126 research outputs found
Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation
Accepted by COLING 2020, final camera ready versionPreprin
A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
Deep learning based image segmentation has achieved the state-of-the-art
performance in many medical applications such as lesion quantification, organ
detection, etc. However, most of the methods rely on supervised learning, which
require a large set of high-quality labeled data. Data annotation is generally
an extremely time-consuming process. To address this problem, we propose a
generic semi-supervised learning framework for image segmentation based on a
deep convolutional neural network (DCNN). An encoder-decoder based DCNN is
initially trained using a few annotated training samples. This initially
trained model is then copied into sub-models and improved iteratively using
random subsets of unlabeled data with pseudo labels generated from models
trained in the previous iteration. The number of sub-models is gradually
decreased to one in the final iteration. We evaluate the proposed method on a
public grand-challenge dataset for skin lesion segmentation. Our method is able
to significantly improve beyond fully supervised model learning by
incorporating unlabeled data.Comment: Accepted for publication at IEEE International Symposium on
Biomedical Imaging (ISBI) 202
Symmetric Sparse Boolean Matrix Factorization and Applications
In this work, we study a variant of nonnegative matrix factorization where we
wish to find a symmetric factorization of a given input matrix into a sparse,
Boolean matrix. Formally speaking, given ,
we want to find such that is minimized among all for which
each row is -sparse. This question turns out to be closely related to a
number of questions like recovering a hypergraph from its line graph, as well
as reconstruction attacks for private neural network training.
As this problem is hard in the worst-case, we study a natural average-case
variant that arises in the context of these reconstruction attacks: for a random Boolean matrix with
-sparse rows, and the goal is to recover up to column
permutation. Equivalently, this can be thought of as recovering a uniformly
random -uniform hypergraph from its line graph.
Our main result is a polynomial-time algorithm for this problem based on
bootstrapping higher-order information about and then decomposing
an appropriate tensor. The key ingredient in our analysis, which may be of
independent interest, is to show that such a matrix has full
column rank with high probability as soon as , which
we do using tools from Littlewood-Offord theory and estimates for binary
Krawtchouk polynomials.Comment: 33 pages, to appear in Innovations in Theoretical Computer Science
(ITCS 2022), v2: updated ref
MIR-GAN : Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition
PreprintPublisher PD
Noise-aware Speech Enhancement using Diffusion Probabilistic Model
5 pages, 2 figuresPreprin
A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
Acknowledgment This work is supported by the award made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P011829/1).PreprintPublisher PD
Postnatal ontogenesis of clock genes in mouse suprachiasmatic nucleus and heart
<p>Abstract</p> <p>Background</p> <p>The master clock within the hypothalamic suprachiasmatic nucleus (SCN) synchronizing clocks in peripheral tissues is entrained by the environmental condition, such as the light-dark (LD) cycle. The mechanisms of circadian clockwork are similar in both SCN and peripheral tissues. The aim of the present work was to observe the profiles of clock genes expression in mouse central and peripheral tissues within postnatal day 5 (P5). The daily expression of four clock genes mRNA (Bmal1, Per2, Cry1 and Rev-erb alpha) in mouse SCN and heart was measured at P1, P3 and P5 by real-time PCR.</p> <p>Results</p> <p>All the studied mice clock genes began to express in a circadian rhythms manner in heart and SCN at P3 and P5 respectively. Interestingly, the daily rhythmic phase of some clock genes shifted during the postnatal days. Moreover, the expressions of clock genes in heart were not synchronized with those in SCN until at P5.</p> <p>Conclusion</p> <p>The data showed the gradual development of clock genes in SCN and a peripheral tissue, and suggested that development of clock genes differed between in the SCN and the heart. Judging from the mRNA expression, it was possible that the central clock synchronized the peripheral clock as early as P5.</p
Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition
12 pages, 5 figures, Accepted by IJCAI 2023Preprin
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