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.

Example Input: We present Line-Storm, an interactive computer system for creative performance. The context we investigated was writing on paper using Line-Storm. We used self-report questionnaires as part of research involving human participants, to evaluate Line-Storm. Line-Storm consisted of a writing stylus and writing pad, augmented with electronics. The writing pad was connected to a contact microphone, and the writing stylus had a small micro-controller board and peripherals attached to it. The signals from these electronic augmentations were fed into the audio-synthesis environment Max/MSP to produce an interactive soundscape. We attempted to discover whether Line-Storm enhanced a self-reported sense of being present and engaged during a writing task, and we compared Line-Storm to a non-interactive control condition. After performing statistical analysis in SPSS, we were unable to support our research hypothesis, that presence and engagement were enhanced by Line-Storm. Participants reported they were, on average, no more present and engaged during the experimental condition than during the control condition. As creativity is subtle, and varies with person, time, context, space and so many other factors, this result was somewhat expected by us. A statistically significant result of our study is that some participants responded to Line-Storm more positively than others. These Preservers of Line-Storm were a group, distinct from other participants, who reported greater presence and engagement and who wrote more words with Line-Storm and during the control condition. We discuss the results of our research and place Line-Storm in an artistic-technological context, drawing upon writings by Martin Heidegger when considering the nature of Line-Storm. Future work includes modifying interactive components, improving aesthetics and using more miniaturized electronics, experimenting with a drawing task instead of a writing task, and collaborating with a composer of electronic music to make a more interesting, immersive, and engaging interactive soundscape for writing or drawing performance.
Example Output: Interactive stylus based sound incorporating writing system

Example Input: Energy based models outputs unmormalized log-probability values given datasamples. Such a estimation is essential in a variety of application problems suchas sample generation, denoising, sample restoration, outlier detection, Bayesianreasoning, and many more. However, standard maximum likelihood training iscomputationally expensive due to the requirement of sampling model distribution. Score matching potentially alleviates this problem, and denoising score matching(Vincent, 2011) is a particular convenient version. However, previous attemptsfailed to produce models capable of high quality sample synthesis. We believethat it is because they only performed denoising score matching over a singlenoise scale. To overcome this limitation, here we instead learn an energy functionusing all noise scales. When sampled using Annealed Langevin dynamics andsingle step denoising jump, our model produced high-quality samples comparableto state-of-the-art techniques such as GANs, in addition to assigning likelihood totest data comparable to previous likelihood models. Our model set a new sam-ple quality baseline in likelihood-based models. We further demonstrate that our model learns sample distribution and generalize well on an image inpainting tasks.
Example Output: Learned energy based model with score matching

Example Input: Unsupervised learning of disentangled representations is an open problem in machine learning. The Disentanglement-PyTorch library is developed to facilitate research, implementation, and testing of new variational algorithms. In this modular library, neural architectures, dimensionality of the latent space, and the training algorithms are fully decoupled, allowing for independent and consistent experiments across variational methods. The library handles the training scheduling, logging, and visualizations of reconstructions and latent space traversals. It also evaluates the encodings based on various disentanglement metrics. The library, so far, includes implementations of the following unsupervised algorithms VAE, Beta-VAE, Factor-VAE, DIP-I-VAE, DIP-II-VAE, Info-VAE, and Beta-TCVAE, as well as conditional approaches such as CVAE and IFCVAE. The library is compatible with the Disentanglement Challenge of NeurIPS 2019, hosted on AICrowd and was used to compete in the first and second stages of the challenge, where it was ranked among the best few participants.
Example Output:
Disentanglement-PyTorch is a library for variational representation learning