We consider the problem of distributed feature quantization, where the goal is to enable a pretrained classifier at a central node to carry out its classification on features that are gathered from distributed nodes through communication constrained channels. We propose the design of distributed quantization schemes specifically tailored to the classification task: unlike quantization schemes that help the central node reconstruct the original signal as accurately as possible, our focus is not reconstruction accuracy, but instead correct classification. Our work does not make any apriori distributional assumptions on the data, but instead uses training data for the quantizer design. Our main contributions include: we prove NP-hardness of finding optimal quantizers in the general case; we design an optimal scheme for a special case; we propose quantization algorithms, that leverage discrete neural representations and training data, and can be designed in polynomial-time for any number of features, any number of classes, and arbitrary division of features across the distributed nodes. We find that tailoring the quantizers to the classification task can offer significant savings: as compared to alternatives, we can achieve more than a factor of two reduction in terms of the number of bits communicated, for the same classification accuracy.

This paper addresses the challenging task of video captioning which aims to generate descriptions for video data. Recently, the attention-based encoder-decoder structures have been widely used in video captioning. In existing literature, the attention weights are often built from the information of an individual modality, while, the association relationships between multiple modalities are neglected. Motivated by this observation, we propose a video captioning model with High-Order Cross-Modal Attention (HOCA) where the attention weights are calculated based on the high-order correlation tensor to capture the frame-level cross-modal interaction of different modalities sufficiently. Furthermore, we novelly introduce Low-Rank HOCA which adopts tensor decomposition to reduce the extremely large space requirement of HOCA, leading to a practical and efficient implementation in real-world applications. Experimental results on two benchmark datasets, MSVD and MSR-VTT, show that Low-rank HOCA establishes a new state-of-the-art.

By analyzing Bayesian inference of generative model for random networks with both relations (edges) and node features (discrete labels), we perform an asymptotically exact analysis of the semi-supervised classfication problems on graph-structured data using the cavity method of statistical physics. We unveil detectability phase transitions which put fundamental limit on ability of classfications for all possible algorithms. Our theory naturally converts to a message passing algorithm which works all the way down to the phase transition in the underlying generative model, and can be translated to a graph convolution neural network algorithm which greatly outperforms existing algorithms including popular graph neural networks in synthetic networks. When applied to real-world datasets, our algorithm achieves comparable performance with the state-of-the art algorithms. Our approach provides benchmark datasets with continuously tunable parameters and optimal results, which can be used to evaluate performance of exiting graph neural networks, and to find and understand their strengths and limitations. In particular, we observe that popular GCNs have sparsity issue and ovefitting issue on large synthetic benchmarks, we also show how to overcome the issues by combining strengths of our approach.

The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here, we propose an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video. Our approach builds upon variational autoencoder (VAE) models for sequential data and combines them with recent work on neural image compression. The approach jointly learns to transform the original sequence into a lower-dimensional representation as well as to discretize and entropy code this representation according to predictions of the sequential VAE. Rate-distortion evaluations on small videos from public data sets with varying complexity and diversity show that our model yields competitive results when trained on generic video content. Extreme compression performance is achieved when training the model on specialized content.

They can take many forms, ranging from everyday explanations for questions such as why one likes Star Wars, to sophisticated formalization in the philosophy of science (Salmon, 2006), to simply highlighting features in recent work on interpretable machine learning (Ribeiro et al., 2016). Although everyday explanations are mostly encoded in natural language, natural language explanations remain understudied in NLP, partly due to a lack of appropriate datasets and problem formulations. To address these challenges, we leverage /r/ChangeMyView, a community dedicated to sharing counterarguments to controversial views on Reddit, to build a sizable dataset of naturally-occurring explanations. Specifically, in /r/ChangeMyView, an original poster (OP) first delineates the rationales for a (controversial) opinion (e.g., in Table 1, "most hit music artists today are bad musicians"). Members of /r/ChangeMyVieware invited to provide counterarguments. If a counterargument changes the OP's view, the OP awards a to indicate the change and is required to explain why the counterargument is persuasive . In this work, we refer to what is being explained, including both the original post and the persuasive comment, as the explanandum.

