Supervised learning is the crown jewel of Machine Learning. Supervised learning is the machine learning task or process of producing a function that predicts output variables. It has been adopted widely in the industry. For example, banks apply supervised models to detect credit card fraud. Quantitative traders make purchase decisions based on ML model predictions.

Supervised learning is the crown jewel of Machine Learning. Supervised learning is the machine learning task or process of producing a function that predicts output variables. It has been adopted widely in the industry. For example, banks apply supervised models to detect credit card fraud. Quantitative traders make purchase decisions based on ML model predictions.

We present a simple, flexible, and general framework titled Partial Registration Network (PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed learning-based methods for registration, we use deep networks to tackle non-convexity of the alignment and partial correspondence problem. While previous learning-based methods assume the entire shape is visible, PRNet is suitable for partial-to-partial registration, outperforming PointNetLK, DCP, and non-learning methods on synthetic data. PRNet is self-supervised, jointly learning an appropriate geometric representation, a keypoint detector that finds points in common between partial views, and keypoint-to-keypoint correspondences. We show PRNet predicts keypoints and correspondences consistently across views and objects. Furthermore, the learned representation is transferable to classification.

Reinforcement learning offers the promise of automating the acquisition of complex behavioral skills. However, compared to commonly used and well-understood supervised learning methods, reinforcement learning algorithms can be brittle, difficult to use and tune, and sensitive to seemingly innocuous implementation decisions. In contrast, imitation learning utilizes standard and well-understood supervised learning methods, but requires near-optimal expert data. Can we learn effective policies via supervised learning without demonstrations? The main idea that we explore in this work is that non-expert trajectories collected from sub-optimal policies can be viewed as optimal supervision, not for maximizing the reward, but for matching the reward of the given trajectory. By then conditioning the policy on the numerical value of the reward, we can obtain a policy that generalizes to larger returns. We show how such an approach can be derived as a principled method for policy search, discuss several variants, and compare the method experimentally to a variety of current reinforcement learning methods on standard benchmarks.

Some of the most important tasks take place in environments which lack cheap and perfect simulators, thus hampering the application of model-free reinforcement learning (RL). While model-based RL aims to learn a dynamics model, in a more general case the learner does not know a priori what the action space is. Here we propose a formalism where the learner induces a world program by learning a dynamics model and the actions in graph-based compositional environments by observing state-state transition examples. Then, the learner can perform RL with the world program as the simulator for complex planning tasks. We highlight a recent application, and propose a challenge for the community to assess world program-based planning.

Complementary-label learning is a new weakly-supervised learning framework that solves the problem where each training example is supplied with a complementary label, which only specifies one of the classes that the example does \textsl{not} belong to. Although a few works have demonstrated that an unbiased estimator of the original classification risk can be obtained from only complementarily labeled data, they are all restricted to the case where each example is associated with exactly one complementary label. It would be more promising to learn from multiple complementary labels simultaneously, as the supervision information would be richer if more complementary labels are provided. So far, whether there exists an unbiased risk estimator for learning from multiple complementary labels simultaneously is still unknown. In this paper, we will give an affirmative answer by deriving the first unbiased risk estimator for learning from multiple complementary labels. In addition, we further theoretically analyze the estimation error bound of our proposed approach, and show that the optimal parametric convergence rate is achieved. Finally, we experimentally demonstrate the effectiveness of the proposed approach.

Active learning methods, like uncertainty sampling, combined with probabilistic prediction techniques have achieved success in various problems like image classification and text classification. For more complex multivariate prediction tasks, the relationships between labels play an important role in designing structured classifiers with better performance. However, computational time complexity limits prevalent probabilistic methods from effectively supporting active learning. Specifically, while non-probabilistic methods based on structured support vector machines can be tractably applied to predicting bipartite matchings, conditional random fields are intractable for these structures. We propose an adversarial approach for active learning with structured prediction domains that is tractable for matching. We evaluate this approach algorithmically in an important structured prediction problems: object tracking in videos. We demonstrate better accuracy and computational efficiency for our proposed method.

Despite the promising results obtained with [representations developed from Web image], the experiments demonstrate that object classification with real-life robotic data is far from being solved."

Multiple Pretext-T ask for Self-Supervised Learning via Mixing Multiple Image Transformations Shin'ya Y amaguchi, Sekitoshi Kanai, Tetsuya Shioda, Shoichiro Takeda NTT Tokyo, Japan {shinya.yamaguchi.mw,sekitoshi.kanai.fu,tetsuya.shioda.yf,shoichiro.takeda.us}@hco.ntt.co.jp Abstract Self-supervised learning is one of the most promising approaches to learn representations capturing semantic features in images without any manual annotation cost. T o learn useful representations, a self-supervised model solves a pretext-task, which is defined by data itself. Among a number of pretext-tasks, the rotation prediction task (Rotation) achieves better representations for solving various target tasks despite its simplicity of the implementation. However, we found that Rotation can fail to capture semantic features related to image textures and colors. T o tackle this problem, we introduce a learning technique called multiple pretext-task for self-supervised learning (MP-SSL), which solves multiple pretext-task in addition to Rotation simultaneously. In order to capture features of textures and colors, we employ the transformations of image enhancements (e.g., sharpening and solarizing) as the additional pretext-tasks. MP-SSL efficiently trains a model by leveraging a Frank-W olfe based multi-task training algorithm. Our experimental results show MP-SSL models outperform Rotation on multiple standard benchmarks and achieve state-of- the-art performance on Places-205. 1. Introduction Convolutional neural networks (CNNs) [27, 16, 44] are widely adopted to solve many target tasks in applications of computer vision such as object recognition [30], semantic segmentation [4], and object detection [42]. However, these successes depend on supervised training of CNNs with the vast amount of labeled data [43], which is expensive and impractical because of the manual annotation cost. Since the cost of labeled data limits the practical applications of CNNs, a number of researches focus on the training techniques to alleviate the requirement of many labeled data; the techniques include transfer learning, semi-supervised learning, and self-supervised learning . A demonstration describing our motivation to modify self-supervised learning by predicting rotations of images (Rotation).

The successful application of deep learning to many visual recognition tasks relies heavily on the availability of a large amount of labeled data which is usually expensive to obtain. The few-shot learning problem has attracted increasing attention from researchers for building a robust model upon only a few labeled samples. Most existing works tackle this problem under the meta-learning framework by mimicking the few-shot learning task with an episodic training strategy. In this paper, we propose a new transfer-learning framework for semi-supervised few-shot learning to fully utilize the auxiliary information from labeled base-class data and unlabeled novel-class data. The framework consists of three components: 1) pre-training a feature extractor on base-class data; 2) using the feature extractor to initialize the classifier weights for the novel classes; and 3) further updating the model with a semi-supervised learning method. Under the proposed framework, we develop a novel method for semi-supervised few-shot learning called TransMatch by instantiating the three components with Imprinting and MixMatch. Extensive experiments on two popular benchmark datasets for few-shot learning, CUB-200-2011 and miniImageNet, demonstrate that our proposed method can effectively utilize the auxiliary information from labeled base-class data and unlabeled novel-class data to significantly improve the accuracy of few-shot learning task.