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Found 19 papers, 8 papers with code
Characterizing Deep Learning Training Workloads on Alibaba-PAI
One critical issue for efficiently operating practical AI clouds, is to characterize the computing and data transfer demands of these workloads, and more importantly, the training performance given the underlying software framework and hardware configurations.
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation
We formulate platform-aware NN architecture search in an optimization framework and propose a novel algorithm to search for optimal architectures aided by efficient accuracy and resource (latency and/or energy) predictors.
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Due to this, previous neural architecture search (NAS) methods are computationally expensive.
Ranked #789 on
Image Classification
on ImageNet
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
The application of deep learning techniques resulted in remarkable improvement of machine learning models.
High performance ultra-low-precision convolutions on mobile devices
Many applications of mobile deep learning, especially real-time computer vision workloads, are constrained by computation power.
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training.
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation
State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs).
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching
We perform a comprehensive set of experiments on standard datasets to carefully study the contributions of each aspect of MatchNet, with direct comparisons to established methods.
Going Deeper with Convolutions
We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).
Caffe: Convolutional Architecture for Fast Feature Embedding
The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
One-Shot Adaptation of Supervised Deep Convolutional Models
In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be?
Deep Convolutional Ranking for Multilabel Image Annotation
Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications.
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies
Learning a visual concept from a small number of positive examples is a significant challenge for machine learning algorithms.
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks.
Why Size Matters: Feature Coding as Nystrom Sampling
Recently, the computer vision and machine learning community has been in favor of feature extraction pipelines that rely on a coding step followed by a linear classifier, due to their overall simplicity, well understood properties of linear classifiers, and their computational efficiency.
Learning with Recursive Perceptual Representations
The use of random projections is key to our method, as we show in the experiments section, in which we observe a consistent improvement over previous --often more complicated-- methods on several vision and speech benchmarks.
Ranked #221 on
Image Classification
on CIFAR-10
Heavy-tailed Distances for Gradient Based Image Descriptors
Many applications in computer vision measure the similarity between images or image patches based on some statistics such as oriented gradients.
Factorized Latent Spaces with Structured Sparsity
Recent approaches to multi-view learning have shown that factorizing the information into parts that are shared across all views and parts that are private to each view could effectively account for the dependencies and independencies between the different input modalities.
