1,459 research outputs found
Finance and economic development: Township and village enterprises in the People's Republic of China
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Towards Interpretability and Robustness of Machine Learning Models
Modern machine learning models can be difficult to probe and understand after they have been trained. This is a major problem for the field, with consequences for trustworthiness, diagnostics, debugging, robustness, and a range of other engineering and human interaction issues surrounding the deployment of a model. Another problem of modern machine learning models is their vulnerability to small adversarial perturbations to the input, which incurs a security risk when they are applied to critical areas.In this thesis, we develop systematic and efficient tools for interpreting machine learning models and evaluating their adversarial robustness. Part I focuses on model interpretation. We derive an efficient feature scoring method by exploiting the graph structure in data. We also develop a learning-based method under an information-based framework. As an attempt to leverage prior knowledge about what constitutes a satisfying interpretation in a given domain, we propose a systematic approach to exploiting syntactic constituency structure by leveraging a parse tree for interpretation of models in the setting of linguistic data. Part II focuses on the evaluation of adversarial robustness. We first propose a probabilistic framework for generating adversarial examples on discrete data, and develop two algorithms to implement it. We also introduce a novel attack method in the setting where the attacker has access to model decisions alone. We investigate the robustness of various machine learning models and existing defense mechanisms under the proposed attack method. In Part III, we build a connection between the two fields by developing a method for detecting adversarial examples via tools in model interpretation
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack
The goal of a decision-based adversarial attack on a trained model is to
generate adversarial examples based solely on observing output labels returned
by the targeted model. We develop HopSkipJumpAttack, a family of algorithms
based on a novel estimate of the gradient direction using binary information at
the decision boundary. The proposed family includes both untargeted and
targeted attacks optimized for and similarity metrics
respectively. Theoretical analysis is provided for the proposed algorithms and
the gradient direction estimate. Experiments show HopSkipJumpAttack requires
significantly fewer model queries than Boundary Attack. It also achieves
competitive performance in attacking several widely-used defense mechanisms.
(HopSkipJumpAttack was named Boundary Attack++ in a previous version of the
preprint.
Uridylation and adenylation of RNAs.
The posttranscriptional addition of nontemplated nucleotides to the 3' ends of RNA molecules can have a significant impact on their stability and biological function. It has been recently discovered that nontemplated addition of uridine or adenosine to the 3' ends of RNAs occurs in different organisms ranging from algae to humans, and on different kinds of RNAs, such as histone mRNAs, mRNA fragments, U6 snRNA, mature small RNAs and their precursors etc. These modifications may lead to different outcomes, such as increasing RNA decay, promoting or inhibiting RNA processing, or changing RNA activity. Growing pieces of evidence have revealed that such modifications can be RNA sequence-specific and subjected to temporal or spatial regulation in development. RNA tailing and its outcomes have been associated with human diseases such as cancer. Here, we review recent developments in RNA uridylation and adenylation and discuss the future prospects in this research area
Language-Based Image Editing with Recurrent Attentive Models
We investigate the problem of Language-Based Image Editing (LBIE). Given a
source image and a natural language description, we want to generate a target
image by editing the source image based on the description. We propose a
generic modeling framework for two sub-tasks of LBIE: language-based image
segmentation and image colorization. The framework uses recurrent attentive
models to fuse image and language features. Instead of using a fixed step size,
we introduce for each region of the image a termination gate to dynamically
determine after each inference step whether to continue extrapolating
additional information from the textual description. The effectiveness of the
framework is validated on three datasets. First, we introduce a synthetic
dataset, called CoSaL, to evaluate the end-to-end performance of our LBIE
system. Second, we show that the framework leads to state-of-the-art
performance on image segmentation on the ReferIt dataset. Third, we present the
first language-based colorization result on the Oxford-102 Flowers dataset.Comment: Accepted to CVPR 2018 as a Spotligh
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