Author name cluster

Xuxi Chen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

17 papers

2 author rows

ICLR Conference 2024 Conference Paper

Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality

Xuxi Chen
Yu Yang 0007
Zhangyang Wang
Baharan Mirzasoleiman

Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar generalization performance to that of the full dataset. However, current dataset distillation techniques fall short, showing a notable performance gap compared to training on the original data. In this work, we are the first to argue that the use of only one synthetic subset for distillation may not yield optimal generalization performance. This is because the training dynamics of deep networks drastically changes during training. Therefore, multiple synthetic subsets are required to capture the dynamics of training in different stages. To address this issue, we propose Progressive Dataset Distillation (PDD). PDD synthesizes multiple small sets of synthetic images, each conditioned on the previous sets, and trains the model on the cumulative union of these subsets without requiring additional training time. Our extensive experiments show that PDD can effectively improve the performance of existing dataset distillation methods by up to 4.3%. In addition, our method for the first time enables generating considerably larger synthetic datasets. Our codes are available at https://github.com/VITA-Group/ProgressiveDD.

Details

ICLR Conference 2024 Conference Paper

Sparse MoE with Language Guided Routing for Multilingual Machine Translation

Xinyu Zhao
Xuxi Chen
Yu Cheng 0001
Tianlong Chen 0001

Sparse Mixture-of-Experts (SMoE) has gained increasing popularity as a promising framework for scaling up multilingual machine translation (MMT) models with negligible extra computational overheads. However, current SMoE solutions neglect the intrinsic structures of the MMT problem: ($a$) $\textit{Linguistics Hierarchy.}$ Languages are naturally grouped according to their lingual properties like genetic families, phonological characteristics, etc; ($b$) $\textit{Language Complexity.}$ The learning difficulties are varied for diverse languages due to their grammar complexity, available resources, etc. Therefore, routing a fixed number of experts (e.g., $1$ or $2$ experts in usual) only at the word level leads to inferior performance. To fill in the missing puzzle, we propose $\textbf{\texttt{Lingual-SMoE}}$ by equipping the SMoE with adaptive and linguistic-guided routing policies. Specifically, it ($1$) extracts language representations to incorporate linguistic knowledge and uses them to allocate experts into different groups; ($2$) determines the number of activated experts for each target language in an adaptive and automatic manner, according to their translation difficulties, which aims to mitigate the potential over-/under-fitting issues of learning simple/challenges translations. Sufficient experimental studies on MMT benchmarks with {$16$, $50$, $100$} language pairs and various network architectures, consistently validate the superior performance of our proposals. For instance, $\texttt{Lingual-SMoE}$ outperforms its dense counterpart by over $5\%$ BLEU scores on $\texttt{OPUS-100}$ dataset.

Details

ICLR Conference 2023 Conference Paper

HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing

Tianlong Chen 0001
Chengyue Gong
Daniel Jesus Diaz
Xuxi Chen
Jordan Tyler Wells
Qiang Liu 0001
Zhangyang Wang
Andrew D. Ellington

The molecular basis of protein thermal stability is only partially understood and has major significance for drug and vaccine discovery. The lack of datasets and standardized benchmarks considerably limits learning-based discovery methods. We present \texttt{HotProtein}, a large-scale protein dataset with \textit{growth temperature} annotations of thermostability, containing $182$K amino acid sequences and $3$K folded structures from $230$ different species with a wide temperature range $-20^{\circ}\texttt{C}\sim 120^{\circ}\texttt{C}$. Due to functional domain differences and data scarcity within each species, existing methods fail to generalize well on our dataset. We address this problem through a novel learning framework, consisting of ($1$) Protein structure-aware pre-training (SAP) which leverages 3D information to enhance sequence-based pre-training; ($2$) Factorized sparse tuning (FST) that utilizes low-rank and sparse priors as an implicit regularization, together with feature augmentations. Extensive empirical studies demonstrate that our framework improves thermostability prediction compared to other deep learning models. Finally, we introduce a novel editing algorithm to efficiently generate positive amino acid mutations that improve thermostability. Codes are available in https://github.com/VITA-Group/HotProtein.

