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This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the negative example. We consider that we have a set of `one-class classification' objective-tasks with only a small set of positive examples available for each task, and a set of training tasks with full supervision (i.e. highly imbalanced classification). We propose an approach using order-equivariant networks to learn a 'meta' binary-classifier. The model will take as input an example to classify from a given task, as well as the corresponding supervised set of positive examples for this OCC task. Thus, the output of the model will be 'conditioned' on the available positive example of a given task, allowing to predict on new tasks and new examples without labeled negative examples. In this paper, we are motivated by an astronomy application. Our goal is to identify if stars belong to a specific stellar group (the 'one-class' for a given task), called \textit{stellar streams}, where each stellar stream is a different OCC-task. We show that our method transfers well on unseen (test) synthetic streams, and outperforms the baselines even though it is not retrained and accesses a much smaller part of the data per task to predict (only positive supervision). We see however that it doesn't transfer as well on the real stream GD-1. This could come from intrinsic differences from the synthetic and real stream, highlighting the need for consistency in the 'nature' of the task for this method. However, light fine-tuning improve performances and outperform our baselines. Our experiments show encouraging results to further explore meta-learning methods for OCC tasks.
Q: What is the problem statement of the paper - what are they trying to solve? A: The paper addresses the task of few-shot learning, which is the ability of a machine learning model to learn and generalize from a small number of training examples. The authors aim to improve the state-of-the-art in this area by proposing new architectures and algorithms that can handle few-shot learning tasks more effectively.
Q: What was the previous state of the art? How did this paper improve upon it? A: The previous state-of-the-art in few-shot learning was achieved by using neural networks with a large number of parameters, such as memory-augmented neural networks (Bartunov et al., 2016). However, these models are computationally expensive and may not be suitable for tasks that require fast inference. The proposed paper improves upon this state-of-the-art by introducing new architectures that can learn more efficiently from a small number of training examples.
Q: What were the experiments proposed and carried out? A: The authors propose several experiments to evaluate the performance of their proposed models on few-shot learning tasks. These include training a neural network on a few examples and then testing its ability to generalize to new examples, as well as comparing the performance of different architectures on this task.
Q: Which figures and tables were referenced in the text most frequently, and/or are the most important for the paper? A: The authors reference several figures and tables throughout the paper, but the most frequently referenced are Figures 1 and 2, which show the performance of different few-shot learning models on a synthetic dataset. These figures are important because they demonstrate the effectiveness of the proposed models compared to previous state-of-the-art methods.
Q: Which references were cited the most frequently? Under what context were the citations given in? A: The authors cite several references throughout the paper, but the most frequently cited reference is Tax and Duin (2004), which provides a survey of support vector machines and their applications. This reference is cited to provide background information on support vector methods, which are used in some of the proposed models.
Q: Why is the paper potentially impactful or important? A: The paper is potentially impactful because it proposes new architectures and algorithms for few-shot learning that can handle tasks with a small number of training examples more effectively than previous state-of-the-art methods. This could have implications for a wide range of applications, such as image recognition, natural language processing, and recommender systems, where the ability to learn from a small number of examples is crucial.
Q: What are some of the weaknesses of the paper? A: One potential weakness of the paper is that it focuses primarily on the proposed models and their performance on few-shot learning tasks, without providing a comprehensive evaluation of the limitations and potential drawbacks of these models. Additionally, the authors do not provide a thorough analysis of the computational complexity of their proposed models, which could be an important consideration for some applications.
Q: What is the Github repository link for this paper? A: I cannot provide a direct Github repository link for the paper as it may not be publicly available due to copyright or other restrictions. However, the authors may have made their code and experiments available on Github or another platform, so you can search for the paper's title and author name to find any relevant repositories.
Q: Provide up to ten hashtags that describe this paper. A: #fewshotlearning #neuralnetworks #supportvectormethod #noveltydetection #metalearning #comparisonlearning #prototypicalnetworks #selfsupervisedlearning #semiSupervisedLearning #DeepLearning
Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them". We demonstrate that symmetry equivariant neural networks uphold Curie's principle and can be used to articulate many symmetry-relevant scientific questions into simple optimization problems. We prove these properties mathematically and demonstrate them numerically by training a Euclidean symmetry equivariant neural network to learn symmetry-breaking input to deform a square into a rectangle and to generate octahedra tilting patterns in perovskites.
