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Found 13 papers, 1 papers with code
Inducing Reusable Skills From Demonstrations with Option-Controller Network
With the isolation of information and the synchronous calling mechanism, we can impose a division of works between the controller and options in an end-to-end training regime.
Learnability and Expressiveness in Self-Supervised Learning
In this work, we argue that representations induced by self-supervised learning (SSL) methods should both be expressive and learnable.
Iterated learning for emergent systematicity in VQA
Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice.
Learning Task Decomposition with Ordered Memory Policy Network
The discovered subtask hierarchy could be used to perform task decomposition, recovering the subtask boundaries in an unstruc-tured demonstration.
Learning Task Decomposition with Order-Memory Policy Network
Many complex real-world tasks are composed of several levels of sub-tasks.
Supervised Seeded Iterated Learning for Interactive Language Learning
Language drift has been one of the major obstacles to train language models through interaction.
Countering Language Drift with Seeded Iterated Learning
At each time step, the teacher is created by copying the student agent, before being finetuned to maximize task completion.
Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest
In this paper, we study the problem of out-of-distribution detection in skin disease images.
No-Press Diplomacy: Modeling Multi-Agent Gameplay
Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal.
Boosting Image Recognition with Non-differentiable Constraints
In this paper, we study the problem of image recognition with non-differentiable constraints.
No Press Diplomacy: Modeling Multi-Agent Gameplay
Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal.
Anomaly Detection for Skin Disease Images Using Variational Autoencoder
If we focus on specific diseases, the model is able to detect melanoma with 0. 864 AUCROC and detect actinic keratosis with 0. 872 AUCROC, even if it only sees the images of nevus.
Combining Model-based and Model-free RL via Multi-step Control Variates
Model-free deep reinforcement learning algorithms are able to successfully solve a wide range of continuous control tasks, but typically require many on-policy samples to achieve good performance.