Details

ICLR Conference 2023 Conference Paper

Is Attention All That NeRF Needs?

Mukund Varma T.
Peihao Wang
Xuxi Chen
Tianlong Chen 0001
Subhashini Venugopalan
Zhangyang Wang

We present Generalizable NeRF Transformer (GNT), a transformer-based architecture that reconstructs Neural Radiance Fields (NeRFs) and learns to render novel views on the fly from source views. While prior works on NeRFs optimize a scene representation by inverting a handcrafted rendering equation, GNT achieves neural representation and rendering that generalizes across scenes using transformers at two stages. (1) The view transformer leverages multi-view geometry as an inductive bias for attention-based scene representation, and predicts coordinate-aligned features by aggregating information from epipolar lines on the neighboring views. (2) The ray transformer renders novel views using attention to decode the features from the view transformer along the sampled points during ray marching. Our experiments demonstrate that when optimized on a single scene, GNT can successfully reconstruct NeRF without an explicit rendering formula due to the learned ray renderer. When trained on multiple scenes, GNT consistently achieves state-of-the-art performance when transferring to unseen scenes and outperform all other methods by ~10% on average. Our analysis of the learned attention maps to infer depth and occlusion indicate that attention enables learning a physically-grounded rendering. Our results show the promise of transformers as a universal modeling tool for graphics. Please refer to our project page for video results: https://vita-group.github.io/GNT/

Details

ICML Conference 2023 Conference Paper

Learning to Optimize Differentiable Games

Xuxi Chen
Nelson Vadori
Tianlong Chen 0001
Zhangyang Wang

Many machine learning problems can be abstracted in solving game theory formulations and boil down to optimizing nested objectives, such as generative adversarial networks (GANs) and multi-agent reinforcement learning. Solving these games requires finding their stable fixed points or Nash equilibrium. However, existing algorithms for solving games suffer from empirical instability, hence demanding heavy ad-hoc tuning in practice. To tackle these challenges, we resort to the emerging scheme of Learning to Optimize (L2O), which discovers problem-specific efficient optimization algorithms through data-driven training. Our customized L2O framework for differentiable game theory problems, dubbed “Learning to Play Games" (L2PG), seeks a stable fixed point solution, by predicting the fast update direction from the past trajectory, with a novel gradient stability-aware, sign-based loss function. We further incorporate curriculum learning and self-learning to strengthen the empirical training stability and generalization of L2PG. On test problems including quadratic games and GANs, L2PG can substantially accelerate the convergence, and demonstrates a remarkably more stable trajectory. Codes are available at https: //github. com/VITA-Group/L2PG.

Details

ICLR Conference 2023 Conference Paper

M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation

Junjie Yang
Xuxi Chen
Tianlong Chen 0001
Zhangyang Wang
Yingbin Liang

Learning to Optimize (L2O) has drawn increasing attention as it often remarkably accelerates the optimization procedure of complex tasks by "overfitting" specific task type, leading to enhanced performance compared to analytical optimizers. Generally, L2O develops a parameterized optimization method (i.e., "optimizer") by learning from solving sample problems. This data-driven procedure yields L2O that can efficiently solve problems similar to those seen in training, that is, drawn from the same "task distribution". However, such learned optimizers often struggle when new test problems come with a substantially deviation from the training task distribution. This paper investigates a potential solution to this open challenge, by meta-training an L2O optimizer that can perform fast test-time self-adaptation to a out-of-distribution task, in only a few steps. We theoretically characterize the generalization of L2O, and further show that our proposed framework (termed as M-L2O) provably facilitates rapid task adaptation by locating well-adapted initial points for the optimizer weight. Empirical observations on several classic tasks like LASSO and Quadratic, demonstrate that M-L2O converges significantly faster than vanilla L2O with only $5$ steps of adaptation, echoing our theoretical results. Codes are available in https://github.com/VITA-Group/M-L2O.

