Facebook

About: Facebook is a company organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Artificial neural network & Language model. The organization has 7856 authors who have published 10906 publications receiving 570123 citations. The organization is also known as: facebook.com & FB.

Predicting Deeper into the Future of Semantic Segmentation

Abstract: The ability to predict and therefore to anticipate the future is an important attribute of intelligence. It is also of utmost importance in real-time systems, e.g. in robotics or autonomous driving, which depend on visual scene understanding for decision making. While prediction of the raw RGB pixel values in future video frames has been studied in previous work, here we introduce the novel task of predicting semantic segmentations of future frames. Given a sequence of video frames, our goal is to predict segmentation maps of not yet observed video frames that lie up to a second or further in the future. We develop an autoregressive convolutional neural network that learns to iteratively generate multiple frames. Our results on the Cityscapes dataset show that directly predicting future segmentations is substantially better than predicting and then segmenting future RGB frames. Prediction results up to half a second in the future are visually convincing and are much more accurate than those of a baseline based on warping semantic segmentations using optical flow.

Latent credibility analysis

Abstract: A frequent problem when dealing with data gathered from multiple sources on the web (ranging from booksellers to Wikipedia pages to stock analyst predictions) is that these sources disagree, and we must decide which of their (often mutually exclusive) claims we should accept. Current state-of-the-art information credibility algorithms known as "fact-finders" are transitive voting systems with rules specifying how votes iteratively flow from sources to claims and then back to sources. While this is quite tractable and often effective, fact-finders also suffer from substantial limitations; in particular, a lack of transparency obfuscates their credibility decisions and makes them difficult to adapt and analyze: knowing the mechanics of how votes are calculated does not readily tell us what those votes mean, and finding, for example, that a source has a score of 6 is not informative. We introduce a new approach to information credibility, Latent Credibility Analysis (LCA), constructing strongly principled, probabilistic models where the truth of each claim is a latent variable and the credibility of a source is captured by a set of model parameters. This gives LCA models clear semantics and modularity that make extending them to capture additional observed and latent credibility factors straightforward. Experiments over four real-world datasets demonstrate that LCA models can outperform the best fact-finders in both unsupervised and semi-supervised settings.

Fixing the train-test resolution discrepancy

Abstract: Data-augmentation is key to the training of neural networks for image classification. This paper first shows that existing augmentations induce a significant discrepancy between the size of the objects seen by the classifier at train and test time: in fact, a lower train resolution improves the classification at test time! We then propose a simple strategy to optimize the classifier performance, that employs different train and test resolutions. It relies on a computationally cheap fine-tuning of the network at the test resolution. This enables training strong classifiers using small training images, and therefore significantly reduce the training time. For instance, we obtain 77.1% top-1 accuracy on ImageNet with a ResNet-50 trained on 128x128 images, and 79.8% with one trained at 224x224. A ResNeXt-101 32x48d pre-trained with weak supervision on 940 million 224x224 images and further optimized with our technique for test resolution 320x320 achieves 86.4% top-1 accuracy (top-5: 98.0%). To the best of our knowledge this is the highest ImageNet single-crop accuracy to date.

Operator growth in the SYK model

Abstract: We discuss the probability distribution for the “size” of a time-evolving operator in the SYK model. Scrambling is related to the fact that as time passes, the distribution shifts towards larger operators. Initially, the rate is exponential and determined by the infinite-temperature chaos exponent. We evaluate the size distribution numerically for N = 30, and show how to compute it in the large-N theory using the dressed fermion propagator. We then evaluate the distribution explicitly at leading nontrivial order in the large-q expansion.

Language models enable zero-shot prediction of the effects of mutations on protein function

Abstract: Modeling the effect of sequence variation on function is a fundamental problem for understanding and designing proteins. Since evolution encodes information about function into patterns in protein sequences, unsupervised models of variant effects can be learned from sequence data. The approach to date has been to fit a model to a family of related sequences. The conventional setting is limited, since a new model must be trained for each prediction task. We show that using only zero-shot inference, without any supervision from experimental data or additional training, protein language models capture the functional effects of sequence variation, performing at state-of-the-art.