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Found 9 papers, 3 papers with code
Six Attributes of Unhealthy Conversations
We present a new dataset of approximately 44000 comments labeled by crowdworkers.
Deep Neural Network Initialization with Sparsity Inducing Activations
Inducing and leveraging sparse activations during training and inference is a promising avenue for improving the computational efficiency of deep networks, which is increasingly important as network sizes continue to grow and their application becomes more widespread.
GenCast: Diffusion-based ensemble forecasting for medium-range weather
Probabilistic weather forecasting is critical for decision-making in high-impact domains such as flood forecasting, energy system planning or transportation routing, where quantifying the uncertainty of a forecast -- including probabilities of extreme events -- is essential to guide important cost-benefit trade-offs and mitigation measures.
Improved Projection Learning for Lower Dimensional Feature Maps
The requirement to repeatedly move large feature maps off- and on-chip during inference with convolutional neural networks (CNNs) imposes high costs in terms of both energy and time.
Increasing the accuracy and resolution of precipitation forecasts using deep generative models
Accurately forecasting extreme rainfall is notoriously difficult, but is also ever more crucial for society as climate change increases the frequency of such extremes.
Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset
We show that standard training of networks built with these layers, and pruned at initialization, achieves state-of-the-art accuracy for extreme sparsities on a variety of benchmark network architectures and datasets.
Six Attributes of Unhealthy Conversation
We present a new dataset of approximately 44000 comments labeled by crowdworkers.
Trajectory growth lower bounds for random sparse deep ReLU networks
This paper considers the growth in the length of one-dimensional trajectories as they are passed through deep ReLU neural networks, which, among other things, is one measure of the expressivity of deep networks.
Trajectory growth through random deep ReLU networks
This paper considers the growth in the length of one-dimensional trajectories as they are passed through deep ReLU neural networks, which, among other things, is one measure of the expressivity of deep networks.