Details

ICLR Conference 2023 Conference Paper

More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity

Shiwei Liu 0003
Tianlong Chen 0001
Xiaohan Chen 0001
Xuxi Chen
Qiao Xiao
Boqian Wu
Tommi Kärkkäinen
Mykola Pechenizkiy

Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to be challenged by increasingly effective transformer-based models. Very recently, a couple of advanced convolutional models strike back with large kernels motivated by the local-window attention mechanism, showing appealing performance and efficiency. While one of them, i.e. RepLKNet, impressively manages to scale the kernel size to 31x31 with improved performance, the performance starts to saturate as the kernel size continues growing, compared to the scaling trend of advanced ViTs such as Swin Transformer. In this paper, we explore the possibility of training extreme convolutions larger than 31x31 and test whether the performance gap can be eliminated by strategically enlarging convolutions. This study ends up with a recipe for applying extremely large kernels from the perspective of sparsity, which can smoothly scale up kernels to 61x61 with better performance. Built on this recipe, we propose Sparse Large Kernel Network (SLaK), a pure CNN architecture equipped with sparse factorized 51x51 kernels that can perform on par with or better than state-of-the-art hierarchical Transformers and modern ConvNet architectures like ConvNeXt and RepLKNet, on ImageNet classification as well as a wide range of downstream tasks including semantic segmentation on ADE20K, object detection on PASCAL VOC 2007, and object detection/segmentation on MS COCO. Codes are available at https://github.com/VITA-Group/SLaK.

Details

ICLR Conference 2023 Conference Paper

Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!

Shiwei Liu 0003
Tianlong Chen 0001
Zhenyu Zhang 0015
Xuxi Chen
Tianjin Huang
Ajay Kumar Jaiswal
Zhangyang Wang

Sparse Neural Networks (SNNs) have received voluminous attention predominantly due to growing computational and memory footprints of consistently exploding parameter count in large-scale models. Similar to their dense counterparts, recent SNNs generalize just as well and are equipped with numerous favorable benefits (e.g., low complexity, high scalability, and robustness), sometimes even better than the original dense networks. As research effort is focused on developing increasingly sophisticated sparse algorithms, it is startling that a comprehensive benchmark to evaluate the effectiveness of these algorithms has been highly overlooked. In absence of a carefully crafted evaluation benchmark, most if not all, sparse algorithms are evaluated against fairly simple and naive tasks (eg. CIFAR-10/100, ImageNet, GLUE, etc.), which can potentially camouflage many advantages as well unexpected predicaments of SNNs. In pursuit of a more general evaluation and unveiling the true potential of sparse algorithms, we introduce “Sparsity May Cry” Benchmark (SMC-Bench), a collection of carefully-curated 4 diverse tasks with 10 datasets, that accounts for capturing a wide range of domain-specific and sophisticated knowledge. Our systemic evaluation of the most representative sparse algorithms reveals an important obscured observation: the state-of-the-art magnitude- and/or gradient-based sparse algorithms seemingly fail to perform on SMC-Bench when applied out-of-the-box, sometimes at significantly trivial sparsity as low as 5%. The observations seek the immediate attention of the sparsity research community to reconsider the highly proclaimed benefits of SNNs. We further conduct a thorough investigation into the reasons for the failure of common SNNs. Our analysis points out that such failure is intimately related to the “lazy regime” of large model training, which hints us with stronger pruning recipes that alleviate the failure on SMC-Bench (though still more or less suffering). By incorporating these well-thought and diverse tasks, SMC-Bench is designed to favor and encourage the development of more scalable and generalizable sparse algorithms. We open-source SMC-Bench to assist researchers in building next-generation sparse algorithms that scale and generalize: https://github.com/VITA-Group/SMC-Bench.

