Search Results for author:
Found 8 papers, 3 papers with code
Interpretable time series neural representation for classification purposes
In this work, we propose a set of requirements for a neural representation of univariate time series to be interpretable.
Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple sensors.
Generalizing to New Physical Systems via Context-Informed Dynamics Model
Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different physical contexts.
LEADS: Learning Dynamical Systems that Generalize Across Environments
Both are sub-optimal: the former disregards the discrepancies between environments leading to biased solutions, while the latter does not exploit their potential commonalities and is prone to scarcity problems.
From Node Embedding To Community Embedding : A Hyperbolic Approach
Considering the success of hyperbolic representations of graph-structured data in last years, an ongoing challenge is to set up a hyperbolic approach for the community detection problem.
Binary Stochastic Representations for Large Multi-class Classification
Different models based on the notion of binary codes have been proposed to overcome this limitation, achieving in a sublinear inference complexity.
LSHTC: A Benchmark for Large-Scale Text Classification
LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thousands).
Link Discovery using Graph Feature Tracking
We consider the problem of discovering links of an evolving undirected graph given a series of past snapshots of that graph.
