6,395 research outputs found
Finding Likely Errors with Bayesian Specifications
We present a Bayesian framework for learning probabilistic specifications
from large, unstructured code corpora, and a method to use this framework to
statically detect anomalous, hence likely buggy, program behavior. The
distinctive insight here is to build a statistical model that correlates all
specifications hidden inside a corpus with the syntax and observed behavior of
programs that implement these specifications. During the analysis of a
particular program, this model is conditioned into a posterior distribution
that prioritizes specifications that are relevant to this program. This allows
accurate program analysis even if the corpus is highly heterogeneous. The
problem of finding anomalies is now framed quantitatively, as a problem of
computing a distance between a "reference distribution" over program behaviors
that our model expects from the program, and the distribution over behaviors
that the program actually produces.
We present a concrete embodiment of our framework that combines a topic model
and a neural network model to learn specifications, and queries the learned
models to compute anomaly scores. We evaluate this implementation on the task
of detecting anomalous usage of Android APIs. Our encouraging experimental
results show that the method can automatically discover subtle errors in
Android applications in the wild, and has high precision and recall compared to
competing probabilistic approaches
Extreme Extraction: Only One Hour per Relation
Information Extraction (IE) aims to automatically generate a large knowledge
base from natural language text, but progress remains slow. Supervised learning
requires copious human annotation, while unsupervised and weakly supervised
approaches do not deliver competitive accuracy. As a result, most fielded
applications of IE, as well as the leading TAC-KBP systems, rely on significant
amounts of manual engineering. Even "Extreme" methods, such as those reported
in Freedman et al. 2011, require about 10 hours of expert labor per relation.
This paper shows how to reduce that effort by an order of magnitude. We
present a novel system, InstaRead, that streamlines authoring with an ensemble
of methods: 1) encoding extraction rules in an expressive and compositional
representation, 2) guiding the user to promising rules based on corpus
statistics and mined resources, and 3) introducing a new interactive
development cycle that provides immediate feedback --- even on large datasets.
Experiments show that experts can create quality extractors in under an hour
and even NLP novices can author good extractors. These extractors equal or
outperform ones obtained by comparably supervised and state-of-the-art
distantly supervised approaches
Ongoing Emergence: A Core Concept in Epigenetic Robotics
We propose ongoing emergence as a core concept in
epigenetic robotics. Ongoing emergence refers to the
continuous development and integration of new skills
and is exhibited when six criteria are satisfied: (1)
continuous skill acquisition, (2) incorporation of new
skills with existing skills, (3) autonomous development
of values and goals, (4) bootstrapping of initial skills, (5)
stability of skills, and (6) reproducibility. In this paper
we: (a) provide a conceptual synthesis of ongoing
emergence based on previous theorizing, (b) review
current research in epigenetic robotics in light of ongoing
emergence, (c) provide prototypical examples of ongoing
emergence from infant development, and (d) outline
computational issues relevant to creating robots
exhibiting ongoing emergence
RAESON: A Tool for Reasoning Tasks Driven by Interactive Visualization of Logical Structure
The paper presents a software tool for analysis and interactive engagement in
various logical reasoning tasks. A first feature of the program consists in
providing an interface for working with logic-specific repositories of formal
knowledge. A second feature provides the means to intuitively visualize and
interactively generate the underlying logical structure that propels customary
logical reasoning tasks. Starting from this we argue that both aspects have
didactic potential and can be integrated in teaching activities to provide an
engaging learning experience.Comment: Proceedings of the Fourth International Conference on Tools for
Teaching Logic (TTL2015), Rennes, France, June 9-12, 2015. Editors: M.
Antonia Huertas, Jo\~ao Marcos, Mar\'ia Manzano, Sophie Pinchinat,
Fran\c{c}ois Schwarzentrube
Enabling Open-World Specification Mining via Unsupervised Learning
Many programming tasks require using both domain-specific code and
well-established patterns (such as routines concerned with file IO). Together,
several small patterns combine to create complex interactions. This compounding
effect, mixed with domain-specific idiosyncrasies, creates a challenging
environment for fully automatic specification inference. Mining specifications
in this environment, without the aid of rule templates, user-directed feedback,
or predefined API surfaces, is a major challenge. We call this challenge
Open-World Specification Mining.
