Consequently, the use of artificial intelligence, deep-learning algorithms to assist with the interpretation of X-rays has the potential to improve diagnostic …

Tensors are the basic data structure of the PyTorch library. Deep learning is one of the mainstreams in supervised machine learning, where using the inputs and labeled output, we aim at developing a function to map an input to an output. Deep learning is deep in precisely the sense that its models learn many layers of transformations, where each layer offers the representation at one level [1]. Deep learning frameworks have played a crucial role in disseminating ideas. The first generation of frameworks allowing for easy modeling encompassed Caffe, Torch, and Theano [1].

It does not matter whether you are an industry veteran or an intern trying to make a mark, training a deep learning model can feel overwhelming at times. You need to focus on various factors like -- dataset quality, hyperparameter tuning, data augmentations, etc. so there are plenty of challenges already. What you do not want in this scenario is added workload of writing the code to train a model. Developing intuition is one of the most difficult aspects of deep learning. Zero Code TF Classifier provides nicely formatted plots for each model that you train which makes it easier to compare the models and also develop intuition.

So as a Data Scientist, you need all this knowledge. And when we say Big Data, we mean all the big data knowledge. And when we say AI, we mean all AI knowledge and like that complete Data Analytics knowledge, visualization knowledge, programming language and tools knowledge, machine learning knowledge, deep learning knowledge, and all that one can think related to data like data mining, data warehouse technologies, SQL and NoSQL technologies and all. Hence, it's a big challenge to become a Data Scientist, and you should start your journey from now onward. Also, reverse engineering can teach you a lot.

In spite of intensive efforts it has remained an open problem to what extent current Artificial Intelligence (AI) methods that employ Deep Neural Networks (DNNs) can be implemented more energy-efficiently on spike-based neuromorphic hardware. This holds in particular for AI methods that solve sequence processing tasks, a primary application target for spike-based neuromorphic hardware. One difficulty is that DNNs for such tasks typically employ Long Short-Term Memory (LSTM) units. Yet an efficient emulation of these units in spike-based hardware has been missing. We present a biologically inspired solution that solves this problem. This solution enables us to implement a major class of DNNs for sequence processing tasks such as time series classification and question answering with substantial energy savings on neuromorphic hardware. In fact, the Relational Network for reasoning about relations between objects that we use for question answering is the first example of a large DNN that carries out a sequence processing task with substantial energy-saving on neuromorphic hardware.

We're halfway 2021, and the ML-sphere keeps spinning: the Conference on Computer Vision and Pattern Recognition (CVPR 2021) was just held, Github and OpenAI released Copilot, an unprecedentedly intelligent code completion assistant, and much more happened in the last few weeks. Zeta Alpha is happy to help you discover the latest AI research and software and keep you up-to-date. The trend of outrageously large models is nowhere near an end. One year ago the release of OpenAI's GPT-3 got the AI community flabbergasted with 175 Billion parameters. This month was the turn of Wu Dao 2.0 to break the record, showing how China's not dragging behind at all when it comes to pouring resources in AI research.

Adaptation of a classifier to new domains is one of the challenging problems in machine learning. This has been addressed using many deep and non-deep learning based methods. Among the methodologies used, that of adversarial learning is widely applied to solve many deep learning problems along with domain adaptation. These methods are based on a discriminator that ensures source and target distributions are close. However, here we suggest that rather than using a point estimate obtaining by a single discriminator, it would be useful if a distribution based on ensembles of discriminators could be used to bridge this gap. This could be achieved using multiple classifiers or using traditional ensemble methods. In contrast, we suggest that a Monte Carlo dropout based ensemble discriminator could suffice to obtain the distribution based discriminator. Specifically, we propose a curriculum based dropout discriminator that gradually increases the variance of the sample based distribution and the corresponding reverse gradients are used to align the source and target feature representations. An ensemble of discriminators helps the model to learn the data distribution efficiently. It also provides a better gradient estimates to train the feature extractor. The detailed results and thorough ablation analysis show that our model outperforms state-of-the-art results.

Recently, artificial neural networks have been gaining momentum in the field of gravitational wave astronomy, for example in surrogate modelling of computationally expensive waveform models for binary black hole inspiral and merger. Surrogate modelling yields fast and accurate approximations of gravitational waves and neural networks have been used in the final step of interpolating the coefficients of the surrogate model for arbitrary waveforms outside the training sample. We investigate the existence of underlying structures in the empirical interpolation coefficients using autoencoders. We demonstrate that when the coefficient space is compressed to only two dimensions, a spiral structure appears, wherein the spiral angle is linearly related to the mass ratio. Based on this finding, we design a spiral module with learnable parameters, that is used as the first layer in a neural network, which learns to map the input space to the coefficients. The spiral module is evaluated on multiple neural network architectures and consistently achieves better speed-accuracy trade-off than baseline models. A thorough experimental study is conducted and the final result is a surrogate model which can evaluate millions of input parameters in a single forward pass in under 1ms on a desktop GPU, while the mismatch between the corresponding generated waveforms and the ground-truth waveforms is better than the compared baseline methods. We anticipate the existence of analogous underlying structures and corresponding computational gains also in the case of spinning black hole binaries.

Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples, the detection of which is crucial for the wide application of these DNN models. Recently, a number of deep testing methods in software engineering were proposed to find the vulnerability of DNN systems, and one of them, i.e., Model Mutation Testing (MMT), was used to successfully detect various adversarial samples generated by different kinds of adversarial attacks. However, the mutated models in MMT are always huge in number (e.g., over 100 models) and lack diversity (e.g., can be easily circumvented by high-confidence adversarial samples), which makes it less efficient in real applications and less effective in detecting high-confidence adversarial samples. In this study, we propose Graph-Guided Testing (GGT) for adversarial sample detection to overcome these aforementioned challenges. GGT generates pruned models with the guide of graph characteristics, each of them has only about 5% parameters of the mutated model in MMT, and graph guided models have higher diversity. The experiments on CIFAR10 and SVHN validate that GGT performs much better than MMT with respect to both effectiveness and efficiency.

Recent advances in robot learning have enabled robots to become increasingly better at mastering a predefined set of tasks. On the other hand, as humans, we have the ability to learn a growing set of tasks over our lifetime. Continual robot learning is an emerging research direction with the goal of endowing robots with this ability. In order to learn new tasks over time, the robot first needs to infer the task at hand. Task inference, however, has received little attention in the multi-task learning literature. In this paper, we propose a novel approach to continual learning of robotic control tasks. Our approach performs unsupervised learning of behavior embeddings by incrementally self-organizing demonstrated behaviors. Task inference is made by finding the nearest behavior embedding to a demonstrated behavior, which is used together with the environment state as input to a multi-task policy trained with reinforcement learning to optimize performance over tasks. Unlike previous approaches, our approach makes no assumptions about task distribution and requires no task exploration to infer tasks. We evaluate our approach in experiments with concurrently and sequentially presented tasks and show that it outperforms other multi-task learning approaches in terms of generalization performance and convergence speed, particularly in the continual learning setting.