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9 papers · 2 filters
River: machine learning for streaming data in Python
Jacob Montiel, Max Halford, Saulo Martiello Mastelini +8
River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performan…
LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks
Enzo Tartaglione, Andrea Bragagnolo, Attilio Fiandrotti +1
LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training neural networks having a sparse topology. Let the sensitivity of a network parameter be the variation of th…
Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks
Apostolos Avranas, Marios Kountouris, Philippe Ciblat
The problem of resource constrained scheduling in a dynamic and heterogeneous wireless setting is considered here. In our setup, the available limited bandwidth resources are alloc…
Self-Concordant Analysis of Generalized Linear Bandits with Forgetting
Yoan Russac, Louis Faury, Olivier Cappé +1
Contextual sequential decision problems with categorical or numerical observations are ubiquitous and Generalized Linear Bandits (GLB) offer a solid theoretical framework to addres…
A Framework to Learn with Interpretation
Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc
To tackle interpretability in deep learning, we present a novel framework to jointly learn a predictive model and its associated interpretation model. The interpreter provides both…
Time Series Source Separation with Slow Flows
Edouard Pineau, Sébastien Razakarivony, Thomas Bonald
In this paper, we show that slow feature analysis (SFA), a common time series decomposition method, naturally fits into the flow-based models (FBM) framework, a type of invertible…