20 citations · 35 across the 24 of their papers we have counts for
7 papers · 1 filter
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana +66
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cul…
Neuroplastic Expansion in Deep Reinforcement Learning
Jiashun Liu, Johan Obando-Ceron, Aaron Courville +1
The loss of plasticity in learning agents, analogous to the solidification of neural pathways in biological brains, significantly impedes learning and adaptation in reinforcement l…
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL
Ghada Sokar, Johan Obando-Ceron, Aaron Courville +2
The use of deep neural networks in reinforcement learning (RL) often suffers from performance degradation as model size increases. While soft mixtures of experts (SoftMoEs) have re…
Mixture of Experts in a Mixture of RL settings
Timon Willi, Johan Obando-Ceron, Jakob Foerster +2
Mixtures of Experts (MoEs) have gained prominence in (self-)supervised learning due to their enhanced inference efficiency, adaptability to distributed training, and modularity. Pr…
On the consistency of hyper-parameter selection in value-based deep reinforcement learning
Johan Obando-Ceron, João G. M. Araújo, Aaron Courville +1
Deep reinforcement learning (deep RL) has achieved tremendous success on various domains through a combination of algorithmic design and careful selection of hyper-parameters. Algo…
In value-based deep reinforcement learning, a pruned network is a good network
Johan Obando-Ceron, Aaron Courville, Pablo Samuel Castro
Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage prior insights into the advantages of spars…
Mixtures of Experts Unlock Parameter Scaling for Deep RL
Johan Obando-Ceron, Ghada Sokar, Timon Willi +6
The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size. Analog…