Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-based optimization algorithms that are efficient in solving inverse problems such as optimal control and motion planning. Simulating deformable objects is, however, more challenging compared to rigid body dynamics. The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and therefore they are significantly more computationally expensive to simulate. Computing gradients with respect to physical design or controller parameters is typically even more computationally challenging. In this paper, we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical simulator for deformable objects, ChainQueen, based on the Moving Least Squares Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects including contact and can be seamlessly incorporated into inference, control and co-design systems. We demonstrate that our simulator achieves high precision in both forward simulation and backward gradient computation. We have successfully employed it in a diverse set of control tasks for soft robots, including problems with nearly 3,000 decision variables.

Knowledge distillation (KD) is a popular method for reducing the computational overhead of deep network inference, in which the output of a teacher model is used to train a smaller, faster student model. Hint training (i.e., FitNets) extends KD by regressing a student model's intermediate representation to a teacher model's intermediate representation. In this work, we introduce bLock-wise Intermediate representation Training (LIT), a novel model compression technique that extends the use of intermediate representations in deep network compression, outperforming KD and hint training. LIT has two key ideas: 1) LIT trains a student of the same width (but shallower depth) as the teacher by directly comparing the intermediate representations, and 2) LIT uses the intermediate representation from the previous block in the teacher model as an input to the current student block during training, avoiding unstable intermediate representations in the student network. We show that LIT provides substantial reductions in network depth without loss in accuracy -- for example, LIT can compress a ResNeXt-110 to a ResNeXt-20 (5.5) on CIFAR10 and a VDCNN-29 to a VDCNN-9 (3.2) on Amazon Reviews without loss in accuracy, outperforming KD and hint training in network size for a given accuracy. We also show that applying LIT to identical student/teacher architectures increases the accuracy of the student model above the teacher model, outperforming the recently-proposed Born Again Networks procedure on ResNet, ResNeXt, and VDCNN. Finally, we show that LIT can effectively compress GAN generators, which are not supported in the KD framework because GANs output pixels as opposed to probabilities. Modern deep networks have achieved increased accuracy by continuing to introduce more layers (Ioffe & Szegedy, 2015; He et al., 2016) at the cost of higher computational overhead. In response, researchers have proposed many techniques to reduce this computational overhead at inference time, which broadly fall under two categories. First, in deep compression (Han et al., 2015; Zhu et al., 2016; Li et al., 2016; Hubara et al., 2017), parts of a model are removed or quantized to reduce the number of weights and/or the computational footprint.

Stochastic Gradient Descent (SGD) methods using randomly selected batches are widely-used to train neural network (NN) models. Performing design exploration to find the best NN for a particular task often requires extensive training with different models on a large dataset, which is very computationally expensive. The most straightforward method to accelerate this computation is to distribute the batch of SGD over multiple processors. To keep the distributed processors fully utilized requires commensurately growing the batch size; however, large batch training often times leads to degradation in accuracy, poor generalization, and even poor robustness to adversarial attacks. Existing solutions for large batch training either significantly degrade accuracy or require massive hyper-parameter tuning. To address this issue, we propose a novel large batch training method which combines recent results in adversarial training (to regularize against `sharp minima') and second order optimization (to use curvature information to change batch size adaptively during training). We extensively evaluate our method on Cifar-10/100, SVHN, TinyImageNet, and ImageNet datasets, using multiple NNs, including residual networks as well as smaller networks for mobile applications such as SqueezeNext. Our new approach exceeds the performance of the existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\% and $5\times$, respectively). We emphasize that this is achieved without any additional hyper-parameter tuning to tailor our proposed method in any of these experiments.

Recently, cloud providers have extended support for trusted hardware primitives such as Intel SGX. Simultaneously, the field of deep learning is seeing enormous innovation and increase in adoption. In this paper, we therefore ask the question: "Can third-party cloud services use SGX to provide practical, yet secure DNN Inference-as-a-service? " Our work addresses the three main challenges that SGX-based DNN inferencing faces, namely, security, ease-of-use, and performance. We first demonstrate that side-channel based attacks on DNN models are indeed possible. We show that, by observing access patterns, we can recover inputs to the DNN model. This motivates the need for Privado, a system we have designed for secure inference-as-a-service. Privado is input-oblivious: it transforms any deep learning framework written in C/C++ to be free of input-dependent access patterns. Privado is fully-automated and has a low TCB: with zero developer effort, given an ONNX description, it generates compact C code for the model which can run within SGX-enclaves. Privado has low performance overhead: we have used Privado with Torch, and have shown its overhead to be 20.77\% on average on 10 contemporary networks.

