The first programming exercise "Twitter Sentiment Analysis in Python" is both fun and challenging, where you analyze tons of twitter message to find out the sentiments e.g.

Welcome to this introduction to TensorRT, our platform for deep learning inference. You will learn how to deploy a deep learning application onto a GPU, increasing throughput and reducing latency during inference. TensorRT provides APIs and parsers to import trained models from all major deep learning frameworks. It then generates optimized runtime engines deployable in the datacenter as well as in automotive and embedded environments. Applications deployed on GPUs with TensorRT perform up to 40x faster than CPU-only platforms. This tutorial uses a C example to walk you through importing an ONNX model into TensorRT, applying optimizations, and generating a high-performance runtime engine for the datacenter environment.

Abstract--The problem of state estimation for unobservable distribution systems is considered. A Bayesian approach is proposed that implements Bayesian inference with a deep neural network to achieve the minimum mean squared error estimation of network states for real-time applications. The proposed technique consists of distribution learning for stochastic power injection, a Monte Carlo technique for the training of a deep neural network for state estimation, and a Bayesian bad data detection and cleansing algorithm. Structural characteristics of the deep neural networks are investigated. Simulations illustrate the accuracy of Bayesian state estimation for unobservable systems and demonstrate the benefit of employing a deep neural network. Numerical results show the robustness of Bayesian state estimation against modeling and estimation errors of power injection distributions and the presence of bad data. Comparing with pseudo-measurement techniques, direct Bayesian state estimation with deep neural networks outperforms existing benchmarks. We consider the problem of state estimation for distribution systems that have limited measurements. This problem is motivated by the need of coping with the rising presence of distributed energy resources (DER) in distribution systems.

Facial pain expression is an important modality for assessing pain, especially when the patient's verbal ability to communicate is impaired. The facial muscle-based action units (AUs), which are defined by the Facial Action Coding System (FACS), have been widely studied and are highly reliable as a method for detecting facial expressions (FE) including valid detection of pain. Unfortunately, FACS coding by humans is a very time-consuming task that makes its clinical use prohibitive. Significant progress on automated facial expression recognition (AFER) has led to its numerous successful applications in FACS-based affective computing problems. However, only a handful of studies have been reported on automated pain detection (APD), and its application in clinical settings is still far from a reality. In this paper, we review the progress in research that has contributed to automated pain detection, with focus on 1) the framework-level similarity between spontaneous AFER and APD problems; 2) the evolution of system design including the recent development of deep learning methods; 3) the strategies and considerations in developing a FACS-based pain detection framework from existing research; and 4) introduction of the most relevant databases that are available for AFER and APD studies. We attempt to present key considerations in extending a general AFER framework to an APD framework in clinical settings. In addition, the performance metrics are also highlighted in evaluating an AFER or an APD system.

Given the emerging global threat of antimicrobial resistance, new methods for next-generation antimicrobial design are urgently needed. We report a peptide generation framework PepCVAE, based on a semi-supervised variational autoencoder (VAE) model, for designing novel antimicrobial peptide (AMP) sequences. Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences. The model further learns a disentangled antimicrobial attribute space by using the feedback from a jointly trained AMP classifier that uses limited labeled instances. The disentangled representation allows for controllable generation of AMPs. Extensive analysis of the PepCVAE-generated sequences reveals superior performance of our model in comparison to a plain VAE, as PepCVAE generates novel AMP sequences with higher long-range diversity, while being closer to the training distribution of biological peptides. These features are highly desired in next-generation antimicrobial design.

Distant supervised relation extraction has been successfully applied to large corpus with thousands of relations. However, the inevitable wrong labeling problem by distant supervision will hurt the performance of relation extraction. In this paper, we propose a method with neural noise converter to alleviate the impact of noisy data, and a conditional optimal selector to make proper prediction. Our noise converter learns the structured transition matrix on logit level and captures the property of distant supervised relation extraction dataset. The conditional optimal selector on the other hand helps to make proper prediction decision of an entity pair even if the group of sentences is overwhelmed by no-relation sentences. We conduct experiments on a widely used dataset and the results show significant improvement over competitive baseline methods.

Training neural networks subject to a Lipschitz constraint is useful for generalization bounds, provable adversarial robustness, interpretable gradients, and Wasserstein distance estimation. By the composition property of Lipschitz functions, it suffices to ensure that each individual affine transformation or nonlinear activation function is 1-Lipschitz. The challenge is to do this while maintaining the expressive power. We identify a necessary property for such an architecture: each of the layers must preserve the gradient norm during backpropagation. Based on this, we propose to combine a gradient norm preserving activation function, GroupSort, with norm-constrained weight matrices. We show that norm-constrained GroupSort architectures are universal Lipschitz function approximators. Empirically, we show that norm-constrained GroupSort networks achieve tighter estimates of Wasserstein distance than their ReLU counterparts and can achieve provable adversarial robustness guarantees with little cost to accuracy.

We propose a methodology to extend the concept of Two-Stream Convolutional Networks to perform end-to-end learning for self-driving cars with temporal cues. The system has the ability to learn spatiotemporal features by simultaneously mapping raw images and pre-calculated optical flows directly to steering commands. Although optical flows encode temporal-rich information, we found that 2D-CNNs are prone to capturing features only as spatial representations. We show how the use of Multitask Learning favors the learning of temporal features via inductive transfer from a shared spatiotemporal representation. Preliminary results demonstrate a competitive improvement of 30% in prediction accuracy and stability compared to widely used regression methods trained on the Comma.ai

Scaling model capacity has been vital in the success of deep learning. For a typical network, necessary compute resources and training time grow dramatically with model size. Conditional computation is a promising way to increase the number of parameters with a relatively small increase in resources. We propose a training algorithm that flexibly chooses neural modules based on the data to be processed. Both the decomposition and modules are learned end-to-end. In contrast to existing approaches, training does not rely on regularization to enforce diversity in module use. We apply modular networks both to image recognition and language modeling tasks, where we achieve superior performance compared to several baselines. Introspection reveals that modules specialize in interpretable contexts.

Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017). However, these gains rely on the availability of large amounts of annotated examples, without which state-of-the-art performance is rarely achievable. This is especially inconvenient for the many NLP fields where annotated examples are scarce, such as medical text. To improve NLP models in this situation, we evaluate five improvements on named entity recognition (NER) tasks when only ten annotated examples are available: (1) layer-wise initialization with pre-trained weights, (2) hyperparameter tuning, (3) combining pre-training data, (4) custom word embeddings, and (5) optimizing out-of-vocabulary (OOV) words. Experimental results show that the F1 score of 69.3% achievable by state-of-the-art models can be improved to 78.87%.