Neural Information Processing Systems http://nips.cc/

Despite many years of research into latent Dirichlet allocation (LDA), applying LDA to collections of non-categorical items is still challenging for practitioners. Yet many problems with much richer data share a similar structure and could benefit from the vast literature on LDA. We propose logistic LDA, a novel discriminative variant of latent Dirichlet allocation which is easy to apply to arbitrary inputs. In particular, our model can easily be applied to groups of images, arbitrary text embeddings, or integrate deep neural networks. Although it is a discriminative model, we show that logistic LDA can learn from unlabeled data in an unsupervised manner by exploiting the group structure present in the data. In contrast to other recent topic models designed to handle arbitrary inputs, our model does not sacrifice the interpretability and principled motivation of LDA.

We present a novel approach for LDA (Latent Dirichlet Allocation) topic reconstruction.

Topic modelling in Natural Language Processing uncovers hidden topics in large, unlabelled text datasets. It is widely applied in fields such as information retrieval, content summarisation, and trend analysis across various disciplines. However, probabilistic topic models can produce different results when rerun due to their stochastic nature, leading to inconsistencies in latent topics. Factors like corpus shuffling, rare text removal, and document elimination contribute to these variations. This instability affects replicability, reliability, and interpretation, raising concerns about whether topic models capture meaningful topics or just noise. To address these problems, we defined a new stability measure that incorporates accuracy and consistency and uses the generative properties of LDA to generate a new corpus with ground truth. These generated corpora are run through LDA 50 times to determine the variability in the output. We show that LDA can correctly determine the underlying number of topics in the documents. We also find that LDA is more internally consistent, as the multiple reruns return similar topics; however, these topics are not the true topics.

While traditional probabilistic models such as Latent Dirichlet Allocation (LDA) (Blei et al., 2003) have been foundational, their underlying bag - of - words assumption limits their ability to capture complex semantics. A recent paradigm shift towards models like BERTopic (Grootendorst, 2022), a state - of - the - art (SOTA) model which leverages contextualized embeddings from pre - trained transformers, has shown significant promise in generating more semantically coherent topics. These models can capture nuanced relationships, including domain - speci fic named entities and morphologically rich constructs, critical for linguistically complex data. However, despite this progress, two significant gaps persist in literature. First, research has overwhelmingly focused on high - resource, standardized languages, with a lot of scope left for under - resourced languages to be unexplored. This focus not only limits the generalizability of existing models but also risks perp etuating a technological bias where the nuances of smaller linguistic communities are overlooked. Models trained on standard corpora often fail to capture the unique lexical and semantic patterns of regional dialects or sociolects, leading to a superficial or even inaccurate understanding of the underlying discourse (Kamilo g lu, 2025) . Second, the predominant application domain has been structured or short - form text like news articles or social media posts (Egger et al., 2022; Schäfer et al., 2024), while the challenges of modeling unstructured, open - ended personal narratives have received less attention. Distinct from the short - form, often decontextualized nature of social media data, daily narratives provide granular, contextually - grounded accounts of lived experience.

Traditional topic models such as Latent Dirichlet Allocation (LDA) have been widely used to uncover latent structures in text corpora, but they often struggle to integrate auxiliary information such as metadata, user attributes, or document labels. These limitations restrict their expressiveness, personalization, and interpretability. To address this, we propose nnLDA, a neural-augmented probabilistic topic model that dynamically incorporates side information through a neural prior mechanism. nnLDA models each document as a mixture of latent topics, where the prior over topic proportions is generated by a neural network conditioned on auxiliary features. This design allows the model to capture complex nonlinear interactions between side information and topic distributions that static Dirichlet priors cannot represent. We develop a stochastic variational Expectation-Maximization algorithm to jointly optimize the neural and probabilistic components. Across multiple benchmark datasets, nnLDA consistently outperforms LDA and Dirichlet-Multinomial Regression in topic coherence, perplexity, and downstream classification. These results highlight the benefits of combining neural representation learning with probabilistic topic modeling in settings where side information is available.

Protecting cloud applications is critical in an era where security threats are increasingly sophisticated and persistent. Continuous Integration and Continuous Deployment (CI/CD) pipelines are particularly vulnerable, making innovative security approaches essential. This research explores the application of Natural Language Processing (NLP) techniques, specifically Topic Modelling, to analyse security-related text data and anticipate potential threats. We focus on Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) to extract meaningful patterns from data sources, including logs, reports, and deployment traces. Using the Gensim framework in Python, these methods categorise log entries into security-relevant topics (e.g., phishing, encryption failures). The identified topics are leveraged to highlight patterns indicative of security issues across CI/CD's continuous stages (build, test, deploy). This approach introduces a semantic layer that supports early vulnerability recognition and contextual understanding of runtime behaviours.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors describe a two novel inference methods for the correlated topic model (CTM). They build on analytic results for the conditional logistic normal likelihood to arrive at a fast, easily parallelized exact inference. This leads to an approximate sampling method for producing Polya-Gamma variates. Finally, they propose a method for efficiently drawing samples in the presence of sparsity.