Papers

Reliable and fast DWARF-based stack unwinding

Debug information, usually encoded in the DWARF format, is a hidden and obscure component of our computing infrastructure. Debug information is obviously used by debuggers, but it also plays a key role in program analysis tools, and, most surprisingl...

Root Mean Square Layer Normalization

Layer normalization (LayerNorm) has been successfully applied to various deep neural networks to help stabilize training and boost model convergence because of its capability in handling re-centering and re-scaling of both inputs and weight matrix. H...

Ryū revisited: printf floating point conversion

Ryū Printf is a new algorithm to convert floating-point numbers to decimal strings according to the printf %f, %e, and %g formats: %f generates ‘full’ output (integer part of the input, dot, configurable number of digits), %e generates scientific out...

SWALP : Stochastic Weight Averaging in Low-Precision Training

Low precision operations can provide scalability, memory savings, portability, and energy efficiency. This paper proposes SWALP, an approach to low precision training that averages low-precision SGD iterates with a modified learning rate schedule. SW...

The continuous Bernoulli: fixing a pervasive error in variational autoencoders

Variational autoencoders (VAE) have quickly become a central tool in machine learning, applicable to a broad range of data types and latent variable models. By far the most common first step, taken by seminal papers and by core software libraries ali...

Trivializations for Gradient-Based Optimization on Manifolds

We introduce a framework to study the transformation of problems with manifold constraints into unconstrained problems through parametrizations in terms of a Euclidean space. We call these parametrizations "trivializations". We prove conditions under...

ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Large deep learning models offer significant accuracy gains, but training billions to trillions of parameters is challenging. Existing solutions such as data and model parallelisms exhibit fundamental limitations to fit these models into limited devi...

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

In several recently proposed stochastic optimization methods (e.g. RMSProp, Adam, Adadelta), parameter updates are scaled by the inverse square roots of exponential moving averages of squared past gradients. Maintaining these per-parameter second-mom...

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration

Previous works utilized ''smaller-norm-less-important'' criterion to prune filters with smaller norm values in a convolutional neural network. In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two req...

Group Normalization

Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems --- BN's error increases rapidly when the batch size becomes...

Memory Tagging and how it improves C/C++ memory safety

Memory safety in C and C++ remains largely unresolved. A technique usually called "memory tagging" may dramatically improve the situation if implemented in hardware with reasonable overhead. This paper describes two existing implementations of memory...

PipeDream: Fast and Efficient Pipeline Parallel DNN Training

PipeDream is a Deep Neural Network(DNN) training system for GPUs that parallelizes computation by pipelining execution across multiple machines. Its pipeline parallel computing model avoids the slowdowns faced by data-parallel training when large mod...

There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average

Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters. To understand consistency regularization, we co...

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution t...

A robust and efficient implementation of LOBPCG

Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) is widely used to compute eigenvalues of large sparse symmetric matrices. The algorithm can suffer from numerical instability if it is not implemented with care. This is especially prob...

Continuously Differentiable Exponential Linear Units

Exponential Linear Units (ELUs) are a useful rectifier for constructing deep learning architectures, as they may speed up and otherwise improve learning by virtue of not have vanishing gradients and by having mean activations near zero. However, the ...

Neural Optimizer Search with Reinforcement Learning

We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures. We train a Recurrent Neural Network controller to generate a string in a domain specific language that describes a mathem...

Neural Optimizer Search with Reinforcement Learning.

Parallel WaveNet: Fast High-Fidelity Speech Synthesis

The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequentia...

Self-Normalizing Neural Networks.

The Internet of Things (IoT) gains momentum. Developments regarding smart grids, intelligent transportation systems, and low-power networks for smart cities constitute significant drivers in the evolution of network industries. IoT creates an array o...