9,843 research outputs found
Relaxations for inference in restricted Boltzmann machines
We propose a relaxation-based approximate inference algorithm that samples
near-MAP configurations of a binary pairwise Markov random field. We experiment
on MAP inference tasks in several restricted Boltzmann machines. We also use
our underlying sampler to estimate the log-partition function of restricted
Boltzmann machines and compare against other sampling-based methods.Comment: ICLR 2014 workshop track submissio
Naturalizing a Programming Language via Interactive Learning
Our goal is to create a convenient natural language interface for performing
well-specified but complex actions such as analyzing data, manipulating text,
and querying databases. However, existing natural language interfaces for such
tasks are quite primitive compared to the power one wields with a programming
language. To bridge this gap, we start with a core programming language and
allow users to "naturalize" the core language incrementally by defining
alternative, more natural syntax and increasingly complex concepts in terms of
compositions of simpler ones. In a voxel world, we show that a community of
users can simultaneously teach a common system a diverse language and use it to
build hundreds of complex voxel structures. Over the course of three days,
these users went from using only the core language to using the naturalized
language in 85.9\% of the last 10K utterances.Comment: 10 pages, ACL201
A Novel Method for the Absolute Pose Problem with Pairwise Constraints
Absolute pose estimation is a fundamental problem in computer vision, and it
is a typical parameter estimation problem, meaning that efforts to solve it
will always suffer from outlier-contaminated data. Conventionally, for a fixed
dimensionality d and the number of measurements N, a robust estimation problem
cannot be solved faster than O(N^d). Furthermore, it is almost impossible to
remove d from the exponent of the runtime of a globally optimal algorithm.
However, absolute pose estimation is a geometric parameter estimation problem,
and thus has special constraints. In this paper, we consider pairwise
constraints and propose a globally optimal algorithm for solving the absolute
pose estimation problem. The proposed algorithm has a linear complexity in the
number of correspondences at a given outlier ratio. Concretely, we first
decouple the rotation and the translation subproblems by utilizing the pairwise
constraints, and then we solve the rotation subproblem using the
branch-and-bound algorithm. Lastly, we estimate the translation based on the
known rotation by using another branch-and-bound algorithm. The advantages of
our method are demonstrated via thorough testing on both synthetic and
real-world dataComment: 10 pages, 7figure
Simplicial volume and fillings of hyperbolic manifolds
Let M be a hyperbolic n-manifold whose cusps have torus cross-sections. In
arXiv:0901.0056, the authors constructed a variety of nonpositively and
negatively curved spaces as "2\pi-fillings" of M by replacing the cusps of M
with compact "partial cones" of their boundaries. These 2\pi-fillings are
closed pseudomanifolds, and so have a fundamental class. We show that the
simplicial volume of any such 2\pi-filling is positive, and bounded above by
Vol(M)/v_n, where v_n is the volume of a regular ideal hyperbolic n-simplex.
This result generalizes the fact that hyperbolic Dehn filling of a 3-manifold
does not increase hyperbolic volume.
In particular, we obtain information about the simplicial volumes of some
4--dimensional homology spheres described by Ratcliffe and Tschantz, answering
a question of Belegradek and establishing the existence of 4--dimensional
homology spheres with positive simplicial volume.Comment: 22 pages; version 2 points out the application to the homology
spheres of Ratcliffe and Tschantz, and makes some other small changes
suggested by the refere
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
Computer-aided pathology diagnosis based on the classification of Whole Slide
Image (WSI) plays an important role in clinical practice, and it is often
formulated as a weakly-supervised Multiple Instance Learning (MIL) problem.
Existing methods solve this problem from either a bag classification or an
instance classification perspective. In this paper, we propose an end-to-end
weakly supervised knowledge distillation framework (WENO) for WSI
classification, which integrates a bag classifier and an instance classifier in
a knowledge distillation framework to mutually improve the performance of both
classifiers. Specifically, an attention-based bag classifier is used as the
teacher network, which is trained with weak bag labels, and an instance
classifier is used as the student network, which is trained using the
normalized attention scores obtained from the teacher network as soft pseudo
labels for the instances in positive bags. An instance feature extractor is
shared between the teacher and the student to further enhance the knowledge
exchange between them. In addition, we propose a hard positive instance mining
strategy based on the output of the student network to force the teacher
network to keep mining hard positive instances. WENO is a plug-and-play
framework that can be easily applied to any existing attention-based bag
classification methods. Extensive experiments on five datasets demonstrate the
efficiency of WENO. Code is available at https://github.com/miccaiif/WENO.Comment: Accepted by NeurIPS 202
- …