Deep neural networks can learn meaningful representations of data. However, these representations are hard to interpret. For example, visualizing a latent layer is generally only possible for at most three dimensions. Neural networks are able to learn and benefit from much higher dimensional representationsm but these are not visually interpretable because neurons have arbitrary ordering within a layer. Here, we utilize the ability of a human observer to identify patterns in structured representations to visualize higher dimensions. To do so, we propose a class of regularizations we call Graph Spectral Regularizations that impose graph structure on latent layers. This is achieved by treating activations as signals on a predefined graph and constraining those activations using graph filters, such as low pass and wavelet-like filters. This framework allows for any kind of graphs and filters to achieve a wide range of structured regularizations depending on the inference needs of the data. First, we show a synthetic example where a graph-structured layer reveals topological features of the data. Next, we show that a smoothing regularization imposes semantically consistent ordering of nodes when applied to capsule nets. Further, we show that the graph-structured layer, using wavelet-like spatially localized filters, can form local receptive fields for improved image and biomedical data interpretation. In other words, the mapping between latent layer, neurons and the output space becomes clear due to the localization of the activations. Finally, we show that when structured as a grid, the representations create coherent images that allow for image processing techniques such as convolutions.

We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory. To account for the fact that typically there are multiple correct SQL queries with the same or very similar semantics, we draw inspiration from syntactic parsing techniques and propose to train our sequence-to-action models with non-deterministic oracles. We evaluate our models on the WikiSQL dataset and achieve an execution accuracy of 83.7% on the test set, a 2.1% absolute improvement over the models trained with traditional static oracles assuming a single correct target SQL query. When further combined with the execution-guided decoding strategy, our model sets a new state-of-the-art performance at an execution accuracy of 87.1%.

This Machine Learning vs Deep Learning vs Artificial Intelligence video will help you understand the differences between ML, DL and AI, and how they are related to each other. The tutorial video will also cover what Machine Learning, Deep Learning and Artificial Intelligence entail, how they work with the help of examples, and whether they really are all that different. A glimpse into the future ( 25:46) Subscribe to our channel for more Machine Learning & AI Tutorials: https://www.youtube.com/user/Simplile... Machine Learning Articles: https://www.simplilearn.com/what-is-a... To gain in-depth knowledge of Machine Learning, Deep learning and Artificial Intelligence, Check out our Artificial Intelligence Engineer Program: https://www.simplilearn.com/artificia... You can also go through the Slides here: https://goo.gl/cdQ7uy By the end of this Artificial Intelligence Course, you will be able to accomplish the following: 1. Design intelligent agents to solve real-world problems which are search, games, machine learning, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, agent decision making 2. Master TensorFlow by understanding the concepts of TensorFlow, the main functions, operations and the execution pipeline 3. Acquire a deep intuition of Machine Learning models by mastering the mathematical and heuristic aspects of Machine Learning 4. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 5. Comprehend and correlate between theoretical concepts and practical aspects of Machine Learning 6. Master and comprehend advanced topics like convolutional neural networks, recurrent neural networks, training deep networks, high-level interfaces - - - - - - What skills will you learn with our Masters in Artificial Intelligence Program? 1. Learn about major applications of Artificial Intelligence across various use cases in various fields like customer service, financial services, healthcare, etc 2. Implement classical Artificial Intelligence techniques such as search algorithms, neural networks, tracking 3. Ability to apply Artificial Intelligence techniques for problem-solving and explain the limitations of current Artificial Intelligence techniques 4. Formalise a given problem in the language/framework of different AI methods such as a search problem, as a constraint satisfaction problem, as a planning problem, etc - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn

In the early days of computing, there was an acronym: GIGO. It stands for Garbage In, Garbage Out. The few people in the mainframe industry understood that if the data going into the system wasn't good then than what came out wasn't accurate information. The advent of the PC meant far more people began using computers, and most of them understood far less than the early programmers and users. A pundit pointed out the GIGO began to mean Garbage In, Gospel Out.

But artificial intelligence now'thinks' it has the answer. Using deep learning techniques, data scientists from Warwick Business School trained a computer system on 200,000 images from the website Scenic-or-Not, where members of the public vote on how beautiful a British scene is. These include Loch Scavaig on the Isle of Skye... and Newbury Road roundabout. The project was linked to earlier studies by the same team from Warwick's Data Science Lab that showed a direct correlation between residing in a scenic location and good health. If the AI could recognise beauty like a human, city planning for wellbeing could potentially be automated.

Deep Learning is a subset of Machine Learning,which itself a subset of Artificial Intelligence. Artificial Intelligence (AI): Any algorithm or technique that allows computing devices to make decisions or solve problems that previously required humans to perform them manually can be called an AI algorithm or technique. An AI can be either a huge stack of simple if-else statements or a very complex algorithm. If you have studied Artificial Intelligence in your school or college,you would read something titled "Rule Based Systems", which is nothing but a collection of IF THEN statements,to perform a task. An example of a Rule based system is MYCIN,which is developed to identify bacteria causing severe infections and to recommend antibiotics by Stanford,which has basically nothing but IF THEN statements.