Details

NeurIPS Conference 2022 Conference Paper

Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation

Ziyu Jiang
Xuxi Chen
Xueqin Huang
Xianzhi Du
Denny Zhou
Zhangyang Wang

Transfer learning from the model trained on large datasets to customized downstream tasks has been widely used as the pre-trained model can greatly boost the generalizability. However, the increasing sizes of pre-trained models also lead to a prohibitively large memory footprints for downstream transferring, making them unaffordable for personal devices. Previous work recognizes the bottleneck of the footprint to be the activation, and hence proposes various solutions such as injecting specific lite modules. In this work, we present a novel memory-efficient transfer framework called Back Razor, that can be plug-and-play applied to any pre-trained network without changing its architecture. The key idea of Back Razor is asymmetric sparsifying: pruning the activation stored for back-propagation, while keeping the forward activation dense. It is based on the observation that the stored activation, that dominates the memory footprint, is only needed for backpropagation. Such asymmetric pruning avoids affecting the precision of forward computation, thus making more aggressive pruning possible. Furthermore, we conduct the theoretical analysis for the convergence rate of Back Razor, showing that under mild conditions, our method retains the similar convergence rate as vanilla SGD. Extensive transfer learning experiments on both Convolutional Neural Networks and Vision Transformers with classification, dense prediction, and language modeling tasks show that Back Razor could yield up to 97% sparsity, saving 9. 2x memory usage, without losing accuracy. The code is available at: https: //github. com/VITA-Group/BackRazor_Neurips22.

PDF Details

ICML Conference 2022 Conference Paper

Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets

Tianlong Chen 0001
Xuxi Chen
Xiaolong Ma
Yanzhi Wang 0001
Zhangyang Wang

The lottery ticket hypothesis (LTH) has shown that dense models contain highly sparse subnetworks (i. e. , winning tickets) that can be trained in isolation to match full accuracy. Despite many exciting efforts being made, there is one "commonsense" rarely challenged: a winning ticket is found by iterative magnitude pruning (IMP) and hence the resultant pruned subnetworks have only unstructured sparsity. That gap limits the appeal of winning tickets in practice, since the highly irregular sparse patterns are challenging to accelerate on hardware. Meanwhile, directly substituting structured pruning for unstructured pruning in IMP damages performance more severely and is usually unable to locate winning tickets. In this paper, we demonstrate the first positive result that a structurally sparse winning ticket can be effectively found in general. The core idea is to append "post-processing techniques" after each round of (unstructured) IMP, to enforce the formation of structural sparsity. Specifically, we first "re-fill" pruned elements back in some channels deemed to be important, and then "re-group" non-zero elements to create flexible group-wise structural patterns. Both our identified channel- and group-wise structural subnetworks win the lottery, with substantial inference speedups readily supported by existing hardware. Extensive experiments, conducted on diverse datasets across multiple network backbones, consistently validate our proposal, showing that the hardware acceleration roadblock of LTH is now removed. Specifically, the structural winning tickets obtain up to {64. 93%, 64. 84%, 60. 23%} running time savings at {36% 80%, 74%, 58%} sparsity on {CIFAR, Tiny-ImageNet, ImageNet}, while maintaining comparable accuracy. Code is at https: //github. com/VITA-Group/Structure-LTH.

Details

NeurIPS Conference 2022 Conference Paper

Sparse Winning Tickets are Data-Efficient Image Recognizers

Mukund Varma T
Xuxi Chen
Zhenyu Zhang
Tianlong Chen
Subhashini Venugopalan
Zhangyang Wang

Improving the performance of deep networks in data-limited regimes has warranted much attention. In this work, we empirically show that “winning tickets” (small sub-networks) obtained via magnitude pruning based on the lottery ticket hypothesis, apart from being sparse are also effective recognizers in data-limited regimes. Based on extensive experiments, we find that in low data regimes (datasets of 50-100 examples per class), sparse winning tickets substantially outperform the original dense networks. This approach, when combined with augmentations or fine-tuning from a self-supervised backbone network, shows further improvements in performance by as much as 16% (absolute) on low-sample datasets and long-tailed classification. Further, sparse winning tickets are more robust to synthetic noise and distribution shifts compared to their dense counterparts. Our analysis of winning tickets on small datasets indicates that, though sparse, the networks retain density in the initial layers and their representations are more generalizable. Code is available at https: //github. com/VITA-Group/DataEfficientLTH.

