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Found 7 papers, 5 papers with code
Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions
In the presence of imbalanced multi-class training data, GANs tend to favor classes with more samples, leading to the generation of low-quality and less diverse samples in tail classes.
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.
Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes.
LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty
To address this, we propose a new algorithm, LEarning Attention over Driving bEhavioRs (LEADER), that learns to attend to critical human behaviors during planning.
Stochastic Block-ADMM for Training Deep Networks
We prove the convergence of our proposed method and justify its capabilities through experiments in supervised and weakly-supervised settings.
Reducing Neural Network Parameter Initialization Into an SMT Problem
Training a neural network (NN) depends on multiple factors, including but not limited to the initial weights.
Re-understanding Finite-State Representations of Recurrent Policy Networks
We introduce an approach for understanding control policies represented as recurrent neural networks.