In this paper, we present a framework for mining specifications and usage
patterns in an Open-World setting. We design this framework to be
miner-agnostic and instead focus on disentangling complex and noisy API
interactions. To evaluate our framework, we introduce a benchmark of 71
clusters extracted from five open-source projects. Using this dataset, we show
that interesting clusters can be recovered, in a fully automatic way, by
leveraging unsupervised learning in the form of word embeddings. Once clusters
have been recovered, the challenge of Open-World Specification Mining is
simplified and any trace-based mining technique can be applied. In addition, we
provide a comprehensive evaluation of three word-vector learners to showcase
the value of sub-word information for embeddings learned in the
software-engineering domain
Grounding Language for Transfer in Deep Reinforcement Learning
In this paper, we explore the utilization of natural language to drive
transfer for reinforcement learning (RL). Despite the wide-spread application
of deep RL techniques, learning generalized policy representations that work
across domains remains a challenging problem. We demonstrate that textual
descriptions of environments provide a compact intermediate channel to
facilitate effective policy transfer. Specifically, by learning to ground the
meaning of text to the dynamics of the environment such as transitions and
rewards, an autonomous agent can effectively bootstrap policy learning on a new
domain given its description. We employ a model-based RL approach consisting of
a differentiable planning module, a model-free component and a factorized state
representation to effectively use entity descriptions. Our model outperforms
prior work on both transfer and multi-task scenarios in a variety of different
environments. For instance, we achieve up to 14% and 11.5% absolute improvement
over previously existing models in terms of average and initial rewards,
respectively.Comment: JAIR 201
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
Recent literature suggests that averaged word vectors followed by simple
post-processing outperform many deep learning methods on semantic textual
similarity tasks. Furthermore, when averaged word vectors are trained
supervised on large corpora of paraphrases, they achieve state-of-the-art
results on standard STS benchmarks. Inspired by these insights, we push the
limits of word embeddings even further. We propose a novel fuzzy bag-of-words
(FBoW) representation for text that contains all the words in the vocabulary
simultaneously but with different degrees of membership, which are derived from
similarities between word vectors. We show that max-pooled word vectors are
only a special case of fuzzy BoW and should be compared via fuzzy Jaccard index
rather than cosine similarity. Finally, we propose DynaMax, a completely
unsupervised and non-parametric similarity measure that dynamically extracts
and max-pools good features depending on the sentence pair. This method is both
efficient and easy to implement, yet outperforms current baselines on STS tasks
by a large margin and is even competitive with supervised word vectors trained
to directly optimise cosine similarity.Comment: Published as a conference paper at ICLR 201
Visual Semantic Planning using Deep Successor Representations
A crucial capability of real-world intelligent agents is their ability to
plan a sequence of actions to achieve their goals in the visual world. In this
work, we address the problem of visual semantic planning: the task of
predicting a sequence of actions from visual observations that transform a
dynamic environment from an initial state to a goal state. Doing so entails
knowledge about objects and their affordances, as well as actions and their
preconditions and effects. We propose learning these through interacting with a
visual and dynamic environment. Our proposed solution involves bootstrapping
reinforcement learning with imitation learning. To ensure cross task
generalization, we develop a deep predictive model based on successor
representations. Our experimental results show near optimal results across a
wide range of tasks in the challenging THOR environment.Comment: ICCV 2017 camera read
Automata Guided Reinforcement Learning With Demonstrations
Tasks with complex temporal structures and long horizons pose a challenge for
reinforcement learning agents due to the difficulty in specifying the tasks in
terms of reward functions as well as large variances in the learning signals.
We propose to address these problems by combining temporal logic (TL) with
reinforcement learning from demonstrations. Our method automatically generates
intrinsic rewards that align with the overall task goal given a TL task
specification. The policy resulting from our framework has an interpretable and
hierarchical structure. We validate the proposed method experimentally on a set
of robotic manipulation tasks
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