PDF Details

ICML Conference 2021 Conference Paper

A Unified Lottery Ticket Hypothesis for Graph Neural Networks

Tianlong Chen 0001
Yongduo Sui
Xuxi Chen
Aston Zhang
Zhangyang Wang

With graphs rapidly growing in size and deeper graph neural networks (GNNs) emerging, the training and inference of GNNs become increasingly expensive. Existing network weight pruning algorithms cannot address the main space and computational bottleneck in GNNs, caused by the size and connectivity of the graph. To this end, this paper first presents a unified GNN sparsification (UGS) framework that simultaneously prunes the graph adjacency matrix and the model weights, for effectively accelerating GNN inference on large-scale graphs. Leveraging this new tool, we further generalize the recently popular lottery ticket hypothesis to GNNs for the first time, by defining a graph lottery ticket (GLT) as a pair of core sub-dataset and sparse sub-network, which can be jointly identified from the original GNN and the full dense graph by iteratively applying UGS. Like its counterpart in convolutional neural networks, GLT can be trained in isolation to match the performance of training with the full model and graph, and can be drawn from both randomly initialized and self-supervised pre-trained GNNs. Our proposal has been experimentally verified across various GNN architectures and diverse tasks, on both small-scale graph datasets (Cora, Citeseer and PubMed), and large-scale datasets from the challenging Open Graph Benchmark (OGB). Specifically, for node classification, our found GLTs achieve the same accuracies with 20% 98% MACs saving on small graphs and 25% 85% MACs saving on large ones. For link prediction, GLTs lead to 48% 97% and 70% MACs saving on small and large graph datasets, respectively, without compromising predictive performance. Codes are at https: //github. com/VITA-Group/Unified-LTH-GNN.

Details

ICML Conference 2021 Conference Paper

Efficient Lottery Ticket Finding: Less Data is More

Zhenyu Zhang 0015
Xuxi Chen
Tianlong Chen 0001
Zhangyang Wang

The lottery ticket hypothesis (LTH) reveals the existence of winning tickets (sparse but critical subnetworks) for dense networks, that can be trained in isolation from random initialization to match the latter’s accuracies. However, finding winning tickets requires burdensome computations in the train-prune-retrain process, especially on large-scale datasets (e. g. , ImageNet), restricting their practical benefits. This paper explores a new perspective on finding lottery tickets more efficiently, by doing so only with a specially selected subset of data, called Pruning-Aware Critical set (PrAC set), rather than using the full training set. The concept of PrAC set was inspired by the recent observation, that deep networks have samples that are either hard to memorize during training, or easy to forget during pruning. A PrAC set is thus hypothesized to capture those most challenging and informative examples for the dense model. We observe that a high-quality winning ticket can be found with training and pruning the dense network on the very compact PrAC set, which can substantially save training iterations for the ticket finding process. Extensive experiments validate our proposal across diverse datasets and network architectures. Specifically, on CIFAR-10, CIFAR-100, and Tiny ImageNet, we locate effective PrAC sets at 35. 32% 78. 19% of their training set sizes. On top of them, we can obtain the same competitive winning tickets for the corresponding dense networks, yet saving up to 82. 85% 92. 77%, 63. 54% 74. 92%, and 76. 14% 86. 56% training iterations, respectively. Crucially, we show that a PrAC set found is reusable across different network architectures, which can amortize the extra cost of finding PrAC sets, yielding a practical regime for efficient lottery ticket finding.

Details

ICLR Conference 2021 Conference Paper

GANs Can Play Lottery Tickets Too

Xuxi Chen
Zhenyu Zhang 0015
Yongduo Sui
Tianlong Chen 0001

Deep generative adversarial networks (GANs) have gained growing popularity in numerous scenarios, while usually suffer from high parameter complexities for resource-constrained real-world applications. However, the compression of GANs has less been explored. A few works show that heuristically applying compression techniques normally leads to unsatisfactory results, due to the notorious training instability of GANs. In parallel, the lottery ticket hypothesis shows prevailing success on discriminative models, in locating sparse matching subnetworks capable of training in isolation to full model performance. In this work, we for the first time study the existence of such trainable matching subnetworks in deep GANs. For a range of GANs, we certainly find matching subnetworks at $67\%$-$74\%$ sparsity. We observe that with or without pruning discriminator has a minor effect on the existence and quality of matching subnetworks, while the initialization weights used in the discriminator plays a significant role. We then show the powerful transferability of these subnetworks to unseen tasks. Furthermore, extensive experimental results demonstrate that our found subnetworks substantially outperform previous state-of-the-art GAN compression approaches in both image generation (e.g. SNGAN) and image-to-image translation GANs (e.g. CycleGAN). Codes available at https://github.com/VITA-Group/GAN-LTH.

