loss function for that
purpose. The resulting Monte Carlo and quasi-Monte Carlo methods train neural
networks with standard information instead of linear information and naturally
are able to generate an arbitrary number of training samples. The methods are
demonstrated for applications in light transport simulation
Formal Policy Synthesis for Continuous-Space Systems via Reinforcement Learning
This paper studies satisfaction of temporal properties on unknown stochastic
processes that have continuous state spaces. We show how reinforcement learning
(RL) can be applied for computing policies that are finite-memory and
deterministic using only the paths of the stochastic process. We address
properties expressed in linear temporal logic (LTL) and use their automaton
representation to give a path-dependent reward function maximised via the RL
algorithm. We develop the required assumptions and theories for the convergence
of the learned policy to the optimal policy in the continuous state space. To
improve the performance of the learning on the constructed sparse reward
function, we propose a sequential learning procedure based on a sequence of
labelling functions obtained from the positive normal form of the LTL
specification. We use this procedure to guide the RL algorithm towards a policy
that converges to an optimal policy under suitable assumptions on the process.
We demonstrate the approach on a 4-dim cart-pole system and 6-dim boat driving
problem.Comment: This is the extended version of the paper accepted in the 16th
International Conference on integrated Formal Methods (iFM
Context-Aware Synthesis and Placement of Object Instances
Learning to insert an object instance into an image in a semantically
coherent manner is a challenging and interesting problem. Solving it requires
(a) determining a location to place an object in the scene and (b) determining
its appearance at the location. Such an object insertion model can potentially
facilitate numerous image editing and scene parsing applications. In this
paper, we propose an end-to-end trainable neural network for the task of
inserting an object instance mask of a specified class into the semantic label
map of an image. Our network consists of two generative modules where one
determines where the inserted object mask should be (i.e., location and scale)
and the other determines what the object mask shape (and pose) should look
like. The two modules are connected together via a spatial transformation
network and jointly trained. We devise a learning procedure that leverage both
supervised and unsupervised data and show our model can insert an object at
diverse locations with various appearances. We conduct extensive experimental
validations with comparisons to strong baselines to verify the effectiveness of
the proposed network
Inverse Transport Networks
We introduce inverse transport networks as a learning architecture for
inverse rendering problems where, given input image measurements, we seek to
infer physical scene parameters such as shape, material, and illumination.
During training, these networks are evaluated not only in terms of how close
they can predict groundtruth parameters, but also in terms of whether the
parameters they produce can be used, together with physically-accurate graphics
renderers, to reproduce the input image measurements. To en- able training of
inverse transport networks using stochastic gradient descent, we additionally
create a general-purpose, physically-accurate differentiable renderer, which
can be used to estimate derivatives of images with respect to arbitrary
physical scene parameters. Our experiments demonstrate that inverse transport
networks can be trained efficiently using differentiable rendering, and that
they generalize to scenes with completely unseen geometry and illumination
better than networks trained without appearance- matching regularization
Evolutionary Cell Aided Design for Neural Network Architectures
Mathematical theory shows us that multilayer feedforward Artificial Neural
Networks(ANNs) are universal function approximators, capable of approximating
any measurable function to any desired degree of accuracy. In practice
designing practical and efficient neural network architectures require
significant effort and expertise. We present a novel software framework called
Evolutionary Cell Aided Design(ECAD) meant to aid in the exploration and design
of efficient Neural Network Architectures(NNAs) for reconfigurable hardware.
Given a general neural network structure and a set of constraints and fitness
functions, the framework will explore both the space of possible NNA and the
space of possible hardware designs, using evolutionary algorithms, and attempt
to find the fittest co-design solutions according to a predefined set of goals.
