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Found 16 papers, 9 papers with code
Friendly Topic Assistant for Transformer Based Abstractive Summarization
Abstractive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance.
keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM
Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering.
Knowledge-Aware Bayesian Deep Topic Model
We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling.
Ordinal Graph Gamma Belief Network for Social Recommender Systems
To build recommender systems that not only consider user-item interactions represented as ordinal variables, but also exploit the social network describing the relationships between the users, we develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions.
MDMLP: Image Classification from Scratch on Small Datasets with MLP
To resolve it, we present (i) multi-dimensional MLP (MDMLP), a conceptually simple and lightweight MLP-based architecture yet achieves SOTA when training from scratch on small-size datasets; (ii) multi-dimension MLP Attention Tool (MDAttnTool), a novel and efficient attention mechanism based on MLPs.
A Variational Edge Partition Model for Supervised Graph Representation Learning
This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities, each of which contributes to the edges via a logical OR mechanism.
TopicNet: Semantic Graph-Guided Topic Discovery
Existing deep hierarchical topic models are able to extract semantically meaningful topics from a text corpus in an unsupervised manner and automatically organize them into a topic hierarchy.
Edge Partition Modulated Graph Convolutional Networks
In this paper, we introduce a relational graph generative process to model how the observed edges are generated by aggregating the node interactions over multiple overlapping node communities, each of which represents a particular type of relation that contributes to the edges via a logical OR mechanism.
Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning
Compared to the larger pre-trained model and the tabular-specific pre-trained model, our approach is still competitive.
EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering
As a result, the backbone learns the shared knowledge among all clusters while modulated weights extract the cluster-specific features.
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ignoring the dependencies between the topics both at the same layer and across different layers.
Incentive-based Decentralized Routing for Connected and Autonomous Vehicles using Information Propagation
The first stage incorporates a decentralized local route switching dynamical system to approximate the system optimal route flow in a local area based on vehicles' knowledge of local traffic information.
Autonomous Vehicles
Physics and Society
Computer Science and Game Theory
Systems and Control
Systems and Control
Bidirectional Convolutional Poisson Gamma Dynamical Systems
Incorporating the natural document-sentence-word structure into hierarchical Bayesian modeling, we propose convolutional Poisson gamma dynamical systems (PGDS) that introduce not only word-level probabilistic convolutions, but also sentence-level stochastic temporal transitions.
Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network
To analyze a collection of interconnected documents, relational topic models (RTMs) have been developed to describe both the link structure and document content, exploring their underlying relationships via a single-layer latent representation with limited expressive capability.
Variational Temporal Deep Generative Model for Radar HRRP Target Recognition
We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP), which characterizes the temporal dependence across the range cells of HRRP.
Convolutional Poisson Gamma Belief Network
For text analysis, one often resorts to a lossy representation that either completely ignores word order or embeds each word as a low-dimensional dense feature vector.
