18,293 research outputs found
Lifelong Learning CRF for Supervised Aspect Extraction
This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain markedly better than the traditional CRF without using this prior
knowledge. The key innovation is that even after CRF training, the model can
still improve its extraction with experiences in its applications.Comment: Accepted at ACL 2017. arXiv admin note: text overlap with
arXiv:1612.0794
New Frontiers of Quantified Self: Finding New Ways for Engaging Users in Collecting and Using Personal Data
In spite of the fast growth in the market of devices and applications that allow people to collect personal information, Quantified Self (QS) tools still present a variety of issues when they are used in everyday lives of common people. In this workshop we aim at exploring new ways for designing QS systems, by gathering different researchers in a unique place for imagining how the tracking, management, interpretation and visualization of personal data could be addressed in the future
Learning Independent Causal Mechanisms
Statistical learning relies upon data sampled from a distribution, and we
usually do not care what actually generated it in the first place. From the
point of view of causal modeling, the structure of each distribution is induced
by physical mechanisms that give rise to dependences between observables.
Mechanisms, however, can be meaningful autonomous modules of generative models
that make sense beyond a particular entailed data distribution, lending
themselves to transfer between problems. We develop an algorithm to recover a
set of independent (inverse) mechanisms from a set of transformed data points.
The approach is unsupervised and based on a set of experts that compete for
data generated by the mechanisms, driving specialization. We analyze the
proposed method in a series of experiments on image data. Each expert learns to
map a subset of the transformed data back to a reference distribution. The
learned mechanisms generalize to novel domains. We discuss implications for
transfer learning and links to recent trends in generative modeling.Comment: ICML 201
Neural Topic Modeling with Continual Lifelong Learning
Lifelong learning has recently attracted attention in building machine
learning systems that continually accumulate and transfer knowledge to help
future learning. Unsupervised topic modeling has been popularly used to
discover topics from document collections. However, the application of topic
modeling is challenging due to data sparsity, e.g., in a small collection of
(short) documents and thus, generate incoherent topics and sub-optimal document
representations. To address the problem, we propose a lifelong learning
framework for neural topic modeling that can continuously process streams of
document collections, accumulate topics and guide future topic modeling tasks
by knowledge transfer from several sources to better deal with the sparse data.
In the lifelong process, we particularly investigate jointly: (1) sharing
generative homologies (latent topics) over lifetime to transfer prior
knowledge, and (2) minimizing catastrophic forgetting to retain the past
learning via novel selective data augmentation, co-training and topic
regularization approaches. Given a stream of document collections, we apply the
proposed Lifelong Neural Topic Modeling (LNTM) framework in modeling three
sparse document collections as future tasks and demonstrate improved
performance quantified by perplexity, topic coherence and information retrieval
task.Comment: ICML202
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