We test the framework on an image classification task and use the MNIST data
set of hand written digits with an Intel Arria 10 GX 1150 device as our target
platform. We design and implement a modular and scalable 2D systolic array with
enhancements for machine learning that can be used by the framework for the
hardware search space. Our results demonstrate the ability to pair neural
network design and hardware development together using an evolutionary
algorithm and removing traditional human-in-the-loop development tasks. By
running various experiments of the fittest solutions for neural network and
hardware searches, we demonstrate the full end-to-end capabilities of the ECAD
framework.Comment: Text and image edit
Neural Logic Machines
We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for
both inductive learning and logic reasoning. NLMs exploit the power of both
neural networks---as function approximators, and logic programming---as a
symbolic processor for objects with properties, relations, logic connectives,
and quantifiers. After being trained on small-scale tasks (such as sorting
short arrays), NLMs can recover lifted rules, and generalize to large-scale
tasks (such as sorting longer arrays). In our experiments, NLMs achieve perfect
generalization in a number of tasks, from relational reasoning tasks on the
family tree and general graphs, to decision making tasks including sorting
arrays, finding shortest paths, and playing the blocks world. Most of these
tasks are hard to accomplish for neural networks or inductive logic programming
alone.Comment: ICLR 2019. Project page:
https://sites.google.com/view/neural-logic-machine
Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning
This paper presents a novel Subject-dependent Deep Aging Path (SDAP), which
inherits the merits of both Generative Probabilistic Modeling and Inverse
Reinforcement Learning to model the facial structures and the longitudinal face
aging process of a given subject. The proposed SDAP is optimized using
tractable log-likelihood objective functions with Convolutional Neural Networks
(CNNs) based deep feature extraction. Instead of applying a fixed aging
development path for all input faces and subjects, SDAP is able to provide the
most appropriate aging development path for individual subject that optimizes
the reward aging formulation. Unlike previous methods that can take only one
image as the input, SDAP further allows multiple images as inputs, i.e. all
information of a subject at either the same or different ages, to produce the
optimal aging path for the given subject. Finally, SDAP allows efficiently
synthesizing in-the-wild aging faces. The proposed model is experimented in
both tasks of face aging synthesis and cross-age face verification. The
experimental results consistently show SDAP achieves the state-of-the-art
performance on numerous face aging databases, i.e. FG-NET, MORPH, AginG Faces
in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). Furthermore, we
also evaluate the performance of SDAP on large-scale Megaface challenge to
demonstrate the advantages of the proposed solution
Neural-Network Guided Expression Transformation
Optimizing compilers, as well as other translator systems, often work by
rewriting expressions according to equivalence preserving rules. Given an input
expression and its optimized form, finding the sequence of rules that were
applied is a non-trivial task. Most of the time, the tools provide no proof, of
any kind, of the equivalence between the original expression and its optimized
form. In this work, we propose to reconstruct proofs of equivalence of simple
mathematical expressions, after the fact, by finding paths of equivalence
preserving transformations between expressions. We propose to find those
sequences of transformations using a search algorithm, guided by a neural
network heuristic. Using a Tree-LSTM recursive neural network, we learn a
distributed representation of expressions where the Manhattan distance between
vectors approximately corresponds to the rewrite distance between expressions.
We then show how the neural network can be efficiently used to search for
transformation paths, leading to substantial gain in speed compared to an
uninformed exhaustive search. In one of our experiments, our neural-network
guided search algorithm is able to solve more instances with a 2 seconds
timeout per instance than breadth-first search does with a 5 minutes timeout
per instance
Neural Style Transfer: A Review
The seminal work of Gatys et al. demonstrated the power of Convolutional
Neural Networks (CNNs) in creating artistic imagery by separating and
recombining image content and style. This process of using CNNs to render a
content image in different styles is referred to as Neural Style Transfer
(NST). Since then, NST has become a trending topic both in academic literature
and industrial applications. It is receiving increasing attention and a variety
of approaches are proposed to either improve or extend the original NST
algorithm. In this paper, we aim to provide a comprehensive overview of the
current progress towards NST. We first propose a taxonomy of current algorithms
in the field of NST. Then, we present several evaluation methods and compare
different NST algorithms both qualitatively and quantitatively. The review
concludes with a discussion of various applications of NST and open problems
for future research. A list of papers discussed in this review, corresponding
codes, pre-trained models and more comparison results are publicly available at
https://github.com/ycjing/Neural-Style-Transfer-Papers.Comment: Project page: https://github.com/ycjing/Neural-Style-Transfer-Paper
Introspective Generative Modeling: Decide Discriminatively
We study unsupervised learning by developing introspective generative
modeling (IGM) that attains a generator using progressively learned deep
convolutional neural networks. The generator is itself a discriminator, capable
of introspection: being able to self-evaluate the difference between its
generated samples and the given training data. When followed by repeated
discriminative learning, desirable properties of modern discriminative
classifiers are directly inherited by the generator. IGM learns a cascade of
CNN classifiers using a synthesis-by-classification algorithm. In the
experiments, we observe encouraging results on a number of applications
including texture modeling, artistic style transferring, face modeling, and
semi-supervised learning.Comment: 10 pages, 9 figure
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