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10 papers · 2 filters
Improving the performance of bagging ensembles for data streams through mini-batching
Guilherme Cassales, Heitor Gomes, Albert Bifet +2
Often, machine learning applications have to cope with dynamic environments where data are collected in the form of continuous data streams with potentially infinite length and tra…
Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy Labels
Benoit Dufumier, Pietro Gori, Julie Victor +2
Contrastive Learning has shown impressive results on natural and medical images, without requiring annotated data. However, a particularity of medical images is the availability of…
Improving Multimodal fusion via Mutual Dependency Maximisation
Pierre Colombo, Emile Chapuis, Matthieu Labeau +1
Multimodal sentiment analysis is a trending area of research, and the multimodal fusion is one of its most active topic. Acknowledging humans communicate through a variety of chann…
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?
Balázs Kégl, Gabriel Hurtado, Albert Thomas
We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that…
HEMP: High-order Entropy Minimization for neural network comPression
Enzo Tartaglione, Stéphane Lathuilière, Attilio Fiandrotti +2
We formulate the entropy of a quantized artificial neural network as a differentiable function that can be plugged as a regularization term into the cost function minimized by grad…
Probabilistic semi-nonnegative matrix factorization: a Skellam-based framework
Benoit Fuentes, Gaël Richard
We present a new probabilistic model to address semi-nonnegative matrix factorization (SNMF), called Skellam-SNMF. It is a hierarchical generative model consisting of prior compone…