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Found 18 papers, 1 papers with code
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.
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.
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.
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.
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.
Supervised Seeded Iterated Learning for Interactive Language Learning
Language drift has been one of the major obstacles to train language models through interaction.
Learning Task Decomposition with Order-Memory Policy Network
Many complex real-world tasks are composed of several levels of sub-tasks.
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.
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.
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.
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods
We address the problem of evaluating the quality of self-supervised learning (SSL) models without access to supervised labels, while being agnostic to the architecture, learning algorithm or data manipulation used during training.
Prompting Decision Transformer for Few-Shot Policy Generalization
Humans can leverage prior experience and learn novel tasks from a handful of demonstrations.
Uniform Masking Prevails in Vision-Language Pretraining
Masked Language Modeling (MLM) has proven to be an essential component of Vision-Language (VL) pretraining.
Hyper-Decision Transformer for Efficient Online Policy Adaptation
To address this challenge, we propose a new framework, called Hyper-Decision Transformer (HDT), that can generalize to novel tasks from a handful of demonstrations in a data- and parameter-efficient manner.
Quantum Langevin Dynamics for Optimization
Finally, based on the observations when comparing QLD with classical Fokker-Plank-Smoluchowski equation, we propose a time-dependent QLD by making temperature and $\hbar$ time-dependent parameters, which can be theoretically proven to converge better than the time-independent case and also outperforms a series of state-of-the-art quantum and classical optimization algorithms in many non-convex landscapes.
