Problem Solving
Learning General World Models in a Handful of Reward-Free Deployments Yingchen Xu UCL, FAIR Jack Parker-Holder University of Oxford Aldo Pacchiano Microsoft Research Philip J. Ball
Combining these two properties, we introduce the reward-free deployment efficiency setting, a new paradigm for RL research.
Ron Yosef Y uval Elovici
In this work, we introduce WinoGA ViL: an online game of vision-and-language associations (e.g., between werewolves and a full moon), used as a dynamic evaluation benchmark.
our work interesting, timely and novel, and that our results demonstrate the fundamental limitations of Transformer
We thank the reviewers for their detailed comments and their useful suggestions. In this rebuttal, we report results on larger transformer models. We study the less understood issues related to how well TLMs are able to perform long chains of reasoning. This directly motivates us to investigate if language models can also learn certain reasoning strategies. We will add this discussion to the paper.
hyperparameter tuning for each individual encoding, (preliminary) experiments on the DARTS search space, and
We thank the reviewers for their helpful reviews. Please see the details below. See the figure below for the results of Reg. We now provide preliminary results for experiments on the DARTS search space. See the figure below (top right).
Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models Minting Pan Xiangming Zhu Y unbo Wang
World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios such as autonomous driving, there commonly exists noncontrollable dynamics independent of the action signals, making it difficult to learn effective world models.
A Proofs Lemma 1
We observed that the latent representations correlate with the class assignment. Further, GPN is capable to separate nodes with perturbed features in the latent space.
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces Hung Tran-The
Traditionally, BO assumes that the search space is known. However, in many problems, this assumption does not hold.
bb073f2855d769be5bf191f6378f7150-AuthorFeedback.pdf
We thank the reviewers for the positive and constructive feedback. Below we respond to their questions. The regret curves have a very tight confidence bound, starting from the very first iterations. V ariances are almost same across iterations? We did mention this in detail in our supp.