1,892 research outputs found
Learning-assisted Theorem Proving with Millions of Lemmas
Large formal mathematical libraries consist of millions of atomic inference
steps that give rise to a corresponding number of proved statements (lemmas).
Analogously to the informal mathematical practice, only a tiny fraction of such
statements is named and re-used in later proofs by formal mathematicians. In
this work, we suggest and implement criteria defining the estimated usefulness
of the HOL Light lemmas for proving further theorems. We use these criteria to
mine the large inference graph of the lemmas in the HOL Light and Flyspeck
libraries, adding up to millions of the best lemmas to the pool of statements
that can be re-used in later proofs. We show that in combination with
learning-based relevance filtering, such methods significantly strengthen
automated theorem proving of new conjectures over large formal mathematical
libraries such as Flyspeck.Comment: journal version of arXiv:1310.2797 (which was submitted to LPAR
conference
Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images
Efficient and easy segmentation of images and volumes is of great practical
importance. Segmentation problems that motivate our approach originate from
microscopy imaging commonly used in materials science, medicine, and biology.
We formulate image segmentation as a probabilistic pixel classification
problem, and we apply segmentation as a step towards characterising image
content. Our method allows the user to define structures of interest by
interactively marking a subset of pixels. Thanks to the real-time feedback, the
user can place new markings strategically, depending on the current outcome.
The final pixel classification may be obtained from a very modest user input.
An important ingredient of our method is a graph that encodes image content.
This graph is built in an unsupervised manner during initialisation and is
based on clustering of image features. Since we combine a limited amount of
user-labelled data with the clustering information obtained from the unlabelled
parts of the image, our method fits in the general framework of semi-supervised
learning. We demonstrate how this can be a very efficient approach to
segmentation through pixel classification.Comment: 9 pages, 7 figures, PDFLaTe
Mobile Video Object Detection with Temporally-Aware Feature Maps
This paper introduces an online model for object detection in videos designed
to run in real-time on low-powered mobile and embedded devices. Our approach
combines fast single-image object detection with convolutional long short term
memory (LSTM) layers to create an interweaved recurrent-convolutional
architecture. Additionally, we propose an efficient Bottleneck-LSTM layer that
significantly reduces computational cost compared to regular LSTMs. Our network
achieves temporal awareness by using Bottleneck-LSTMs to refine and propagate
feature maps across frames. This approach is substantially faster than existing
detection methods in video, outperforming the fastest single-frame models in
model size and computational cost while attaining accuracy comparable to much
more expensive single-frame models on the Imagenet VID 2015 dataset. Our model
reaches a real-time inference speed of up to 15 FPS on a mobile CPU.Comment: In CVPR 201
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