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Found 5 papers, 3 papers with code
Diffusion-based Neural Network Weights Generation
By learning the distribution of a neural network on a variety pretrained models, our approach enables adaptive sampling weights for unseen datasets achieving faster convergence and reaching competitive performance.
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control.
Ranked #1 on
SMAC+
on Off_Superhard_parallel
Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning
To overcome such limitations, we propose Hardware-adaptive Efficient Latency Predictor (HELP), which formulates the device-specific latency estimation problem as a meta-learning problem, such that we can estimate the latency of a model's performance for a given task on an unseen device with a few samples.
HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-Learning
To overcome such limitations, we propose Hardware-adaptive Efficient Latency Predictor (HELP), which formulates the device-specific latency estimation problem as a meta-learning problem, such that we can estimate the latency of a model's performance for a given task on an unseen device with a few samples.
Simplified Stochastic Feedforward Neural Networks
In this paper, we aim at developing efficient training methods for SFNN, in particular using known architectures and pre-trained parameters of DNN.