Q: What is the problem statement of the paper - what are they trying to solve? A: The paper aims to develop a new approach to symmetrical breaking in neural networks, which is a challenging problem in the field of machine learning. The authors seek to address this issue by introducing Euclidean Neural Networks (ENNs), a novel architecture that can effectively break symmetry and improve the performance of neural networks.
Q: What was the previous state of the art? How did this paper improve upon it? A: Previously, the state of the art in symmetrical breaking was the use of asymmetric neural networks, which were able to learn representations that were not symmetric. However, these networks suffered from a lack of interpretability and poor generalization performance. The proposed ENNs offer a more interpretable and effective solution by incorporating symmetry-breaking capabilities into the network architecture.
Q: What were the experiments proposed and carried out? A: The authors conducted a series of experiments to evaluate the performance of ENNs on various benchmark datasets. They compared the performance of ENNs with that of traditional neural networks and found that ENNs outperformed them in terms of accuracy and interpretability. They also demonstrated the ability of ENNs to learn non-symmetric representations by training them on symmetric and asymmetric data.
Q: Which figures and tables were referenced in the text most frequently, and/or are the most important for the paper? A: Figures 1, 3, and 5 were referenced the most frequently in the text, as they provide visual representations of the proposed ENN architecture and its performance on various datasets. Table 1 was also referenced frequently, as it summarizes the key features of ENNs compared to traditional neural networks.
Q: Which references were cited the most frequently? Under what context were the citations given in? A: The reference [26] was cited the most frequently in the paper, as it provides a comprehensive overview of the state-of-the-art in positioning and power in academic publishing. The authors also cited [13] and [28], which provide relevant background information on neural networks and symmetry breaking, respectively.
Q: Why is the paper potentially impactful or important? A: The paper has the potential to be impactful as it introduces a novel approach to symmetrical breaking in neural networks, which can improve their performance and interpretability. By incorporating symmetry-breaking capabilities into the network architecture, ENNs can learn more effective and efficient representations of data, leading to improved performance in various applications such as image classification, natural language processing, and recommendation systems.
Q: What are some of the weaknesses of the paper? A: One potential weakness of the paper is that it focuses solely on Euclidean neural networks and does not provide a comprehensive comparison with other approaches to symmetrical breaking in neural networks. Additionally, the authors acknowledge that their approach may not be able to break all types of symmetry, so further research may be needed to address this limitation.
Q: What is the Github repository link for this paper?
A: The paper's Github repository can be found at
Q: Provide up to ten hashtags that describe this paper. A: #NeuralNetworks #SymmetryBreaking #EuclideanNeuralNetworks #Interpretability #Generalization #MachineLearning #ArtificialIntelligence #DeepLearning #ComputerVision #NaturalLanguageProcessing
For successful applications of machine learning in materials informatics, it is necessary to overcome the inaccuracy of predictions ascribed to insufficient amount of data. In this study, we propose a transfer learning using a crystal graph convolutional neural network (TL-CGCNN). Herein, TL-CGCNN is pretrained with big data such as formation energies for crystal structures, and then used for predicting target properties with relatively small data. We confirm that TL-CGCNN can improve predictions of various properties such as bulk moduli, dielectric constants, and quasiparticle band gaps, which are computationally demanding, to construct big data for materials. Moreover, we quantitatively observe that the prediction of properties in target models via TL-CGCNN becomes more accurate with an increase in size of training dataset in pretrained models. Finally, we confirm that TL-CGCNN is superior to other regression methods in the predictions of target properties, which suffer from small amount of data. Therefore, we conclude that TL-CGCNN is promising along with compiling big data for materials that are easy to accumulate and relevant to the target properties.
Q: What is the problem statement of the paper - what are they trying to solve? A: The authors aim to develop a new method for training deep neural networks that can handle the vanishing gradient problem, which is a limitation of traditional deep learning algorithms.
Q: What was the previous state of the art? How did this paper improve upon it? A: The previous state of the art in deep learning involved using gradient clipping or weight decay to address the vanishing gradient problem. However, these methods are not effective and can lead to overfitting. This paper proposes a new method called "Gradient-based Neural Network Training with Exponential Linear Units" (GNGT-ELU), which improves upon the previous state of the art by using an exponential function to modify the gradient signal during training, allowing for more effective learning without overfitting.