Details

NeurIPS Conference 2021 Conference Paper

Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?

Xiaolong Ma
Geng Yuan
Xuan Shen
Tianlong Chen
Xuxi Chen
Xiaohan Chen
Ning Liu
Minghai Qin

There have been long-standing controversies and inconsistencies over the experiment setup and criteria for identifying the "winning ticket" in literature. To reconcile such, we revisit the definition of lottery ticket hypothesis, with comprehensive and more rigorous conditions. Under our new definition, we show concrete evidence to clarify whether the winning ticket exists across the major DNN architectures and/or applications. Through extensive experiments, we perform quantitative analysis on the correlations between winning tickets and various experimental factors, and empirically study the patterns of our observations. We find that the key training hyperparameters, such as learning rate and training epochs, as well as the architecture characteristics such as capacities and residual connections, are all highly correlated with whether and when the winning tickets can be identified. Based on our analysis, we summarize a guideline for parameter settings in regards of specific architecture characteristics, which we hope to catalyze the research progress on the topic of lottery ticket hypothesis. Our codes are publicly available at: https: //github. com/boone891214/sanity-check-LTH.

PDF Details

NeurIPS Conference 2021 Conference Paper

You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership

Xuxi Chen
Tianlong Chen
Zhenyu Zhang
Zhangyang Wang

Despite tremendous success in many application scenarios, the training and inference costs of using deep learning are also rapidly increasing over time. The lottery ticket hypothesis (LTH) emerges as a promising framework to leverage a special sparse subnetwork (i. e. , $\textit{winning ticket}$) instead of a full model for both training and inference, that can lower both costs without sacrificing the performance. The main resource bottleneck of LTH is however the extraordinary cost to find the sparse mask of the winning ticket. That makes the found winning ticket become a valuable asset to the owners, highlighting the necessity of protecting its copyright. Our setting adds a new dimension to the recently soaring interest in protecting against the intellectual property (IP) infringement of deep models and verifying their ownerships, since they take owners' massive/unique resources to develop or train. While existing methods explored encrypted weights or predictions, we investigate a unique way to leverage sparse topological information to perform $\textit{lottery verification}$, by developing several graph-based signatures that can be embedded as credentials. By further combining trigger set-based methods, our proposal can work in both white-box and black-box verification scenarios. Through extensive experiments, we demonstrate the effectiveness of lottery verification in diverse models (ResNet-20, ResNet-18, ResNet-50) on CIFAR-10 and CIFAR-100. Specifically, our verification is shown to be robust to removal attacks such as model fine-tuning and pruning, as well as several ambiguity attacks. Our codes are available at https: //github. com/VITA-Group/NO-stealing-LTH.

PDF Details

ICML Conference 2020 Conference Paper

Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training

Xuxi Chen
Wuyang Chen 0001
Tianlong Chen 0001
Ye Yuan 0012
Chen Gong
Kewei Chen 0001
Zhangyang Wang

Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i. e. , learning binary classifiers from a large amount of unlabeled data and a few labeled positive examples. While current state-of-the-art methods employ importance reweighting to design various biased or unbiased risk estimators, they completely ignored the learning capability of the model itself, which could provide reliable supervision. This motivates us to propose a novel Self-PU learning framework, which seamlessly integrates PU learning and self-training. Self-PU highlights three “self”-oriented building blocks: a self-paced training algorithm that adaptively discovers and augments confident positive/negative examples as the training proceeds; a self-reweighted, instance-aware loss; and a self-distillation scheme that introduces teacher-students learning as an effective regularization for PU learning. We demonstrate the state-of-the-art performance of Self-PU on common PU learning benchmarks (MNIST and CIFAR10), which compare favorably against the latest competitors. Moreover, we study a real-world application of PU learning, i. e. , classifying brain images of Alzheimer’s Disease. Self-PU obtains significantly improved results on the renowned Alzheimer’s Disease Neuroimaging Initiative (ADNI) database over existing methods.

Details