instruction:
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.
question:
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.
answer:
Learned energy based model with score matching


question:
We propose a new algorithm to learn a one-hidden-layer convolutional neural network where both the convolutional weights and the outputs weights are parameters to be learned. Our algorithm works for a general class of (potentially overlapping) patches, including commonly used structures for computer vision tasks. Our algorithm draws ideas from (1) isotonic regression for learning neural networks and (2) landscape analysis of non-convex matrix factorization problems. We believe these findings may inspire further development in designing provable algorithms for learning neural networks and other complex models. While our focus is theoretical, we also present experiments that illustrate our theoretical findings.
answer:
We propose an algorithm for provably recovering parameters (convolutional and output weights) of a convolutional network with overlapping patches.


question:
For the challenging semantic image segmentation task the best performing models
 have traditionally combined the structured modelling capabilities of Conditional
 Random Fields (CRFs) with the feature extraction power of CNNs. In more recent
 works however, CRF post-processing has fallen out of favour. We argue that this
 is mainly due to the slow training and inference speeds of CRFs, as well as the
 difficulty of learning the internal CRF parameters. To overcome both issues we
 propose to add the assumption of conditional independence to the framework of
 fully-connected CRFs. This allows us to reformulate the inference in terms of
 convolutions, which can be implemented highly efficiently on GPUs.Doing so
 speeds up inference and training by two orders of magnitude. All parameters of
 the convolutional CRFs can easily be optimized using backpropagation. Towards
 the goal of facilitating further CRF research we have made our implementations
 publicly available.
answer:

We propose Convolutional CRFs a fast, powerful and trainable alternative to Fully Connected CRFs.