Recently, deep learning approaches have been extensively studied for low-dose CT denoising thanks to its superior performance despite the fast computational time. In particular, cycleGAN has been demonstrated as a powerful unsupervised learning scheme to improve the low-dose CT image quality without requiring matched high-dose reference data. Unfortunately, one of the main limitations of the cycleGAN approach is that it requires two deep neural network generators at the training phase, although only one of them is used at the inference phase. The secondary auxiliary generator is needed to enforce the cycle-consistency, but the additional memory requirement and increases of the learnable parameters are the main huddles for cycleGAN training. To address this issue, here we propose a novel cycleGAN architecture using a single switchable generator. In particular, a single generator is implemented using adaptive instance normalization (AdaIN) layers so that the baseline generator converting a low-dose CT image to a routine-dose CT image can be switched to a generator converting high-dose to low-dose by simply changing the AdaIN code. Thanks to the shared baseline network, the additional memory requirement and weight increases are minimized, and the training can be done more stably even with small training data. Experimental results show that the proposed method outperforms the previous cycleGAN approaches while using only about half the parameters.

In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices. In light of this need, federated learning has emerged as a popular training paradigm. However, many federated learning approaches trade transmitting data for communicating updated weight parameters for each local device. Therefore, a successful breach that would have otherwise directly compromised the data instead grants whitebox access to the local model, which opens the door to a number of attacks, including exposing the very data federated learning seeks to protect. Additionally, in distributed scenarios, individual client devices commonly exhibit high statistical heterogeneity. Many common federated approaches learn a single global model; while this may do well on average, performance degrades when the i.i.d. assumption is violated, underfitting individuals further from the mean, and raising questions of fairness. To address these issues, we propose Weight Anonymized Factorization for Federated Learning (WAFFLe), an approach that combines the Indian Buffet Process with a shared dictionary of weight factors for neural networks. Experiments on MNIST, FashionMNIST, and CIFAR-10 demonstrate WAFFLe's significant improvement to local test performance and fairness while simultaneously providing an extra layer of security.

We introduce three new robustness benchmarks consisting of naturally occurring distribution changes in image style, geographic location, camera operation, and more. Using our benchmarks, we take stock of previously proposed hypotheses for out-of-distribution robustness and put them to the test. We find that using larger models and synthetic data augmentation can improve robustness on real-world distribution shifts, contrary to claims in prior work. Motivated by this, we introduce a new data augmentation method which advances the state-of-the-art and outperforms models pretrained with 1000x more labeled data. We find that some methods consistently help with distribution shifts in texture and local image statistics, but these methods do not help with some other distribution shifts like geographic changes. Hence no evaluated method consistently improves robustness. We conclude that future research must study multiple distribution shifts simultaneously.

Graph Convolutional Networks (GCNs) show promising results for semi-supervised learning tasks on graphs, thus become favorable comparing with other approaches. Despite the remarkable success of GCNs, it is difficult to train GCNs with insufficient supervision. When labeled data are limited, the performance of GCNs becomes unsatisfying for low-degree nodes. While some prior work analyze successes and failures of GCNs on the entire model level, profiling GCNs on individual node level is still underexplored. In this paper, we analyze GCNs in regard to the node degree distribution. From empirical observation to theoretical proof, we confirm that GCNs are biased towards nodes with larger degrees with higher accuracy on them, even if high-degree nodes are underrepresented in most graphs. We further develop a novel Self-Supervised-Learning Degree-Specific GCN (SL-DSGC) that mitigate the degree-related biases of GCNs from model and data aspects. Firstly, we propose a degree-specific GCN layer that captures both discrepancies and similarities of nodes with different degrees, which reduces the inner model-aspect biases of GCNs caused by sharing the same parameters with all nodes. Secondly, we design a self-supervised-learning algorithm that creates pseudo labels with uncertainty scores on unlabeled nodes with a Bayesian neural network. Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective. Uncertainty scores are further exploited to weight pseudo labels dynamically in the stochastic gradient descent for SL-DSGC. Experiments on three benchmark datasets show SL-DSGC not only outperforms state-of-the-art self-training/self-supervised-learning GCN methods, but also improves GCN accuracy dramatically for low-degree nodes.

In this work, we introduce kernels with random Fourier features in the meta-learning framework to leverage their strong few-shot learning ability. We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable. We formulate the optimization of MetaVRF as a variational inference problem by deriving an evidence lower bound under the meta-learning framework. To incorporate shared knowledge from related tasks, we propose a context inference of the posterior, which is established by an LSTM architecture. The LSTM-based inference network can effectively integrate the context information of previous tasks with task-specific information, generating informative and adaptive features. The learned MetaVRF can produce kernels of high representational power with a relatively low spectral sampling rate and also enables fast adaptation to new tasks. Experimental results on a variety of few-shot regression and classification tasks demonstrate that MetaVRF delivers much better, or at least competitive, performance compared to existing meta-learning alternatives.

It seems like only last year that we were arguing about whether the slow-release rollout of the 1.5 billion parameter Generative Pretrained Transformer-2 (GPT-2) was reasonable. If the debate seems recent, that's because it is (writing from 2020): The notorious GPT-2 model was announced by OpenAI in February 2019, but it wasn't fully released until nearly 9 months later (although it was replicated before that). The release schedule was admittedly somewhat experimental, meant more to foster discussion of responsible open publishing, rather than a last-ditch effort to avert an AI apocalypse. All that is a bit moot by now because not only has OpenAI trained a much larger language model in GPT-3, but you can sign up to access it through their new API. Comparing GPT-3 to GPT-2 is like comparing apples to, well, raisins, because the model is about that much larger.

Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two. Machine Learning incorporates " classical" algorithms for various kinds of tasks such as clustering, regression or classification. Machine Learning algorithms must be trained on data. The more data you provide to your algorithm, the better it gets. The "training" part of a Machine Learning model means that this model tries to optimize along a certain dimension.

PyTorch has rapidly become one of the most transformative frameworks in the field of Deep Learning. Since its release, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility, and how easy it is to use when building Deep Learning models. Deep Learning jobs command some of the highest salaries in the development world. This course is meant to take you from the complete basics, to building state-of-the art Deep Learning and Computer Vision applications with PyTorch. With over 44000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.

Recently, researchers from DeepMind and Google introduced methods for choosing the best policy in offline reinforcement learning (ORL) known as offline hyperparameter selection (OHS). It uses logged data from a set of many policies that are trained using different hyperparameters. Reinforcement learning has become one of the most critical techniques in AI which has been used to attain Artificial General Intelligence. Offline reinforcement learning has now become a fundamental approach for deploying RL techniques in real-world scenarios. According to this blog post, offline reinforcement learning can assist in pre-training a reinforcement learning agent using the existing data.

In 2005, Ray Kurzweil said, "the singularity is near." Now, AI can code in any language, and we're moving to way better AI. GPT-3 got "mindboggling" results by training on a ton of data: Basically the whole Internet. It doesn't need to train on your specific use-case (zero-shot learning). It can fool 88% of people, and we're still in the baby stage.