DD-PPO is distributed (uses multiple machines), decentralized (lacks a centralized server), and synchronous (no computation is ever'stale'), making it conceptually simple and easy to implement. In our experiments on training virtual robots to navigate in Habitat-Sim (Savva et al., 2019), DD-PPO exhibits near-linear scaling - achieving a speedup of 107x on 128 GPUs over a serial implementation. We leverage this scaling to train an agent for 2.5 Billion steps of experience (the equivalent of 80 years of human experience) - over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs. This massive-scale training not only sets the state of art on Habitat Autonomous Navigation Challenge 2019, but essentially'solves' the task - near-perfect autonomous navigation in an unseen environment without access to a map, directly from an RGB-D camera and a GPS Compass sensor. Fortuitously, error vs computation exhibits a power-law-like distribution; thus, 90% of peak performance is obtained relatively early (at 100 million steps) and relatively cheaply (under 1 day with 8 GPUs). Finally, we show that the scene understanding and navigation policies learned can be transferred to other navigation tasks - the analog of'ImageNet pre-training task-specific fine-tuning' for embodied AI. Our model outperforms ImageNet pre-trained CNNs on these transfer tasks and can serve as a universal resource (all models code will be publicly available). 1 I NTRODUCTION Recent advances in deep reinforcement learning (RL) have given rise to systems that can outperform human experts at variety of games (Silver et al., 2017; Tian et al., 2019; OpenAI, 2018). These advances, even more-so than those from supervised learning, rely on significant numbers of training samples, making them impractical without large-scale, distributed parallelization. Thus, scaling RL via multi-node distribution is of importance to AI - that is the focus of this work. Several works have proposed systems for distributed RL (Heess et al., 2017; Liang et al., 2018; Tian et al., 2019; Silver et al., 2016; OpenAI, 2018; Espeholt et al., 2018). These works utilize two core components: 1) workers that collect experience ('rollout workers'), and 2) a parameter server that optimizes the model. The rollout workers are then distributed across, potentially, thousands of CPUs 1 .

Data scientists and machine learning engineers use containers to create custom, lightweight environments to train and serve models at scale with deep learning frameworks such as TensorFlow, Apache MXNet, and PyTorch. With containers, developers get consistent environments for development and deployment. In this tech talk, we'll show you how to use AWS Deep Learning Containers to train and serve models at scale with Amazon SageMaker.

DeepMind today announced a new milestone for its artificial intelligence agents trained to play the Blizzard Entertainment game StarCraft II. The Google-owned AI lab's more sophisticated software, still called AlphaStar, is now grandmaster level in the real-time strategy game, capable of besting 99.8 percent of all human players in competition. The findings are to be published in a research paper in the scientific journal Nature. Not only that, but DeepMind says it also evened the playing field when testing the new and improved AlphaStar against human opponents who opted into online competitions this past summer. For one, it trained AlphaStar to use all three of the game's playable races, adding to the complexity of the game at the upper echelons of pro play.

The Roguelike Celebration happened earlier this month at GitHub in San Francisco. Fiction writer Robin Sloan gave a talk, "Writing with the machine: GPT-2 and text generation" at the conference. In this project, Sloan utilized OpenAI's GPT-2 algorithm to write short fantasy stories with help from the general public. To keep things literary, Sloan created a backstory for these stories. In a nutshell, a group of wizards is simulating quests to defeat the dark lord of the land.

I'm answering questions that AI/ML/CV people not familiar with graphs or graph neural networks typically ask. I provide PyTorch examples to clarify the idea behind this relatively new and exciting kind of model. To answer them, I'll provide motivating examples, papers and Python code making it a tutorial on Graph Neural Networks (GNNs). Some basic knowledge of machine learning and computer vision is expected, however, I'll provide some background and intuitive explanation as we go. First of all, let's briefly recall what is a graph?