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Found 8 papers, 6 papers with code
Constrained Submodular Optimization for Vaccine Design
Advances in machine learning have enabled the prediction of immune system responses to prophylactic and therapeutic vaccines.
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
Neural network pruning is a popular technique used to reduce the inference costs of modern, potentially overparameterized, networks.
Maximum n-times Coverage for Vaccine Design
We introduce the maximum $n$-times coverage problem that selects $k$ overlays to maximize the summed coverage of weighted elements, where each element must be covered at least $n$ times.
Overinterpretation reveals image classification model pathologies
Here, we demonstrate that neural networks trained on CIFAR-10 and ImageNet suffer from overinterpretation, and we find models on CIFAR-10 make confident predictions even when 95% of input images are masked and humans cannot discern salient features in the remaining pixel-subsets.
Information Condensing Active Learning
We introduce Information Condensing Active Learning (ICAL), a batch mode model agnostic Active Learning (AL) method targeted at Deep Bayesian Active Learning that focuses on acquiring labels for points which have as much information as possible about the still unacquired points.
Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles
The inaccuracy of neural network models on inputs that do not stem from the training data distribution is both problematic and at times unrecognized.
What made you do this? Understanding black-box decisions with sufficient input subsets
Local explanation frameworks aim to rationalize particular decisions made by a black-box prediction model.
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures
Under this model, gradient methods can be used to efficiently optimize the continuous latent factors with respect to inferred outcomes.
