In recent years, social media platforms have hosted an explosion of hate speech and objectionable content. The urgent need for effective automatic hate speech detection models have drawn remarkable investment from companies and researchers. Social media posts are generally short and their semantics could drastically be altered by even a single token. Thus, it is crucial for this task to learn context-aware input representations, and consider relevancy scores between input embeddings and class representations as an additional signal. To accommodate these needs, this paper introduces CRAB (Class Representation Attentive BERT), a neural model for detecting hate speech in social media. The model benefits from two semantic representations: (i) trainable token-wise and sentence-wise class representations, and (ii) contextualized input embeddings from state-of-the-art BERT encoder. To investigate effectiveness of CRAB, we train our model on Twitter data and compare it against strong baselines. Our results show that CRAB achieves 1.89% relative improved Macro-averaged F1 over state-of-the-art baseline. The results of this research open an opportunity for the future research on automated abusive behavior detection in social media

Few-shot learning has recently emerged as a new challenge in the deep learning field: unlike conventional methods that train the deep neural networks (DNNs) with a large number of labeled data, it asks for the generalization of DNNs on new classes with few annotated samples. Recent advances in few-shot learning mainly focus on image classification while in this paper we focus on object detection. The initial explorations in few-shot object detection tend to simulate a classification scenario by using the positive proposals in images with respect to certain object class while discarding the negative proposals of that class. Negatives, especially hard negatives, however, are essential to the embedding space learning in few-shot object detection. In this paper, we restore the negative information in few-shot object detection by introducing a new negative-and positive-representative based metric learning framework and a new inference scheme with negative and positive representatives. We build our work on a recent few-shot pipeline RepMet [1] with several new modules to encode negative information for both training and testing. Extensive experiments on ImageNet-LOC and PASCAL VOC show our method substantially improves the state-of-the-art few-shot object detection solutions.

Text-to-image multimodal tasks, generating/retrieving an image from a given text description, are extremely challenging tasks since raw text descriptions cover quite limited information in order to fully describe visually realistic images. We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input. First, we use the text description as initial input and conduct dependency parsing to extract the syntactic structure and analyse the semantic aspect, including object quantities, to extract the scene graph. Then, we train the extracted objects, attributes, and relations in the scene graph and the corresponding geometric relation information using Graph Convolutional Networks, and it generates text representation which integrates textual and visual semantic information. The text representation is aggregated with word-level and sentence-level embedding to generate both visual contextual word and sentence representation. For the evaluation, we attached VICTR to the state-of-the-art models in text-to-image generation.VICTR is easily added to existing models and improves across both quantitative and qualitative aspects.

Although 3D Convolutional Neural Networks (CNNs) are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory and computational requirements. Compressing such networks by pruning therefore becomes highly desirable. However, pruning 3D CNNs is largely unexplored possibly because of the complex nature of typical pruning algorithms that embeds pruning into an iterative optimization paradigm. In this work, we introduce a Resource Aware Neuron Pruning (RANP) algorithm that prunes 3D CNNs at initialization to high sparsity levels. Specifically, the core idea is to obtain an importance score for each neuron based on their sensitivity to the loss function. This neuron importance is then reweighted according to the neuron resource consumption related to FLOPs or memory. We demonstrate the effectiveness of our pruning method on 3D semantic segmentation with widely used 3D-UNets on ShapeNet and BraTS'18 as well as on video classification with MobileNetV2 and I3D on UCF101 dataset. In these experiments, our RANP leads to roughly 50-95 reduction in FLOPs and 35-80 reduction in memory with negligible loss in accuracy compared to the unpruned networks. This significantly reduces the computational resources required to train 3D CNNs. The pruned network obtained by our algorithm can also be easily scaled up and transferred to another dataset for training.

When you take a medication, you want to know precisely what it does. Pharmaceutical companies go through extensive testing to ensure that you do. With a new deep learning-based technique created at Rice University's Brown School of Engineering, they may soon get a better handle on how drugs in development will perform in the human body. The Rice lab of computer scientist Lydia Kavraki has introduced Metabolite Translator, a computational tool that predicts metabolites, the products of interactions between small molecules like drugs and enzymes. The Rice researchers take advantage of deep-learning methods and the availability of massive reaction datasets to give developers a broad picture of what a drug will do.

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Abstract: Deep Learning has enjoyed an impressive growth over the past few years in fields ranging from visual recognition to natural language processing. Improvements in these areas have been fundamental to the development of self-driving cars, machine translation, and healthcare applications. This progress has arguably been made possible by a combination of increases in computing power and clever heuristics, raising puzzling questions that lack full theoretical understanding. Here, we will discuss the relationship between the theory behind deep learning and its application. This panel discussion will be hosted remotely via Zoom.

Artificial intelligence allows underserved populations to gain access to a radiologist, pointed out Anthony L. Loschner, MD, assistant professor and associate program director, Critical Care Fellowship Program, Virginia Tech Carilion School of Medicine. Artificial intelligence allows underserved populations to gain access to a radiologist by just clicking a snapshot on their cell phone, said Anthony L. Loschner, MD, assistant professor and associate program director, Critical Care Fellowship Program, Virginia Tech Carilion School of Medicine, about his presentation at this year's CHEST meeting.TranscriptIntroduce us to the use of artificial intelligence in pulmonary medicine.The title of my presentation was "Artificial Intelligence in Pulmonary Medicine: The Rise of the Machines." And we reviewed the major articles that had been published in the last year or so guiding future AI in pulmonary medicine.There were some reviews that were done, literature reviews, where I reviewed articles on how AI is currently being used. Examples include great work by a Stanford team called ChestNet, and they're using a deep learning algorithm to read chest x-rays on any media pretty much.

The Fermionic Neural Network (FermiNet) is one of the first deep learning demonstrations for computing atomic energy. Fermionic Neural Network is a new neural network architecture successfully applied in modeling the quantum state of large collections of electrons. In quantum systems, the position of particles like electrons is described by a probability cloud as they don't have exact locations. This uncertainty in locating particles makes the representation of the state of a quantum system challenging. The probabilities assigned to possible configurations of electron positions are encoded in the wavefunction, which sets a positive or negative number to every electrons' configuration.

This is Part 4 of our ongoing series on NumPy optimization. In Parts 1 and 2 we covered the concepts of vectorization and broadcasting, and how they can be applied to optimize an implementation of the K-Means clustering algorithm. Next in the cue, Part 3 covered important concepts like strides, reshape, and transpose in NumPy. In this post, Part 4, we'll cover the application of those concepts to speed up a deep learning-based object detector: YOLO. Here are the links to the earlier parts for your reference.