This document summarizes a presentation about variational autoencoders (VAEs) presented at the ICLR 2016 conference. The document discusses 5 VAE-related papers presented at ICLR 2016, including Importance Weighted Autoencoders, The Variational Fair Autoencoder, Generating Images from Captions with Attention, Variational Gaussian Process, and Variationally Auto-Encoded Deep Gaussian Processes. It also provides background on variational inference and VAEs, explaining how VAEs use neural networks to model probability distributions and maximize a lower bound on the log likelihood.
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.
【DL輪読会】Diffusion Policy: Visuomotor Policy Learning via Action DiffusionDeep Learning JP
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This document discusses a paper on visuomotor policy learning via action diffusion. The paper presents a method for training policies that map camera images directly to actions by incorporating action diffusion, which adds noise to actions during training. This helps explore the action space and avoid getting stuck in local optima during policy learning. The method can learn policies for complex manipulation tasks entirely from pixels using self-supervised reinforcement learning with image rewards.
This document discusses the relationship between control as inference, reinforcement learning, and active inference. It provides an overview of key concepts such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), optimality variables, the evidence lower bound (ELBO), variational inference, and the free energy principle as applied to active inference. Control as inference frames reinforcement learning as probabilistic inference by defining a generative process and performing variational inference to find an optimal policy. Active inference uses the free energy principle and minimizes expected free energy to select actions that resolve uncertainty.
1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
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Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
The document summarizes a research paper that compares the performance of MLP-based models to Transformer-based models on various natural language processing and computer vision tasks. The key points are:
1. Gated MLP (gMLP) architectures can achieve performance comparable to Transformers on most tasks, demonstrating that attention mechanisms may not be strictly necessary.
2. However, attention still provides benefits for some NLP tasks, as models combining gMLP and attention outperformed pure gMLP models on certain benchmarks.
3. For computer vision, gMLP achieved results close to Vision Transformers and CNNs on image classification, indicating gMLP can match their data efficiency.
This document discusses the relationship between control as inference, reinforcement learning, and active inference. It provides an overview of key concepts such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), optimality variables, the evidence lower bound (ELBO), variational inference, and the free energy principle as applied to active inference. Control as inference frames reinforcement learning as probabilistic inference by defining a generative process and performing variational inference to find an optimal policy. Active inference uses the free energy principle and minimizes expected free energy to select actions that resolve uncertainty.
1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
?
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
The document summarizes a research paper that compares the performance of MLP-based models to Transformer-based models on various natural language processing and computer vision tasks. The key points are:
1. Gated MLP (gMLP) architectures can achieve performance comparable to Transformers on most tasks, demonstrating that attention mechanisms may not be strictly necessary.
2. However, attention still provides benefits for some NLP tasks, as models combining gMLP and attention outperformed pure gMLP models on certain benchmarks.
3. For computer vision, gMLP achieved results close to Vision Transformers and CNNs on image classification, indicating gMLP can match their data efficiency.
23. MATLABで使用する数学関数
基本はほとんどC言語と同様.
● 三角関数
sin cos sind cosd atan2 pi ...
● 指数?対数
exp log log10 log2
● 複素数 (MATLABでは標準装備!!)
real imag abs angle i j
Tip
関数の使い方は helpコマ
ンド や docコマンド で
確認できる.
例)
>> help sind
>> doc cosd