In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Ex Input:
Machine learning algorithms are vulnerable to poisoning attacks: An adversary can inject malicious points in the training dataset to influence the learning process and degrade the algorithm's performance. Optimal poisoning attacks have already been proposed to evaluate worst-case scenarios, modelling attacks as a bi-level optimization problem. Solving these problems is computationally demanding and has limited applicability for some models such as deep networks. In this paper we introduce a novel generative model to craft systematic poisoning attacks against machine learning classifiers generating adversarial training examples, i.e. samples that look like genuine data points but that degrade the classifier's accuracy when used for training. We propose a Generative Adversarial Net with three components: generator, discriminator, and the target classifier. This approach allows us to model naturally the detectability constrains that can be expected in realistic attacks and to identify the regions of the underlying data distribution that can be more vulnerable to data poisoning. Our experimental evaluation shows the effectiveness of our attack to compromise machine learning classifiers, including deep networks.

Ex Output:
In this paper we propose a novel generative model to craft systematic poisoning attacks with detectability constraints against machine learning classifiers, including deep networks.

Ex Input:
Pruning is a popular technique for compressing a neural network: a large pre-trained network is fine-tuned while connections are successively removed. However, the value of pruning has largely evaded scrutiny. In this extended abstract, we examine residual networks obtained through Fisher-pruning and make two interesting observations. First, when time-constrained, it is better to train a simple, smaller network from scratch than prune a large network. Second, it is the architectures obtained through the pruning process --- not the learnt weights --- that prove valuable. Such architectures are powerful when trained from scratch. Furthermore, these architectures are easy to approximate without any further pruning: we can prune once and obtain a family of new, scalable network architectures for different memory requirements.

Ex Output:
Training small networks beats pruning, but pruning finds good small networks to train that are easy to copy.

Ex Input:
Single-cell RNA-sequencing (scRNA-seq) is a powerful tool for analyzing biological systems. However, due to biological and technical noise, quantifying the effects of multiple experimental conditions presents an analytical challenge. To overcome this challenge, we developed MELD: Manifold Enhancement of Latent Dimensions. MELD leverages tools from graph signal processing to learn a latent dimension within the data scoring the prototypicality of each datapoint with respect to experimental or control conditions. We call this dimension the Enhanced Experimental Signal (EES). MELD learns the EES by filtering the noisy categorical experimental label in the graph frequency domain to recover a smooth signal with continuous values. This method can be used to identify signature genes that vary between conditions and identify which cell types are most affected by a given perturbation. We demonstrate the advantages of MELD analysis in two biological datasets, including T-cell activation in response to antibody-coated beads and treatment of human pancreatic islet cells with interferon gamma.

Ex Output:
A novel graph signal processing framework for quantifying the effects of experimental perturbations in single cell biomedical data.