18,349 research outputs found
A Map of Update Constraints in Inductive Inference
We investigate how different learning restrictions reduce learning power and
how the different restrictions relate to one another. We give a complete map
for nine different restrictions both for the cases of complete information
learning and set-driven learning. This completes the picture for these
well-studied \emph{delayable} learning restrictions. A further insight is
gained by different characterizations of \emph{conservative} learning in terms
of variants of \emph{cautious} learning.
Our analyses greatly benefit from general theorems we give, for example
showing that learners with exclusively delayable restrictions can always be
assumed total.Comment: fixed a mistake in Theorem 21, result is the sam
Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs
This paper introduces a novel algorithm for transductive inference in
higher-order MRFs, where the unary energies are parameterized by a variable
classifier. The considered task is posed as a joint optimization problem in the
continuous classifier parameters and the discrete label variables. In contrast
to prior approaches such as convex relaxations, we propose an advantageous
decoupling of the objective function into discrete and continuous subproblems
and a novel, efficient optimization method related to ADMM. This approach
preserves integrality of the discrete label variables and guarantees global
convergence to a critical point. We demonstrate the advantages of our approach
in several experiments including video object segmentation on the DAVIS data
set and interactive image segmentation
Inferring Algebraic Effects
We present a complete polymorphic effect inference algorithm for an ML-style
language with handlers of not only exceptions, but of any other algebraic
effect such as input & output, mutable references and many others. Our main aim
is to offer the programmer a useful insight into the effectful behaviour of
programs. Handlers help here by cutting down possible effects and the resulting
lengthy output that often plagues precise effect systems. Additionally, we
present a set of methods that further simplify the displayed types, some even
by deliberately hiding inferred information from the programmer
Inductive Visual Localisation: Factorised Training for Superior Generalisation
End-to-end trained Recurrent Neural Networks (RNNs) have been successfully
applied to numerous problems that require processing sequences, such as image
captioning, machine translation, and text recognition. However, RNNs often
struggle to generalise to sequences longer than the ones encountered during
training. In this work, we propose to optimise neural networks explicitly for
induction. The idea is to first decompose the problem in a sequence of
inductive steps and then to explicitly train the RNN to reproduce such steps.
Generalisation is achieved as the RNN is not allowed to learn an arbitrary
internal state; instead, it is tasked with mimicking the evolution of a valid
state. In particular, the state is restricted to a spatial memory map that
tracks parts of the input image which have been accounted for in previous
steps. The RNN is trained for single inductive steps, where it produces updates
to the memory in addition to the desired output. We evaluate our method on two
different visual recognition problems involving visual sequences: (1) text
spotting, i.e. joint localisation and reading of text in images containing
multiple lines (or a block) of text, and (2) sequential counting of objects in
aerial images. We show that inductive training of recurrent models enhances
their generalisation ability on challenging image datasets.Comment: In BMVC 2018 (spotlight
Bounded Refinement Types
We present a notion of bounded quantification for refinement types and show
how it expands the expressiveness of refinement typing by using it to develop
typed combinators for: (1) relational algebra and safe database access, (2)
Floyd-Hoare logic within a state transformer monad equipped with combinators
for branching and looping, and (3) using the above to implement a refined IO
monad that tracks capabilities and resource usage. This leap in expressiveness
comes via a translation to "ghost" functions, which lets us retain the
automated and decidable SMT based checking and inference that makes refinement
typing effective in practice.Comment: 14 pages, International Conference on Functional Programming, ICFP
201
Synthesizing Short-Circuiting Validation of Data Structure Invariants
This paper presents incremental verification-validation, a novel approach for
checking rich data structure invariants expressed as separation logic
assertions. Incremental verification-validation combines static verification of
separation properties with efficient, short-circuiting dynamic validation of
arbitrarily rich data constraints. A data structure invariant checker is an
inductive predicate in separation logic with an executable interpretation; a
short-circuiting checker is an invariant checker that stops checking whenever
it detects at run time that an assertion for some sub-structure has been fully
proven statically. At a high level, our approach does two things: it statically
proves the separation properties of data structure invariants using a static
shape analysis in a standard way but then leverages this proof in a novel
manner to synthesize short-circuiting dynamic validation of the data
properties. As a consequence, we enable dynamic validation to make up for
imprecision in sound static analysis while simultaneously leveraging the static
verification to make the remaining dynamic validation efficient. We show
empirically that short-circuiting can yield asymptotic improvements in dynamic
validation, with low overhead over no validation, even in cases where static
verification is incomplete
Literal Perceptual Inference
In this paper, I argue that theories of perception that appeal to Helmholtz’s idea of unconscious inference (“Helmholtzian” theories) should be taken literally, i.e. that the inferences appealed to in such theories are inferences in the full sense of the term, as employed elsewhere in philosophy and in ordinary discourse.
In the course of the argument, I consider constraints on inference based on the idea that inference is a deliberate acton, and on the idea that inferences depend on the syntactic structure of representations. I argue that inference is a personal-level but sometimes unconscious process that cannot in general be distinguished from association on the basis of the structures of the representations over which it’s defined. I also critique arguments against representationalist interpretations of Helmholtzian theories, and argue against the view that perceptual inference is encapsulated in a module
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