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How to represent features for machine learning is an important business. For example, deep learning is all about finding good representations. What exactly they are depends on a task at hand. We investigate how to use available labels to obtain good representations. Motivation The paper that inspired us a while ago was Nonparametric Guidance of Autoencoder Representations using Label Information b
Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train. Our interest lies in empirically evaluating the expressiveness and the learnability of LSTMs in the sequence-to-sequence regime by training them to evaluate short computer programs, a domain that has traditionally been seen as too complex for neural networks.
We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple
WebRTCにおけるNAT越えの課題へのアプローチ - Qiita に移転しました。
q - Run SQL directly on CSV or TSV files¶ Overview¶ q's purpose is to bring SQL expressive power to the Linux command line by providing easy access to text as actual data, and allowing direct access to multi-file sqlite3 databases. q <flags> <sql-query> q allows the following: Performing SQL-like statements directly on tabular text data, auto-caching the data in order to accelerate additional quer
PPL2016@岡山 ディープラーニングの研究開発時には、計算を支援するためのフレームワークが用いられる。ChainerはPython上で動くディープラーニングフレームワークの一つである。他の多くのフレームワークと異なり、順伝播処理を行った時の実行履歴情報をもとに逆伝播のグラフを動的に構築するdefine-by-runという方式を採用している。この方式により、分岐や再帰を含むような複雑な構造のネットワークも直感的に構築でき、加えてデバッグが容易である。また、CuPyと呼ばれるNumPyサブセットのCUDAによる行列演算ライブラリを作成し、バックエンドとして利用している。本講演では、ディープラーニングフレームワークの基礎と実装、そして課題についてChainerを通して説明する。
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We provide a tokenizer, a part-of-speech tagger, hierarchical word clusters, and a dependency parser for tweets, along with annotated corpora and web-based annotation tools. Contributors: Archna Bhatia, Dipanjan Das, Chris Dyer, Jacob Eisenstein, Jeffrey Flanigan, Kevin Gimpel, Michael Heilman, Lingpeng Kong, Daniel Mills, Brendan O'Connor, Olutobi Owoputi, Nathan Schneider, Noah Smith, Swabha Swa
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