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Found 9 papers, 2 papers with code
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model
Stepwise inference protocols, such as scratchpads and chain-of-thought, help language models solve complex problems by decomposing them into a sequence of simpler subproblems.
Meta-Learning for Neural Network-based Temporal Point Processes
The point process is widely used in many applications to predict such events related to human activities.
Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task
Motivated by this, we perform a controlled study for understanding compositional generalization in conditional diffusion models in a synthetic setting, varying different attributes of the training data and measuring the model's ability to generate samples out-of-distribution.
Predicting Opinion Dynamics via Sociologically-Informed Neural Networks
Traditionally, theoretical models of opinion dynamics have been proposed to describe the interactions between individuals (i. e., social interaction) and their impact on the evolution of collective opinions.
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains
Since the supports may have various granularities depending on attributes (e. g., poverty rate and crime rate), modeling such data is not straightforward.
Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes
Hawkes processes offer a central tool for modeling the diffusion processes, in which the influence from the past events is described by the triggering kernel.
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
By deriving the posterior GP, we can predict the data value at any location point by considering the spatial correlations and the dependences between areal data sets, simultaneously.
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information
Though many point processes have been proposed to model events in a continuous spatio-temporal space, none of them allow for the consideration of the rich contextual factors that affect event occurrence, such as weather, social activities, geographical characteristics, and traffic.
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity.
