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Found 13 papers, 2 papers with code
Foundation Inference Models for Markov Jump Processes
Markov jump processes are continuous-time stochastic processes which describe dynamical systems evolving in discrete state spaces.
Foundational Inference Models for Dynamical Systems
The resulting models, which we call foundational inference models (FIM), can be (i) copied and matched along the time dimension to increase their resolution; and (ii) copied and composed to build inference models of any dimensionality, without the need of any finetuning.
Neural Dynamic Focused Topic Model
Topic models and all their variants analyse text by learning meaningful representations through word co-occurrences.
The future is different: Large pre-trained language models fail in prediction tasks
Our models display performance drops of only about 40% in the worst cases (2% in the best ones) when predicting the popularity of future posts, while using only about 7% of the total number of parameters of LPLM and providing interpretable representations that offer insight into real-world events, like the GameStop short squeeze of 2021
Hidden Schema Networks
Large, pretrained language models infer powerful representations that encode rich semantic and syntactic content, albeit implicitly.
Informed Pre-Training on Prior Knowledge
In this paper, we propose a novel informed machine learning approach and suggest to pre-train on prior knowledge.
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
Dynamic Review-based Recommenders
In the present work we leverage the known power of reviews to enhance rating predictions in a way that (i) respects the causality of review generation and (ii) includes, in a bidirectional fashion, the ability of ratings to inform language review models and vice-versa, language representations that help predict ratings end-to-end.
Combining expert knowledge and neural networks to model environmental stresses in agriculture
In this work we combine representation learning capabilities of neural network with agricultural knowledge from experts to model environmental heat and drought stresses.
Generative Deep Learning Techniques for Password Generation
Password guessing approaches via deep learning have recently been investigated with significant breakthroughs in their ability to generate novel, realistic password candidates.
Recurrent Point Review Models
Specifically, we use the dynamic representations of recurrent point process models, which encode the history of how business or service reviews are received in time, to generate instantaneous language models with improved prediction capabilities.
Recurrent Point Processes for Dynamic Review Models
Recent progress in recommender system research has shown the importance of including temporal representations to improve interpretability and performance.
Adiabatic Quantum Computing for Binary Clustering
Quantum computing for machine learning attracts increasing attention and recent technological developments suggest that especially adiabatic quantum computing may soon be of practical interest.
