170 research outputs found
Bayesian detection of embryonic gene expression onset in C. elegans
To study how a zygote develops into an embryo with different tissues,
large-scale 4D confocal movies of C. elegans embryos have been produced
recently by experimental biologists. However, the lack of principled
statistical methods for the highly noisy data has hindered the comprehensive
analysis of these data sets. We introduced a probabilistic change point model
on the cell lineage tree to estimate the embryonic gene expression onset time.
A Bayesian approach is used to fit the 4D confocal movies data to the model.
Subsequent classification methods are used to decide a model selection
threshold and further refine the expression onset time from the branch level to
the specific cell time level. Extensive simulations have shown the high
accuracy of our method. Its application on real data yields both previously
known results and new findings.Comment: Published at http://dx.doi.org/10.1214/15-AOAS820 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation
Target-guided open-domain conversation aims to proactively and naturally
guide a dialogue agent or human to achieve specific goals, topics or keywords
during open-ended conversations. Existing methods mainly rely on single-turn
datadriven learning and simple target-guided strategy without considering
semantic or factual knowledge relations among candidate topics/keywords. This
results in poor transition smoothness and low success rate. In this work, we
adopt a structured approach that controls the intended content of system
responses by introducing coarse-grained keywords, attains smooth conversation
transition through turn-level supervised learning and knowledge relations
between candidate keywords, and drives an conversation towards an specified
target with discourse-level guiding strategy. Specially, we propose a novel
dynamic knowledge routing network (DKRN) which considers semantic knowledge
relations among candidate keywords for accurate next topic prediction of next
discourse. With the help of more accurate keyword prediction, our
keyword-augmented response retrieval module can achieve better retrieval
performance and more meaningful conversations. Besides, we also propose a novel
dual discourse-level target-guided strategy to guide conversations to reach
their goals smoothly with higher success rate. Furthermore, to push the
research boundary of target-guided open-domain conversation to match real-world
scenarios better, we introduce a new large-scale Chinese target-guided
open-domain conversation dataset (more than 900K conversations) crawled from
Sina Weibo. Quantitative and human evaluations show our method can produce
meaningful and effective target-guided conversations, significantly improving
over other state-of-the-art methods by more than 20% in success rate and more
than 0.6 in average smoothness score.Comment: 8 pages, 2 figues, 6tables, AAAI2020, fix our model's abbreviatio
Enhance the Visual Representation via Discrete Adversarial Training
Adversarial Training (AT), which is commonly accepted as one of the most
effective approaches defending against adversarial examples, can largely harm
the standard performance, thus has limited usefulness on industrial-scale
production and applications. Surprisingly, this phenomenon is totally opposite
in Natural Language Processing (NLP) task, where AT can even benefit for
generalization. We notice the merit of AT in NLP tasks could derive from the
discrete and symbolic input space. For borrowing the advantage from NLP-style
AT, we propose Discrete Adversarial Training (DAT). DAT leverages VQGAN to
reform the image data to discrete text-like inputs, i.e. visual words. Then it
minimizes the maximal risk on such discrete images with symbolic adversarial
perturbations. We further give an explanation from the perspective of
distribution to demonstrate the effectiveness of DAT. As a plug-and-play
technique for enhancing the visual representation, DAT achieves significant
improvement on multiple tasks including image classification, object detection
and self-supervised learning. Especially, the model pre-trained with Masked
Auto-Encoding (MAE) and fine-tuned by our DAT without extra data can get 31.40
mCE on ImageNet-C and 32.77% top-1 accuracy on Stylized-ImageNet, building the
new state-of-the-art. The code will be available at
https://github.com/alibaba/easyrobust.Comment: Accepted to NeurIPS 2022, https://github.com/alibaba/easyrobus
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