Model-Based Machine Learning: an online bookWant a paper copy? You can now buy the book! All royalties go to the Cystic Fibrosis Trust. Spotted an error? Have comments? Let us know! Follow @mbmlbook Get hands on with source code for the book.
How to Use t-SNE Effectively
A popular method for exploring high-dimensional data is something called t-SNE, introduced by van der Maaten and Hinton in 2008 [1]. The technique has become widespread in the field of machine learning, since it has an almost magical ability to create compelling two-dimensonal “maps” from data with hundreds or even thousands of dimensions. Although impressive, these images can be tempting to misre
Google Brain Robotics Data
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection Sergey Levine, Peter Pastor, Alex Krizhevsky, Deirdre Quillen ISER, 2016. [blog post] [arXiv] [PDF] Unsupervised Learning for Physical Interaction through Video Prediction Chelsea Finn, Ian Goodfellow, Sergey Levine NIPS, 2016. [arXiv] [PDF] Video Prediction with Neural Advection (github tensorfl
LSTMVis
- Visual Analysis for Recurent Neural Networks Hendrik Strobelt, Sebastian Gehrmann, Bernd Huber, Hanspeter Pfister, Alexander M. Rush Recurrent neural networks, and in particular long short-term memory networks (LSTMs), are a remarkably effective tool for sequence processing that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understandin
Memory Networks for Language Understanding, ICML Tutorial 2016
Memory Networks for Language Understanding, ICML Tutorial 2016 Speaker: Jason Weston Time: 11am-1pm, June 19 @ Crown Plaza Broadway + Breakout room There has been a recent resurgence in interest in the use of the combination of reasoning, attention and memory for solving tasks, particularly in the field of language understanding. I will review some of these recent efforts, as well as focusing on o
Variational Auto-Encoder - nbviewer
batch_size = 100 n_epochs = 100 n_hiddens_recog = [500] n_hiddens_gen = [500] n_latents = 20 n_layers_recog = len(n_hiddens_recog) n_layers_gen = len(n_hiddens_gen) layers = {} # Recognition model. rec_layer_sizes = [(x_train.shape[1], n_hiddens_recog[0])] rec_layer_sizes += zip(n_hiddens_recog[:-1], n_hiddens_recog[1:]) rec_layer_sizes += [(n_hiddens_recog[-1], n_latents * 2)] for i, (n_incoming,
REINFORCEjs: Gridworld with Dynamic Programming
# About **REINFORCEjs** is a Reinforcement Learning library that implements several common RL algorithms supported with fun web demos, and is currently maintained by [@karpathy](https://twitter.com/karpathy). In particular, the library currently includes: ### Dynamic Programming For solving finite (and not too large), deterministic MDPs. The solver uses standard tabular methods will no bells and w
DQNの生い立ち ＋ Deep Q-NetworkをChainerで書いた - Qiita
はじめに そもそもDQNが作りたかったわけじゃなくて、他の目的でChainerを使いたかったのでその練習にDQNを書いたんですが、せっかくだし公開しようと思いました 公開しました 。またどうせ公開するなら、この機会にこれ（Q学習+関数近似）関連で持っている知識をついでに整理しようと思ってまとめました。 ニュース記事とかNatureとかNIPSの論文だけ読むと、DQN作ったDeepmind/Googleすげー！！！って感覚になりそうですが、強化学習的な歴史的経緯を考えると強化学習+深層学習になった、むしろかなり当然の成り行きで生まれた技術であることがわかります。（ATARIのゲームを人間以上のパフォーマンスでプレイするというのがビジュアル的にわかりやすかった$\leftrightarrow$問題設定が良かったというのもあります。） この記事ではNIPSとNatureの以下の２本の論文 ・ V
The Unreasonable Effectiveness of Recurrent Neural Networks
May 21, 2015 There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simpl
A Note on BPTT for LSTM LM
This document provides an overview of backpropagation through time (BPTT) for long short-term memory (LSTM) language models. It describes the forward and backward passes for LSTM, including equations for calculating the input, forget, output and cell gates, as well as the cell state and hidden state. In the backward pass, it derives the equations for calculating the gradients with respect to the w
Photo-Real Talking Head with Deep Bidirectional LSTM - Microsoft Research
We propose to use deep bidirectional LSTM for audio/visual modeling in our photo-real talking head system. Abstract Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that was designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose to use deep bidirectional LSTM for audio/visual m
Photo-Real Talking Head with Deep Bidirectional LSTM - Microsoft Research
Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that is designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose to use deep bidirectional LSTM (BLSTM) for audio/visual modeling in our photo-real talking head system. An audio/visual database of a subject’s talking is firstly reco
RNNLIB
RNNLIB is a recurrent neural network library for sequence learning problems. Applicable to most types of spatiotemporal data, it has proven particularly effective for speech and handwriting recognition. full installation and usage instructions given at http://sourceforge.net/p/rnnl/wiki/Home/ Features LSTMMultidimensional recurrent neural networksConnectionist temporal classificationAdaptive weigh
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新たなRNNと自然言語処理
On the spectral radius of weight matrices in RNNs
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THEANETS — Theanets 0.7.3 documentation
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This is a minimal implementation of training for a language model using long short-term memory (LSTM) neural networks