Entity recommendation, providing search users with an improved experience via assisting them in finding related entities for a given query, has become an indispensable feature of today's search engines. Existing studies typically only consider the queries with explicit entities. They usually fail to handle complex queries that without entities, such as "what food is good for cold weather", because their models could not infer the underlying meaning of the input text. In this work, we believe that contexts convey valuable evidence that could facilitate the semantic modeling of queries, and take them into consideration for entity recommendation. In order to better model the semantics of queries and entities, we learn the representation of queries and entities jointly with attentive deep neural networks. We evaluate our approach using large-scale, real-world search logs from a widely used commercial Chinese search engine. Our system has been deployed in ShenMa Search Engine and you can fetch it in UC Browser of Alibaba. Results from online A/B test suggest that the impression efficiency of click-through rate increased by 5.1% and page view increased by 5.5%.

This paper is about recovering the unseen future data from a given sequence of historical samples, so called as \emph{future data recovery}---a significant problem closely related to time series forecasting. To address the problem, it is now prevalent to use deep neural networks, which are actually built upon the hypothesis that the desired evolution law can be learnt by using many observed samples to feed an overparameterized network. In practice, however, it is not always feasible to obtain a huge mass of training samples. To overcome the issue, we would suggest to consider a different methodology. Namely, we convert future data recovery into a more inclusive problem called \emph{sequential tensor completion} (STC), which is to restore a latent tensor of sequential structure from a sampling of its entries. Unlike the ordinary tensor completion problem studied in the majority of literature, STC has a distinctive setup that allows the locations of missing entries to be distributed arbitrarily, integrating seamlessly the future values of time series into the framework of missing data. Then we propose two methods to address STC, including Discrete Fourier Transform based $\ell_1$ minimization ($\mathrm{DFT}_{\ell_1}$) and Convolution Nuclear Norm Minimization (CNNM). We provide theoretical results to guarantee the recovery performance of the proposed methods. Remarkably, our theories disclose an important message; that is, under certain conditions, the unseen future values are indeed recoverable from the historical observations. Experiments on univariate time series, images and videos show encouraging results.

Aljosa Smolic 1 smolica@scss.tcd.ie 1 V-SENSE School of Computer Science and Statistics T rinity College Dublin, Ireland 2 University of Houston-Victoria, Victoria, T exas, US 3 CAAD Department of Architecture ETH, Zurich, Switzerland 4 Department of Architecture T arbiat Modares University T ehran, Iran Abstract Scene understanding of full-scale 3D models of an urban area remains a challenging task. While advanced computer vision techniques offer cost-effective approaches to analyse 3D urban elements, a precise and densely labelled dataset is quintessential. The paper presents the first-ever labelled dataset for a highly dense Aerial Laser Scanning (ALS) point cloud at city-scale. This work introduces a novel benchmark dataset that includes a manually annotated point cloud for over 260 million laser scanning points into 100'000 (approx.) Objects are labelled into 13 classes using hierarchical levels of detail from large ( i.e. building, vegetation and ground) to refined ( i.e. window, door and tree) elements. To validate the performance of our dataset, two different applications are showcased. Firstly, the labelled point cloud is employed for training Convolutional Neural Networks (CNNs) to classify urban elements. The dataset is tested on the well-known state-of-the-art CNNs ( i.e. Secondly, the complete ALS dataset is applied as detailed ground truth for city-scale image-based 3D reconstruction. 1 Introduction In computer vision, automated identification of three-dimensional (3D) assets in an unstructured large dataset is essential for scene understanding.

We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret using memory reduction interactions, to investigate parts of the memory role when errors have been made, and ultimately to improve the agent training process. We report on several examples of use of DRLViz, in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpret-ability and explain-ability in the field of visual analytics.

