An example prompt for constructing PDDL models of the action "close a

PlanBench seeks to cover a broad range of tasks related to planning and reasoning.

We provide hyper-parameters of our models in Table A.1. Table A.1: Hyper-parameters used for training our VisualCSE and AudioCSE. Vision, we use Dropout augmentation (the same strategy in SimCSE) for AudioCSE. We compare unsup-SimCSE and unsup-VisualCSE on a small scale retrieval test. As shown in Table C.1, VisualCSE generally retrieves qualitatively different sentences than SimCSE.

Xiaoqian Wu Shanghai Jiao Tong University enlighten@sjtu.edu.cn In Tab. 1, we conclude the notations in this work for clarity.Notation Definition r A rule. The size of the premise symbols set M . S is the symbol set, and R is the rule set. A \ B The set difference of A and B. D A very large-scale activity images database.

"B-Blue D-Black" stands for blue background and black digit).

Similarly, a user may want to interpolate different shapes.(iii)

Note that only the samples that the predictions of the network were correct are counted for a fair evaluation. Since segmentation labels are provided as 0's and 1's, it is possible to evaluate the quality of attribution maps as abinary classification task. This process can be repeated with different thresholds to produce a ROC curve. These examples also predict the correct label (person, horse, cow,train, bus, cat). Finally, a separate classifier is retrained on this perturbed dataset.

In this section, we provide the class assignment of all datasets under different missing rates. The proposed setting is anew multi-task learning scenario. Its practical applications could not be limited by the mentioned assumption in the testing space. Table B.2: The observed classes of each task onOffice-Caltech with different missing rates. Office-Home [9] contains images from four domains/tasks: Artistic, Clipart, Product and Realworld. Skin-Lesion contains three skin lesion classification tasks: HAM10000 [8], Dermofit [2] and Derm7pt[5].