75,795 research outputs found
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
We consider the NP-hard problem of MAP-inference for undirected discrete
graphical models. We propose a polynomial time and practically efficient
algorithm for finding a part of its optimal solution. Specifically, our
algorithm marks some labels of the considered graphical model either as (i)
optimal, meaning that they belong to all optimal solutions of the inference
problem; (ii) non-optimal if they provably do not belong to any solution. With
access to an exact solver of a linear programming relaxation to the
MAP-inference problem, our algorithm marks the maximal possible (in a specified
sense) number of labels. We also present a version of the algorithm, which has
access to a suboptimal dual solver only and still can ensure the
(non-)optimality for the marked labels, although the overall number of the
marked labels may decrease. We propose an efficient implementation, which runs
in time comparable to a single run of a suboptimal dual solver. Our method is
well-scalable and shows state-of-the-art results on computational benchmarks
from machine learning and computer vision.Comment: Reworked version, submitted to PAM
Adversarial Interpretation of Bayesian Inference
We build on the optimization-centric view on Bayesian inference advocated by Knoblauch et al. (2019). Thinking about Bayesian and generalized Bayesian posteriors as the solutions to a regularized minimization problem allows us to answer an intriguing question: If minimization is the primal problem, then what is its dual? By deriving the Fenchel dual of the problem, we demonstrate that this dual corresponds to an adversarial game: In the dual space, the prior becomes the cost function for an adversary that seeks to perturb the likelihood [loss] function targeted by standard [generalized] Bayesian inference. This implies that Bayes-like procedures are adversarially robust—providing another firm theoretical foundation for their empirical performance. Our contributions are foundational, and apply to a wide-ranging set of Machine Learning methods. This includes standard Bayesian inference, generalized Bayesian and Gibbs posteriors (Bissiri et al., 2016), as well as a diverse set of other methods including Generalized Variational Inference (Knoblauch et al., 2019) and the Wasserstein Autoencoder (Tolstikhin et al., 2017)
Generative discriminative models for multivariate inference and statistical mapping in medical imaging
This paper presents a general framework for obtaining interpretable
multivariate discriminative models that allow efficient statistical inference
for neuroimage analysis. The framework, termed generative discriminative
machine (GDM), augments discriminative models with a generative regularization
term. We demonstrate that the proposed formulation can be optimized in closed
form and in dual space, allowing efficient computation for high dimensional
neuroimaging datasets. Furthermore, we provide an analytic estimation of the
null distribution of the model parameters, which enables efficient statistical
inference and p-value computation without the need for permutation testing. We
compared the proposed method with both purely generative and discriminative
learning methods in two large structural magnetic resonance imaging (sMRI)
datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using
the AD dataset, we demonstrated the ability of GDM to robustly handle
confounding variations. Using Schizophrenia dataset, we demonstrated the
ability of GDM to handle multi-site studies. Taken together, the results
underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding
Memory-Based Dual Gaussian Processes for Sequential Learning
Sequential learning with Gaussian processes (GPs) is challenging when access
to past data is limited, for example, in continual and active learning. In such
cases, errors can accumulate over time due to inaccuracies in the posterior,
hyperparameters, and inducing points, making accurate learning challenging.
Here, we present a method to keep all such errors in check using the recently
proposed dual sparse variational GP. Our method enables accurate inference for
generic likelihoods and improves learning by actively building and updating a
memory of past data. We demonstrate its effectiveness in several applications
involving Bayesian optimization, active learning, and continual learning.Comment: International Conference on Machine Learning (ICML) 202
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
The arms race between attacks and defenses for machine learning models has
come to a forefront in recent years, in both the security community and the
privacy community. However, one big limitation of previous research is that the
security domain and the privacy domain have typically been considered
separately. It is thus unclear whether the defense methods in one domain will
have any unexpected impact on the other domain.
In this paper, we take a step towards resolving this limitation by combining
the two domains. In particular, we measure the success of membership inference
attacks against six state-of-the-art defense methods that mitigate the risk of
adversarial examples (i.e., evasion attacks). Membership inference attacks
determine whether or not an individual data record has been part of a model's
training set. The accuracy of such attacks reflects the information leakage of
training algorithms about individual members of the training set. Adversarial
defense methods against adversarial examples influence the model's decision
boundaries such that model predictions remain unchanged for a small area around
each input. However, this objective is optimized on training data. Thus,
individual data records in the training set have a significant influence on
robust models. This makes the models more vulnerable to inference attacks.
To perform the membership inference attacks, we leverage the existing
inference methods that exploit model predictions. We also propose two new
inference methods that exploit structural properties of robust models on
adversarially perturbed data. Our experimental evaluation demonstrates that
compared with the natural training (undefended) approach, adversarial defense
methods can indeed increase the target model's risk against membership
inference attacks.Comment: ACM CCS 2019, code is available at
https://github.com/inspire-group/privacy-vs-robustnes
Blending Learning and Inference in Structured Prediction
In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machines. For this purpose we utilize the structures of the
predictors to describe a low dimensional structured prediction task which
encourages local consistencies within the different structures while learning
the parameters of the model. Convexity of the learning task provides the means
to enforce the consistencies between the different parts. The
inference-learning blending algorithm that we propose is guaranteed to converge
to the optimum of the low dimensional primal and dual programs. Unlike many of
the existing approaches, the inference-learning blending allows us to learn
efficiently high-order graphical models, over regions of any size, and very
large number of parameters. We demonstrate the effectiveness of our approach,
while presenting state-of-the-art results in stereo estimation, semantic
segmentation, shape reconstruction, and indoor scene understanding
Block Belief Propagation for Parameter Learning in Markov Random Fields
Traditional learning methods for training Markov random fields require doing
inference over all variables to compute the likelihood gradient. The iteration
complexity for those methods therefore scales with the size of the graphical
models. In this paper, we propose \emph{block belief propagation learning}
(BBPL), which uses block-coordinate updates of approximate marginals to compute
approximate gradients, removing the need to compute inference on the entire
graphical model. Thus, the iteration complexity of BBPL does not scale with the
size of the graphs. We prove that the method converges to the same solution as
that obtained by using full inference per iteration, despite these
approximations, and we empirically demonstrate its scalability improvements
over standard training methods.Comment: Accepted to AAAI 201
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