# Knet : beginning deep learning with 100 lines of

@inproceedings{Yuret2016KnetB, title={Knet : beginning deep learning with 100 lines of}, author={Julia Deniz Yuret}, year={2016} }

Knet (pronounced "kay-net") is the Koç University machine learning framework implemented in Julia, a high-level, high-performance, dynamic programming language. Unlike gradient generating compilers like Theano and TensorFlow which restrict users into a modeling mini-language, Knet allows models to be defined by just describing their forward computation in plain Julia, allowing the use of loops, conditionals, recursion, closures, tuples, dictionaries, array indexing, concatenation and other high… Expand

#### 32 Citations

Fast multidimensional reduction and broadcast operations on GPU for machine learning

- Computer Science
- Concurr. Comput. Pract. Exp.
- 2018

This work proposes two new strategies that extend the existing implementations to perform on tensors for scalar reduction and broadcast and introduces formal definitions of both operations using tensor notations, investigate their mathematical properties, and exploit these properties to provide an efficient solution for each. Expand

TYPE FastMultidimensional Reduction and Broadcast Operations on GPU forMachine Learning

- 2018

Present Address Koç University, Rumelifeneri Yolu, Sarıyer, Istanbul, Turkey, 34450 Summary Reduction and broadcast operations are commonly used in machine learning algorithms for different purposes.… Expand

Parsing with Context Embeddings

- Computer Science
- CoNLL Shared Task
- 2017

We introduce context embeddings, dense vectors derived from a language model that represent the left/right context of a word instance, and demonstrate that context embeddings significantly improve… Expand

TensorFlow.jl: An Idiomatic Julia Front End for TensorFlow

- Computer Science
- J. Open Source Softw.
- 2018

TensorFlow.jl is a Julia client library for the TensorFlow deep-learning framework that allows users to define Tensor Flow graphs using Julia syntax, which are interchangeable with the graphs produced by Google’s first-party Python Tensorflow client and can be used to perform training or inference on machine-learning models. Expand

Morphological Analysis Using a Sequence Decoder

- Computer Science
- Transactions of the Association for Computational Linguistics
- 2019

Morse, a recurrent encoder-decoder model that produces morphological analyses of each word in a sentence is introduced and it is shown that generating morphological features individually rather than as a combined tag allows the model to handle rare or unseen tags and to outperform whole-tag models. Expand

Graph Tracking in Dynamic Probabilistic Programs via Source Transformations

- Computer Science
- 2019

Many machine learning methods acting on graph structures can be expressed in terms of message passing, among them variational methods for approximate Bayesian inference, automatic differentiation (AD), and backpropagation. Expand

Multidimensional Broadcast Operation on the GPU

- 2017

Broadcast is a common operation in machine learning and widely used in calculating bias or subtracting maximum for normalization in convolutional neural networks. Broadcast operation is required when… Expand

Learning Sparse Neural Networks via Sensitivity-Driven Regularization

- Computer Science, Physics
- NeurIPS
- 2018

This work quantifies the output sensitivity to the parameters and introduces a regularization term that gradually lowers the absolute value of parameters with low sensitivity, so that a very large fraction of the parameters approach zero and are eventually set to zero by simple thresholding. Expand

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation

- Computer Science, Mathematics
- ICML
- 2019

A novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries and employ PENs to learn summary statistics in approximate Bayesian computation (ABC). Expand

SParse: Koç University Graph-Based Parsing System for the CoNLL 2018 Shared Task

- Computer Science
- CoNLL
- 2018

Sarse, the Graph-Based Parsing model submitted for the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, extends the state-of-the-art biaffine parser with a structural meta-learning module, SMeta, that combines local and global label predictions. Expand

#### References

SHOWING 1-10 OF 18 REFERENCES

Theano: A Python framework for fast computation of mathematical expressions

- Mathematics, Computer Science
- ArXiv
- 2016

The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed. Expand

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

- Computer Science
- ArXiv
- 2016

The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields. Expand

Torch7: A Matlab-like Environment for Machine Learning

- Computer Science
- NIPS 2011
- 2011

Torch7 is a versatile numeric computing framework and machine learning library that extends Lua that can easily be interfaced to third-party software thanks to Lua’s light interface. Expand

Caffe: Convolutional Architecture for Fast Feature Embedding

- Computer Science
- ACM Multimedia
- 2014

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Expand

Long Short-Term Memory

- Computer Science, Medicine
- Neural Computation
- 1997

A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Expand

Automatic differentiation in machine learning: a survey

- Mathematics, Computer Science
- J. Mach. Learn. Res.
- 2017

By precisely defining the main differentiation techniques and their interrelationships, this work aims to bring clarity to the usage of the terms “autodiff’, “automatic differentiation”, and “symbolic differentiation" as these are encountered more and more in machine learning settings. Expand

Very Deep Convolutional Networks for Large-Scale Image Recognition

- Computer Science
- ICLR
- 2015

This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. Expand

Gradient-based learning applied to document recognition

- Computer Science
- 1998

This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task, and Convolutional neural networks are shown to outperform all other techniques. Expand

Julia: A Fresh Approach to Numerical Computing

- Computer Science, Mathematics
- SIAM Rev.
- 2017

The Julia programming language and its design is introduced---a dance between specialization and abstraction, which recognizes what remains the same after computation, and which is best left untouched as they have been built by the experts. Expand

Learning and Stochastic Approximations 3 Q ( z , w ) measures the economical cost ( in hard currency units ) of delivering

- 1998

The convergence of online learning algorithms is analyzed using the tools of the stochastic approximation theory, and proved under very weak conditions. A general framework for online learning… Expand