Search Results for author: Yuchen Lu

Found 18 papers, 1 papers with code

Quantum Langevin Dynamics for Optimization

no code implementations27 Nov 2023 Zherui Chen, Yuchen Lu, Hao Wang, Yizhou Liu, Tongyang Li

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.

Hyper-Decision Transformer for Efficient Online Policy Adaptation

no code implementations17 Apr 2023 Mengdi Xu, Yuchen Lu, Yikang Shen, Shun Zhang, Ding Zhao, Chuang Gan

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.

Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods

no code implementations2 Jun 2022 Yuchen Lu, Zhen Liu, Aristide Baratin, Romain Laroche, Aaron Courville, Alessandro Sordoni

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.

Domain Generalization Self-Supervised Learning

Learnability and Expressiveness in Self-Supervised Learning

no code implementations29 Sep 2021 Yuchen Lu, Zhen Liu, Alessandro Sordoni, Aristide Baratin, Romain Laroche, Aaron Courville

In this work, we argue that representations induced by self-supervised learning (SSL) methods should both be expressive and learnable.

Data Augmentation Self-Supervised Learning

Inducing Reusable Skills From Demonstrations with Option-Controller Network

no code implementations29 Sep 2021 Siyuan Zhou, Yikang Shen, Yuchen Lu, Aaron Courville, Joshua B. Tenenbaum, Chuang Gan

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.

Iterated learning for emergent systematicity in VQA

no code implementations ICLR 2021 Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron Courville

Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice.

Question Answering Systematic Generalization +1

Learning Task Decomposition with Ordered Memory Policy Network

no code implementations19 Mar 2021 Yuchen Lu, Yikang Shen, Siyuan Zhou, Aaron Courville, Joshua B. Tenenbaum, Chuang Gan

The discovered subtask hierarchy could be used to perform task decomposition, recovering the subtask boundaries in an unstruc-tured demonstration.

Inductive Bias

Countering Language Drift with Seeded Iterated Learning

no code implementations ICML 2020 Yuchen Lu, Soumye Singhal, Florian Strub, Olivier Pietquin, Aaron Courville

At each time step, the teacher is created by copying the student agent, before being finetuned to maximize task completion.


No Press Diplomacy: Modeling Multi-Agent Gameplay

1 code implementation4 Sep 2019 Philip Paquette, Yuchen Lu, Steven Bocco, Max O. Smith, Satya Ortiz-Gagne, Jonathan K. Kummerfeld, Satinder Singh, Joelle Pineau, Aaron Courville

Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal.

Reinforcement Learning (RL)

Anomaly Detection for Skin Disease Images Using Variational Autoencoder

no code implementations3 Jul 2018 Yuchen Lu, Peng Xu

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.

Anomaly Detection Test

Combining Model-based and Model-free RL via Multi-step Control Variates

no code implementations ICLR 2018 Tong Che, Yuchen Lu, George Tucker, Surya Bhupatiraju, Shane Gu, Sergey Levine, Yoshua Bengio

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.

Continuous Control OpenAI Gym

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