【DL輪読会】Efficiently Modeling Long Sequences with Structured State SpacesDeep Learning JP
This document summarizes a research paper on modeling long-range dependencies in sequence data using structured state space models and deep learning. The proposed S4 model (1) derives recurrent and convolutional representations of state space models, (2) improves long-term memory using HiPPO matrices, and (3) efficiently computes state space model convolution kernels. Experiments show S4 outperforms existing methods on various long-range dependency tasks, achieves fast and memory-efficient computation comparable to efficient Transformers, and performs competitively as a general sequence model.
【DL輪読会】Efficiently Modeling Long Sequences with Structured State SpacesDeep Learning JP
This document summarizes a research paper on modeling long-range dependencies in sequence data using structured state space models and deep learning. The proposed S4 model (1) derives recurrent and convolutional representations of state space models, (2) improves long-term memory using HiPPO matrices, and (3) efficiently computes state space model convolution kernels. Experiments show S4 outperforms existing methods on various long-range dependency tasks, achieves fast and memory-efficient computation comparable to efficient Transformers, and performs competitively as a general sequence model.
1. The document discusses implicit behavioral cloning, which was presented in a 2021 Conference on Robot Learning (CoRL) paper.
2. Implicit behavioral cloning uses an implicit model rather than an explicit model to map observations to actions. The implicit model is trained using an InfoNCE loss function to discriminate positive observation-action pairs from negatively sampled pairs.
3. Experiments showed that the implicit model outperformed explicit models on several manipulation tasks like bi-manual sweeping, insertion, and sorting. The implicit approach was able to generalize better than explicit behavioral cloning.
This document discusses self-supervised representation learning (SRL) for reinforcement learning tasks. SRL learns state representations by using prediction tasks as an auxiliary objective. The key ideas are: (1) SRL learns an encoder that maps observations to states using a prediction task like modeling future states or actions; (2) The learned state representations improve generalization and exploration in reinforcement learning algorithms; (3) Several SRL methods are discussed, including world models, inverse models, and causal infoGANs.
Several recent papers have explored self-supervised learning methods for vision transformers (ViT). Key approaches include:
1. Masked prediction tasks that predict masked patches of the input image.
2. Contrastive learning using techniques like MoCo to learn representations by contrasting augmented views of the same image.
3. Self-distillation methods like DINO that distill a teacher ViT into a student ViT using different views of the same image.
4. Hybrid approaches that combine masked prediction with self-distillation, such as iBOT.
1. The document discusses implicit behavioral cloning, which was presented in a 2021 Conference on Robot Learning (CoRL) paper.
2. Implicit behavioral cloning uses an implicit model rather than an explicit model to map observations to actions. The implicit model is trained using an InfoNCE loss function to discriminate positive observation-action pairs from negatively sampled pairs.
3. Experiments showed that the implicit model outperformed explicit models on several manipulation tasks like bi-manual sweeping, insertion, and sorting. The implicit approach was able to generalize better than explicit behavioral cloning.
This document discusses self-supervised representation learning (SRL) for reinforcement learning tasks. SRL learns state representations by using prediction tasks as an auxiliary objective. The key ideas are: (1) SRL learns an encoder that maps observations to states using a prediction task like modeling future states or actions; (2) The learned state representations improve generalization and exploration in reinforcement learning algorithms; (3) Several SRL methods are discussed, including world models, inverse models, and causal infoGANs.
Several recent papers have explored self-supervised learning methods for vision transformers (ViT). Key approaches include:
1. Masked prediction tasks that predict masked patches of the input image.
2. Contrastive learning using techniques like MoCo to learn representations by contrasting augmented views of the same image.
3. Self-distillation methods like DINO that distill a teacher ViT into a student ViT using different views of the same image.
4. Hybrid approaches that combine masked prediction with self-distillation, such as iBOT.