290 research outputs found
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
Learning to Generate and Refine Object Proposals
Visual object recognition is a fundamental and challenging
problem in computer vision. To build a practical recognition
system, one is first confronted with high computation complexity
due to an enormous search space from an image, which is caused by
large variations in object appearance, pose and mutual occlusion,
as well as other environmental factors. To reduce the search
complexity, a moderate set of image regions that are likely to
contain an object, regardless of its category, are usually first
generated in modern object recognition subsystems. These possible
object regions are called object proposals, object hypotheses or
object candidates, which can be used for down-stream
classification or global reasoning in many different vision tasks
like object detection, segmentation and tracking, etc.
This thesis addresses the problem of object proposal generation,
including bounding box and segment proposal generation, in
real-world scenarios. In particular, we investigate the
representation learning in object proposal generation with 3D
cues and contextual information, aiming to propose higher-quality
object candidates which have higher object recall, better
boundary coverage and lower number. We focus on three main
issues: 1) how can we incorporate additional geometric and
high-level semantic context information into the proposal
generation for stereo images? 2) how do we generate object
segment proposals for stereo images with learning representations
and learning grouping process? and 3) how can we learn a
context-driven representation to refine segment proposals
efficiently?
In this thesis, we propose a series of solutions to address each
of the raised problems. We first propose a semantic context and
depth-aware object proposal generation method. We design a set of
new cues to encode the objectness, and then train an efficient
random forest classifier to re-rank the initial proposals and
linear regressors to fine-tune their locations. Next, we extend
the task to the segment proposal generation in the same setting
and develop a learning-based segment proposal generation method
for stereo images. Our method makes use of learned deep features
and designed geometric features to represent a region and learns
a similarity network to guide the superpixel grouping process. We
also learn a ranking network to predict the objectness score for
each segment proposal. To address the third problem, we take a
transformation-based approach to improve the quality of a given
segment candidate pool based on context information. We propose
an efficient deep network that learns affine transformations to
warp an initial object mask towards nearby object region, based
on a novel feature pooling strategy. Finally, we extend our
affine warping approach to address the object-mask alignment
problem and particularly the problem of refining a set of segment
proposals. We design an end-to-end deep spatial transformer
network that learns free-form deformations (FFDs) to non-rigidly
warp the shape mask towards the ground truth, based on a
multi-level dual mask feature pooling strategy. We evaluate all
our approaches on several publicly available object recognition
datasets and show superior performance
Inferring latent task structure for Multitask Learning by Multiple Kernel Learning
<p>Abstract</p> <p>Background</p> <p>The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published <it>q</it>-Norm MKL algorithm.</p> <p>Results</p> <p>We demonstrate the performance of our method on two problems from Computational Biology. First, we show that our method is able to improve performance on a splice site dataset with given hierarchical task structure by refining the task relationships. Second, we consider an MHC-I dataset, for which we assume no knowledge about the degree of task relatedness. Here, we are able to learn the task similarities<it> ab initio</it> along with the Multitask classifiers. In both cases, we outperform baseline methods that we compare against.</p> <p>Conclusions</p> <p>We present a novel approach to Multitask Learning that is capable of learning task similarity along with the classifiers. The framework is very general as it allows to incorporate prior knowledge about tasks relationships if available, but is also able to identify task similarities in absence of such prior information. Both variants show promising results in applications from Computational Biology.</p
GLocalX - From Local to Global Explanations of Black Box AI Models
Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications
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