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Found 13 papers, 3 papers with code
Local Intrinsic Dimensional Entropy
For a finite $|\mathcal{X}|$, this yields robust entropy measures which satisfy many important properties, such as invariance to bijections, while the same is not true for continuous spaces (where $|\mathcal{X}|=\infty$).
AP: Selective Activation for De-sparsifying Pruned Neural Networks
However, in network pruning, we find that the sparsity introduced by ReLU, which we quantify by a term called dynamic dead neuron rate (DNR), is not beneficial for the pruned network.
Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks
(ii) In addition to the strong theoretical motivation, SILO is empirically optimal in the sense of matching an Oracle, which exhaustively searches for the optimal value of max_lr via grid search.
Towards Better Long-range Time Series Forecasting using Generative Forecasting
In this paper, we propose a new forecasting strategy called Generative Forecasting (GenF), which generates synthetic data for the next few time steps and then makes long-range forecasts based on generated and observed data.
Achieving Low Complexity Neural Decoders via Iterative Pruning
The advancement of deep learning has led to the development of neural decoders for low latency communications.
Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization
Empirical studies find that conventional training of neural networks, unlike network-to-network regularization, leads to networks of high KG and lower test accuracies.
Towards Better Long-range Time Series Forecasting using Generative Adversarial Networks
In this paper, we propose a new forecasting strategy called Generative Forecasting (GenF), which generates synthetic data for the next few time steps and then makes long-range forecasts based on generated and observed data.
Co-complexity: An Extended Perspective on Generalization Error
Subsequently, we propose a joint entropy-like measure of complexity between function spaces (classifier and generator), called co-complexity, which leads to tighter bounds on the generalization error in this setting.
Investigating Convolutional Neural Networks using Spatial Orderness
Convolutional Neural Networks (CNN) have been pivotal to the success of many state-of-the-art classification problems, in a wide variety of domains (for e. g. vision, speech, graphs and medical imaging).
Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks
Augmenting transformation knowledge onto a convolutional neural network's weights has often yielded significant improvements in performance.
Pose-Invariant Object Recognition for Event-Based Vision with Slow-ELM
Neuromorphic image sensors produce activity-driven spiking output at every pixel.
Spatiotemporal Filtering for Event-Based Action Recognition
In this work we introduce spatiotemporal filtering in the spike-event domain, as an alternative way of channeling spatiotemporal information through to a convolutional neural network.
Spatiotemporal Feature Learning for Event-Based Vision
Unlike conventional frame-based sensors, event-based visual sensors output information through spikes at a high temporal resolution.
