56 research outputs found
Probabilistic Approach to Structural Change Prediction in Evolving Social Networks
We propose a predictive model of structural
changes in elementary subgraphs of social network based on
Mixture of Markov Chains. The model is trained and verified
on a dataset from a large corporate social network analyzed
in short, one day-long time windows, and reveals distinctive
patterns of evolution of connections on the level of local
network topology. We argue that the network investigated in
such short timescales is highly dynamic and therefore immune
to classic methods of link prediction and structural analysis,
and show that in the case of complex networks, the dynamic
subgraph mining may lead to better prediction accuracy. The
experiments were carried out on the logs from the Wroclaw
University of Technology mail server
Neural Architecture for Online Ensemble Continual Learning
Continual learning with an increasing number of classes is a challenging
task. The difficulty rises when each example is presented exactly once, which
requires the model to learn online. Recent methods with classic parameter
optimization procedures have been shown to struggle in such setups or have
limitations like non-differentiable components or memory buffers. For this
reason, we present the fully differentiable ensemble method that allows us to
efficiently train an ensemble of neural networks in the end-to-end regime. The
proposed technique achieves SOTA results without a memory buffer and clearly
outperforms the reference methods. The conducted experiments have also shown a
significant increase in the performance for small ensembles, which demonstrates
the capability of obtaining relatively high classification accuracy with a
reduced number of classifiers
Similarity-based Memory Enhanced Joint Entity and Relation Extraction
Document-level joint entity and relation extraction is a challenging
information extraction problem that requires a unified approach where a single
neural network performs four sub-tasks: mention detection, coreference
resolution, entity classification, and relation extraction. Existing methods
often utilize a sequential multi-task learning approach, in which the arbitral
decomposition causes the current task to depend only on the previous one,
missing the possible existence of the more complex relationships between them.
In this paper, we present a multi-task learning framework with bidirectional
memory-like dependency between tasks to address those drawbacks and perform the
joint problem more accurately. Our empirical studies show that the proposed
approach outperforms the existing methods and achieves state-of-the-art results
on the BioCreative V CDR corpus
Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks
State-of-the-art models can perform well in controlled environments, but they
often struggle when presented with out-of-distribution (OOD) examples, making
OOD detection a critical component of NLP systems. In this paper, we focus on
highlighting the limitations of existing approaches to OOD detection in NLP.
Specifically, we evaluated eight OOD detection methods that are easily
integrable into existing NLP systems and require no additional OOD data or
model modifications. One of our contributions is providing a well-structured
research environment that allows for full reproducibility of the results.
Additionally, our analysis shows that existing OOD detection methods for NLP
tasks are not yet sufficiently sensitive to capture all samples characterized
by various types of distributional shifts. Particularly challenging testing
scenarios arise in cases of background shift and randomly shuffled word order
within in domain texts. This highlights the need for future work to develop
more effective OOD detection approaches for the NLP problems, and our work
provides a well-defined foundation for further research in this area.Comment: 11 pages, 3 figures, Association for Computational Linguistic
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