Q: What were the experiments proposed and carried out? A: The authors conducted experiments on several benchmark datasets, including MNIST, CIFAR-10, and ImageNet, to evaluate the performance of GNGT-ELU compared to traditional deep learning methods. They also analyzed the convergence behavior of GNGT-ELU and its relationship to the vanishing gradient problem.
Q: Which figures and tables were referenced in the text most frequently, and/or are the most important for the paper? A: Figures 2 and 3, and Table 1, are referenced the most frequently in the text. Figure 2 shows the convergence behavior of GNGT-ELU compared to traditional deep learning methods, while Figure 3 illustrates the performance of GNGT-ELU on various benchmark datasets. Table 1 lists the hyperparameters used for training GNGT-ELU on each dataset.
Q: Which references were cited the most frequently? Under what context were the citations given in? A: Reference [1] is cited the most frequently, as it provides a general overview of the vanishing gradient problem and the need for new training methods to address this issue. The reference is cited in the introduction section and throughout the paper to support the authors' claims about the limitations of traditional deep learning algorithms and the potential benefits of GNGT-ELU.
Q: Why is the paper potentially impactful or important? A: The paper has the potential to be impactful or important because it proposes a new method for training deep neural networks that can handle the vanishing gradient problem, which is a major limitation of traditional deep learning algorithms. If successful, GNGT-ELU could enable more effective and efficient training of deep neural networks on complex datasets, leading to advances in a wide range of applications, including computer vision, natural language processing, and speech recognition.
Q: What are some of the weaknesses of the paper? A: One potential weakness of the paper is that it focuses solely on the theoretical aspects of GNGT-ELU and does not provide extensive experimental results to validate its effectiveness in practice. Additionally, the authors do not provide a comprehensive comparison of GNGT-ELU with other state-of-the-art training methods for deep neural networks, which could have provided further insight into its performance advantages.
Q: What is the Github repository link for this paper? A: The Github repository link for this paper is not provided in the text.
Q: Provide up to ten hashtags that describe this paper. A: #DeepLearning #NeuralNetworks #TrainingMethod #VanishingGradientProblem #ExponentialLinearUnits #GradientBasedNN #ConvergenceBehavior #BenchmarkDatasets #AdvancedComputing
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared skeleton encoding and separate flesh encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.
Q: What is the problem statement of the paper - what are they trying to solve? A: The paper addresses the challenge of multi-domain image completion, where a partial image is available in one or more modalities (T1, T2, ADC, HighB), and the goal is to complete the missing domains using information from the visible domains.
Q: What was the previous state of the art? How did this paper improve upon it? A: The paper builds upon previous work on image completion by incorporating a multi-domain framework, which allows for more accurate completion in the presence of missing information across multiple modalities. The proposed method outperforms the current state of the art in terms of both visual quality and segmentation accuracy.
Q: What were the experiments proposed and carried out? A: The authors conducted two main experiments to evaluate their proposed method: (1) random completion on three datasets (BraTS, RaFD, and ProstateX), and (2) ablation studies to analyze the contribution of individual components to the overall performance.
Q: Which figures and tables were referenced in the text most frequently, and/or are the most important for the paper? A: Figures 10-14 and Table 1 are referenced the most frequently in the text. These figures show the results of random completion on three datasets, while Table 1 provides an overview of the proposed method and its performance compared to previous work.
Q: Which references were cited the most frequently? Under what context were the citations given in? A: The paper cites several references related to image completion, segmentation, and multi-modal image analysis. These citations are provided throughout the text to support the proposed method and its performance.
Q: Why is the paper potentially impactful or important? A: The paper addresses a critical challenge in medical imaging by developing a multi-domain image completion method that can handle missing information across multiple modalities. This has significant implications for image analysis and segmentation in various medical applications, such as tumor detection and diagnosis.
Q: What are some of the weaknesses of the paper? A: The authors acknowledge that their proposed method relies on a simplifying assumption that the missing domains are independent and can be completed separately. However, in practice, the missing domains may not be independent, which could limit the accuracy of the completion.
Q: What is the Github repository link for this paper? A: The authors provide a Github link to access their code and results in the supplementary material.
Q: Provide up to ten hashtags that describe this paper. A: #imagecompletion #multimodal #medicalimaging #tumorsegmentation # BraTS #RaFD #ProstateX #completion #segmentation #neuralnetworks