no code implementations • NeurIPS 2007 • Nicolas L. Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl
We study the following question: is the two-dimensional structure of images a very strong prior or is it something that can be learned with a few examples of natural images?
no code implementations • NeurIPS 2011 • James S. Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl
Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs.
no code implementations • 20 Dec 2013 • Balázs Kégl
Next, we connect neighborhood features that correlate, and construct edge features by subtracting the correlated neighborhood features of each other.
no code implementations • 20 Dec 2013 • Balázs Kégl
Within the framework of AdaBoost. MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem to $K$ binary one-against-all classifications.
no code implementations • 14 Jun 2016 • Akın Kazakçıand Mehdi Cherti, Balázs Kégl
For an artificial creative agent, an essential driver of the search for novelty is a value function which is often provided by the system designer or users.
no code implementations • 19 May 2017 • Laetitia Le, Camille Marini, Alexandre Gramfort, David Nguyen, Mehdi Cherti, Sana Tfaili, Ali Tfayli, Arlette Baillet-Guffroy, Patrice Prognon, Pierre Chaminade, Eric Caudron, Balázs Kégl
Monoclonal antibodies constitute one of the most important strategies to treat patients suffering from cancers such as hematological malignancies and solid tumors.
2 code implementations • 4 Jun 2018 • Patricio Cerda, Gaël Varoquaux, Balázs Kégl
We show that a simple approach that exposes the redundancy to the learning algorithm brings significant gains.
no code implementations • 3 Oct 2018 • Balázs Kégl, Mehdi Cherti, Akın Kazakçı
Traditional wisdom in generative modeling literature is that spurious samples that a model can generate are errors and they should be avoided.
no code implementations • 30 May 2019 • Léonard Boussioux, Tomás Giro-Larraz, Charles Guille-Escuret, Mehdi Cherti, Balázs Kégl
Insects play such a crucial role in ecosystems that a shift in demography of just a few species can have devastating consequences at environmental, social and economic levels.
1 code implementation • 23 Oct 2019 • Sophie Giffard-Roisin, Mo Yang, Guillaume Charpiat, Christina Kumler-Bonfanti, Balázs Kégl, Claire Monteleoni
The forecast of tropical cyclone trajectories is crucial for the protection of people and property.
no code implementations • 1 Jan 2021 • Jianfeng Zhang, Illyyne Saffar, Aladin Virmaux, Balázs Kégl
We propose an unsupervised domain adaptation approach based on generative models.
1 code implementation • ICLR 2021 • 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.
no code implementations • 29 Sep 2021 • Albert Thomas, Balázs Kégl, Othman Gaizi, Gabriel Hurtado
Iterated batch reinforcement learning (RL) is a growing subfield fueled by the demand from systems engineers for intelligent control solutions that they can apply within their technical and organizational constraints.
no code implementations • 6 Oct 2021 • Aladin Virmaux, Illyyne Saffar, Jianfeng Zhang, Balázs Kégl
Knothe-Rosenblatt Domain Adaptation (KRDA) is based on the Knothe-Rosenblatt transport: we exploit autoregressive density estimation algorithms to accurately model the different sources by an autoregressive model using a mixture of Gaussians.
no code implementations • 20 Jun 2022 • Giuseppe Paolo, Jonas Gonzalez-Billandon, Albert Thomas, Balázs Kégl
In the last decade, reinforcement learning successfully solved complex control tasks and decision-making problems, like the Go board game.
no code implementations • 9 Oct 2023 • Abdelhakim Benechehab, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl
In model-based reinforcement learning (MBRL), most algorithms rely on simulating trajectories from one-step dynamics models learned on data.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 5 Feb 2024 • Abdelhakim Benechehab, Albert Thomas, Giuseppe Paolo, Maurizio Filippone, Balázs Kégl
In model-based reinforcement learning, most algorithms rely on simulating trajectories from one-step models of the dynamics learned on data.
no code implementations • 5 Feb 2024 • Abdelhakim Benechehab, Albert Thomas, Balázs Kégl
We consider the problem of offline reinforcement learning where only a set of system transitions is made available for policy optimization.
no code implementations • 6 Feb 2024 • Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl
We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language Models.