Search Results for author:
Found 18 papers, 10 papers with code
BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing
Basic Linear Algebra Subprograms (BLAS) are a set of low level linear algebra kernels widely adopted by applications involved with the deep learning and scientific computing.
Distributed, Parallel, and Cluster Computing
Large Scale Artificial Neural Network Training Using Multi-GPUs
This paper describes a method for accelerating large scale Artificial Neural Networks (ANN) training using multi-GPUs by reducing the forward and backward passes to matrix multiplication.
Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent
Then we present the study of ISGD batch size to the learning rate, parallelism, synchronization cost, system saturation and scalability.
Simple and Efficient Parallelization for Probabilistic Temporal Tensor Factorization
Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data.
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate.
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks
Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance.
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search
Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time.
Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search
To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics.
Ranked #18 on
Image Classification
on CIFAR-10
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time.
Ranked #7 on
Neural Architecture Search
on CIFAR-10 Image Classification
(Params metric)
Sample-Efficient Neural Architecture Search by Learning Action Space
To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics.
Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search
As a result, using manually designed action space to perform NAS often leads to sample-inefficient explorations of architectures and thus can be sub-optimal.
Few-shot Neural Architecture Search
supernet, to approximate the performance of every architecture in the search space via weight-sharing.
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search
If the nonlinear partition function and the local model fits well with ground-truth black-box function, then good partitions and candidates can be reached with much fewer samples.
Learning Space Partitions for Path Planning
Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function.
Multi-objective Optimization by Learning Space Partition
In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier.
Multi-objective Optimization by Learning Space Partitions
In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier.
Searching the Deployable Convolution Neural Networks for GPUs
To achieve this goal, we build a distributed NAS system to search on a novel search space that consists of prominent factors to impact latency and accuracy.
GPUNet: Searching the Deployable Convolution Neural Networks for GPUs
This paper intends to expedite the model customization with a model hub that contains the optimized models tiered by their inference latency using Neural Architecture Search (NAS).
Ranked #2 on
Neural Architecture Search
on ImageNet