Inspired by the recent social movement of #MeToo, we are building a chatbot to assist survivors of sexual harassment cases (designed for the city of Maastricht but can easily be extended). The motivation behind this work is twofold: properly assist survivors of such events by directing them to appropriate institutions that can offer them help and increase the incident documentation so as to gather more data about harassment cases which are currently under reported. We break down the problem into three data science/machine learning components: harassment type identification (treated as a classification problem), spatio-temporal information extraction (treated as Named Entity Recognition problem) and dialogue with the users (treated as a slot-filling based chatbot). We are able to achieve a success rate of more than 98% for the identification of a harassment-or-not case and around 80% for the specific type harassment identification. Locations and dates are identified with more than 90% accuracy and time occurrences prove more challenging with almost 80%. Finally, initial validation of the chatbot shows great potential for the further development and deployment of such a beneficial for the whole society tool.

Despite achieving great success on performance in various sequential decision task, deep reinforcement learning is extremely data inefficient. Many approaches have been proposed to improve the data efficiency, e.g. Previous researches on transfer learning mostly attempt to learn a common feature space of states across related tasks to exploit knowledge as much as possible. However, semantic information of actions may be shared as well, even between tasks with different action space size. In this work, we first propose a method to learn action embedding for discrete actions in RL from generated trajectories without any prior knowledge, and then leverage it to transfer policy across tasks with different state space and/or discrete action space. Our experimental results show that our method can effectively learn informative action embeddings and accelerate learning by policy transfer across tasks. Introduction Deep reinforcement learning (DRL), which combines reinforcement learning algorithms and deep neural networks, has achieved great success in many domains, such as playing Atari games (Mnih et al. 2015), playing game of Go (Silver et al. 2016) and robotics control (Levine et al. 2016). Although the DRL is viewed as one of the most potential ways to the General Artificial Intelligence, it is still criticized for its data inefficiency. Training an agent from scratch requires considerable numbers of interactions with the environment for a very specific task.

Let's Plays of video games represent a relatively unexplored area for experimental AI in games. In this short paper, we discuss an approach to generate automated commentary for Let's Play videos, drawing on convolutional deep neural networks. We focus on Let's Plays of the popular game Minecraft. We compare our approach and a prior approach and demonstrate the generation of automated, artificial commentary. Introduction Let's Plays have garnered an enormous audience on websites such as Twitch and Y ouTube. At their core, Let's Plays consist of individuals playing through a segment of a video game and engaging viewers with improvised commentary, often times not related to the game itself.

Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly. In this paper, we introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. In stark contrast to most existing reading comprehension datasets where the questions focus on factual and literal understanding of the context paragraph, our dataset focuses on reading between the lines over a diverse collection of people's everyday narratives, asking such questions as "what might be the possible reason of ...?", or "what would have happened if ..." that require reasoning beyond the exact text spans in the context. To establish baseline performances on Cosmos QA, we experiment with several state-of-the-art neural architectures for reading comprehension, and also propose a new architecture that improves over the competitive baselines. Experimental results demonstrate a significant gap between machine (68.4%) and human performance (94%), pointing to avenues for future research on commonsense machine comprehension. Dataset, code and leaderboard is publicly available at https://wilburone.github.io/cosmos.

Link: Deep Learning with TensorFlow 2.0 [2019] Data Science Deep Learning Machine-Learning Scientific Libraries ... Learn about the updates being made to TensorFlow in its 2.0 version. We'll give an ... 8,767 students enrolled Created by 365 Careers, 365 Careers Team Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow Set Yourself Apart with Hands-on Deep and Machine Learning Experience Grasp the Mathematics Behind Deep Learning Algorithms Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding Some basic Python programming skills You'll need to install Anaconda. We will show you how to do it in one of the first lectures of the course. All software and data used in the course are free. Data scientists, machine learning engineers, and AI researchers all have their own skillsets.

NVIDIA today announced breakthroughs in language understanding that allow businesses to engage more naturally with customers using real-time conversational AI. NVIDIA's AI platform is the first to train one of the most advanced AI language models -- BERT -- in less than an hour and complete AI inference in just over 2 milliseconds. This groundbreaking level of performance makes it possible for developers to use state-of-the-art language understanding for large-scale applications they can make available to hundreds of millions of consumers worldwide. Early adopters of NVIDIA's performance advances include Microsoft and some of the world's most innovative startups, which are harnessing NVIDIA's platform to develop highly intuitive, immediately responsive language-based services for their customers. Limited conversational AI services have existed for several years.