Articles About Machine Learning

Soft policy optimization using dual-track advantage estimator

In reinforcement learning (RL), we always expect the agent to explore as many states as possible in the initial stage of training and exploit the explored information in the subsequent stage to discover the most returnable trajectory. Based on this principle, in this paper, we soften the proximal policy optimization by introducing the entropy and dynamically setting the temperature coefficient to balance the opportunity of exploration and exploitation… While maximizing the expected reward, the agent will also seek other trajectories […]

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Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup

While deep neural networks achieve great performance on fitting the training distribution, the learned networks are prone to overfitting and are susceptible to adversarial attacks. In this regard, a number of mixup based augmentation methods have been recently proposed… However, these approaches mainly focus on creating previously unseen virtual examples and can sometimes provide misleading supervisory signal to the network. To this end, we propose Puzzle Mix, a mixup method for explicitly utilizing the saliency information and the underlying statistics […]

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AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images… The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track […]

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Switching Gradient Directions for Query-Efficient Black-Box Adversarial Attacks

We propose a simple and highly query-efficient black-box adversarial attack named SWITCH, which has a state-of-the-art performance under $ell_2$ and $ell_infty$ norms in the score-based setting. In the black box attack setting, designing query-efficient attacks remains an open problem… The high query efficiency of the proposed approach stems from the combination of transfer-based attacks and random-search-based ones. The surrogate model’s gradient $hat{mathbf{g}}$ is exploited for the guidance, which is then switched if our algorithm detects that it does not point […]

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Dialogue Response Ranking Training with Large-Scale Human Feedback Data

Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning more followup interactions… Current conversational models are increasingly capable of producing turns that are context-relevant, but in order to produce compelling agents, these models need to be able to predict and optimize for turns that are genuinely engaging. We leverage social media feedback data (number of replies and upvotes) to build a large-scale training dataset […]

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A Self Contour-based Rotation and Translation-Invariant Transformation for Point Clouds Recognition

Recently, several direct processing point cloud models have achieved state-of-the-art performances for classification and segmentation tasks. However, these methods lack rotation robustness, and their performances degrade severely under random rotations, failing to extend to real-world applications with varying orientations… To address this problem, we propose a method named Self Contour-based Transformation (SCT), which can be flexibly integrated into a variety of existing point cloud recognition models against arbitrary rotations without any extra modifications. The SCT provides efficient and mathematically proved […]

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RaLL: End-to-end Radar Localization on Lidar Map Using Differentiable Measurement Model

Radar sensor provides lighting and weather invariant sensing, which is naturally suitable for long-term localization in outdoor scenes. On the other hand, the most popular available map currently is built by lidar… In this paper, we propose a deep neural network for end-to-end learning of radar localization on lidar map to bridge the gap. We first embed both sensor modals into a common feature space by a neural network. Then multiple offsets are added to the map modal for similarity […]

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AttnGrounder: Talking to Cars with Attention

We propose Attention Grounder (AttnGrounder), a single-stage end-to-end trainable model for the task of visual grounding. Visual grounding aims to localize a specific object in an image based on a given natural language text query… Unlike previous methods that use the same text representation for every image region, we use a visual-text attention module that relates each word in the given query with every region in the corresponding image for constructing a region dependent text representation. Furthermore, for improving the […]

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Manifold attack

Machine Learning in general and Deep Learning in particular has gained much interest in the recent decade and has shown significant performance improvements for many Computer Vision or Natural Language Processing tasks. In order to deal with databases which have just a small amount of training samples or to deal with models which have large amount of parameters, the regularization is indispensable… In this paper, we enforce the manifold preservation (manifold learning) from the original data into latent presentation by […]

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Simple Simultaneous Ensemble Learning in Genetic Programming

Learning ensembles by bagging can substantially improve the generalization performance of low-bias high-variance estimators, including those evolved by Genetic Programming (GP). Yet, the best way to learn ensembles in GP remains to be determined… This work attempts to fill the gap between existing GP ensemble learning algorithms, which are often either simple but expensive, or efficient but complex. We propose a new algorithm that is both simple and efficient, named Simple Simultaneous Ensemble Genetic Programming (2SEGP). 2SEGP is obtained by […]

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