Unluckily, compared to content-based image retrieval [5, 6, 7, 8, 9], IBSR is challenging due to the large appearance gap between 3D shapes and 2D images.

Unlike the conventional machine learning paradigms where learning is performed on a static dataset, domain incremental learning, i.e., continual learning with evolving domains, hopes to accommodate the model to the dynamically changing data distributions, while retaining the knowledge learned from previous domains [

Our setup is further extended to hybrid logical-neural constraints for capturing very complexconstraints,likegraphreachability.

However,for sequences ofdeletions, most prior work inthe non-convexsetting gives valid guarantees only for sequences that are chosenindependently of the models that are published. If people choose to delete their data as a function of the published models (because they don't like what the models reveal about them, for example), then the update sequence isadaptive.

During optimization, contrastive learning keeps the different modalities separated by a certain distance, which is influenced by the temperature parameter in the loss function. Our experiments further demonstrate that varying the modality gap distance has a significant impact in improving the model's downstream zero-shot classification performance and fairness.

Large-scale simulation models of complex socio-technical systems provide decision-makerswith high-fidelity testbeds inwhich policyinterventions canbe evaluated andwhat-if scenarios explored.

Towardsafinerlocalization thatbetterservesdownstream applications likerobot interaction, we extend the problem into task oriented instance segmentation.