Titlebook: Machine Learning and Knowledge Discovery in Databases: Research Track; European Conference, Danai Koutra,Claudia Plant,Francesco Bonchi Con

sacrum

COLON

Improving Autoregressive NLP Tasks via Modular Linearized Attentioner resource-constrained environment. While prior research has reduced the size of these models, increasing computational efficiency without considerable performance impacts remains difficult, especially for autoregressive tasks. This paper proposes ., which combines multiple efficient attention mech

Flounder

Legion

flutter

Encouraging Sparsity in Neural Topic Modeling with Non-Mean-Field Inferenceve model. Recently, researchers have explored the use of variational autoencoders (VAE) to improve the performance of LDA. However, there remain two major limitations: (1) the Dirichlet prior is inadequate to extract precise semantic information in VAE-LDA models, as it introduces a trade-off betwee

Endoscope

The Metric is the Message: Benchmarking Challenges for Neural Symbolic Regressiony favorable performance for the method using it but making comparison between methods difficult. Here we compare the performance of several NSR methods using diverse metrics reported in the literature, and some of our own devising. We show that reliance on a single metric can hide an NSR method’s sh

MIRE

Credence

Neural Class Expression Synthesis in of the search process invokes a reasoner and a heuristic function. The reasoner finds the instances of the current expression, and the heuristic function computes the information gain and decides on the next step to be taken. As the size of the background knowledge base grows, search-based approache

Dissonance

Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approachepresentations, it has difficulties in coping with structural changes in its environment (such as increasing the number of objects). Meanwhile, Relational Reinforcement Learning inherits the relational representations from symbolic planning to learn reusable policies. However, it has so far been una

Ondines-curse

ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extractionocabulary, which is generally in the form of a Knowledge Graph (KG) or an ontology. Various approaches have been proposed so far to address this task. However, applying these techniques to biomedical text often yields unsatisfactory results because it is hard to infer relations directly from sentenc