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Found 19 papers, 5 papers with code
RecycleGPT: An Autoregressive Language Model with Recyclable Module
Existing large language models have to run K times to generate a sequence of K tokens.
A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting
In order to enhance the performance of Transformer models for long-term multivariate forecasting while minimizing computational demands, this paper introduces the Joint Time-Frequency Domain Transformer (JTFT).
A Fast Algorithm for Onboard Atmospheric Powered Descent Guidance
Atmospheric powered descent guidance can be solved by successive convexification; however, its onboard application is impeded by the sharp increase in computation caused by nonlinear aerodynamic forces.
Efficient Climate Simulation via Machine Learning Method
We develop a user-friendly platform NeuroGCM for efficiently developing hybrid modeling in climate simulation.
Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users
Risk assessment is a substantial problem for financial institutions that has been extensively studied both for its methodological richness and its various practical applications.
RD$^2$: Reward Decomposition with Representation Decomposition
In this work, we propose a set of novel reward decomposition principles by constraining uniqueness and compactness of different state features/representations relevant to different sub-rewards.
Model-based Adversarial Meta-Reinforcement Learning
When the test task distribution is different from the training task distribution, the performance may degrade significantly.
Episodic Reinforcement Learning with Associative Memory
Sample efficiency has been one of the major challenges for deep reinforcement learning.
The Deep Learning Compiler: A Comprehensive Survey
In this paper, we perform a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations.
OpenArray v1.0: a simple operator library for the decoupling of ocean modeling and parallel computing
In this work, we design a simple computing library to bridge the gap and decouple the work of ocean modeling from parallel computing.
Distributional Reward Decomposition for Reinforcement Learning
Many reinforcement learning (RL) tasks have specific properties that can be leveraged to modify existing RL algorithms to adapt to those tasks and further improve performance, and a general class of such properties is the multiple reward channel.
An Adaptive Remote Stochastic Gradient Method for Training Neural Networks
RSG is further combined with adaptive methods to construct ARSG for acceleration.
swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor
The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability.
swCaffe: a Parallel Framework for Accelerating Deep Learning Applications on Sunway TaihuLight
Second, we propose a set of optimization strategies for redesigning a variety of neural network layers based on Caffe.
Quantum Teleportation-Inspired Algorithm for Sampling Large Random Quantum Circuits
We show that low-depth random quantum circuits can be efficiently simulated by a quantum teleportation-inspired algorithm.
Quantum Physics
RedSync : Reducing Synchronization Traffic for Distributed Deep Learning
Data parallelism has become a dominant method to scale Deep Neural Network (DNN) training across multiple nodes.
Episodic Memory Deep Q-Networks
Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN).
Quantum Supremacy Circuit Simulation on Sunway TaihuLight
We design a large-scale simulator of universal random quantum circuits, often called 'quantum supremacy circuits', and implement it on Sunway TaihuLight.
Quantum Physics
Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks
Recent deep learning (DL) models have moved beyond static network architectures to dynamic ones, handling data where the network structure changes every example, such as sequences of variable lengths, trees, and graphs.
