- Multitask prompted training enables zero-shot task generalization: https://arxiv.org/pdf/2110.08207v1.pdf
- DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries: https://arxiv.org/pdf/2110.06922v1.pdf
- Object DGCNN: 3D Object Detection using Dynamic Graphs: https://arxiv.org/pdf/2110.06923v1.pdf
- Symbolic Knowledge Distillation: from General Language Models to Common sense Models: https://arxiv.org/pdf/2110.07178v1.pdf
- Graph Neural Networks with Learnable Structural and Positional Representations: https://arxiv.org/pdf/2110.07875.pdf
- Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research: https://arxiv.org/pdf/2110.07448v1.pdf
- Leveraging automated unit tests for unsupervised code translation: https://arxiv.org/pdf/2110.06773v1.pdf
- Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model: https://arxiv.org/pdf/2110.06560v1.pdf
- STARFORMER: transformer with state-action-reward representations: https://arxiv.org/pdf/2110.06206v1.pdf
- Transformers in Vision: A Survey: https://arxiv.org/pdf/2101.01169
- Neural tangent kernel eigenvalues accurately predict generalization: https://arxiv.org/pdf/2110.03922v2.pdf
- VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding: https://arxiv.org/pdf/2109.14084v2.pdf
- Curriculum learning: a regularization method for efficient and stable billion-scale GPT model pre-training: https://arxiv.org/pdf/2108.06084v2.pdf
- Evaluating Predictive Distributions: Does Bayesian Deep Learning Work?: https://arxiv.org/pdf/2110.04629v1
- Ego4D: Around the World in 3,000 Hours of Egocentric Video: https://arxiv.org/pdf/2110.07058v1
- How to train RNNs on chaotic data?: https://arxiv.org/pdf/2110.07238v1
- Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations: https://arxiv.org/pdf/2110.05960v1
- Non-deep Networks: https://arxiv.org/pdf/2110.07641v1
- Vectorization of Raster Manga by Deep Reinforcement Learning: https://arxiv.org/pdf/2110.04830v1
- Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation: https://arxiv.org/pdf/2110.07858v1