EMO: Episodic Memory Optimization for Few-Shot Meta-Learning

Few-shot meta-learning presents a challenge for gradient descent optimization due to the limited number of training samples per task. To address this issue, we propose an episodic memory optimization for meta-learning, we call \emph{EMO}, which is inspired by the human ability to recall past learning experiences from the brain's memory. EMO retains the gradient history of past experienced tasks in external memory, enabling few-shot learning in a memory-augmented way. By learning to retain and recall the learning process of past training tasks, EMO nudges parameter updates in the right direction, even when the gradients provided by a limited number of examples are uninformative. We prove theoretically that our algorithm converges for smooth, strongly convex objectives. EMO is generic, flexible, and model-agnostic, making it a simple plug-and-play optimizer that can be seamlessly embedded into existing optimization-based few-shot meta-learning approaches. Empirical results show that EMO scales well with most few-shot classification benchmarks and improves the performance of optimization-based meta-learning methods, resulting in accelerated convergence.Comment: Accepted by CoLLAs 202

Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph Generation

This paper investigates the problem of scene graph generation in videos with the aim of capturing semantic relations between subjects and objects in the form of $\langle$subject, predicate, object$\rangle$ triplets. Recognizing the predicate between subject and object pairs is imbalanced and multi-label in nature, ranging from ubiquitous interactions such as spatial relationships (\eg \emph{in front of}) to rare interactions such as \emph{twisting}. In widely-used benchmarks such as Action Genome and VidOR, the imbalance ratio between the most and least frequent predicates reaches 3,218 and 3,408, respectively, surpassing even benchmarks specifically designed for long-tailed recognition. Due to the long-tailed distributions and label co-occurrences, recent state-of-the-art methods predominantly focus on the most frequently occurring predicate classes, ignoring those in the long tail. In this paper, we analyze the limitations of current approaches for scene graph generation in videos and identify a one-to-one correspondence between predicate frequency and recall performance. To make the step towards unbiased scene graph generation in videos, we introduce a multi-label meta-learning framework to deal with the biased predicate distribution. Our meta-learning framework learns a meta-weight network for each training sample over all possible label losses. We evaluate our approach on the Action Genome and VidOR benchmarks by building upon two current state-of-the-art methods for each benchmark. The experiments demonstrate that the multi-label meta-weight network improves the performance for predicates in the long tail without compromising performance for head classes, resulting in better overall performance and favorable generalizability. Code: \url{https://github.com/shanshuo/ML-MWN}.Comment: ICMR 202

Learning to Learn Variational Semantic Memory

In this paper, we introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning. The variational semantic memory accrues and stores semantic information for the probabilistic inference of class prototypes in a hierarchical Bayesian framework. The semantic memory is grown from scratch and gradually consolidated by absorbing information from tasks it experiences. By doing so, it is able to accumulate long-term, general knowledge that enables it to learn new concepts of objects. We formulate memory recall as the variational inference of a latent memory variable from addressed contents, which offers a principled way to adapt the knowledge to individual tasks. Our variational semantic memory, as a new long-term memory module, confers principled recall and update mechanisms that enable semantic information to be efficiently accrued and adapted for few-shot learning. Experiments demonstrate that the probabilistic modelling of prototypes achieves a more informative representation of object classes compared to deterministic vectors. The consistent new state-of-the-art performance on four benchmarks shows the benefit of variational semantic memory in boosting few-shot recognition.Comment: accepted to NeurIPS 2020; code is available in https://github.com/YDU-uva/VS

Learning to Learn Kernels with Variational Random Features

In this work, we introduce kernels with random Fourier features in the meta-learning framework to leverage their strong few-shot learning ability. We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable. We formulate the optimization of MetaVRF as a variational inference problem by deriving an evidence lower bound under the meta-learning framework. To incorporate shared knowledge from related tasks, we propose a context inference of the posterior, which is established by an LSTM architecture. The LSTM-based inference network can effectively integrate the context information of previous tasks with task-specific information, generating informative and adaptive features. The learned MetaVRF can produce kernels of high representational power with a relatively low spectral sampling rate and also enables fast adaptation to new tasks. Experimental results on a variety of few-shot regression and classification tasks demonstrate that MetaVRF delivers much better, or at least competitive, performance compared to existing meta-learning alternatives.Comment: ICML'2020; code is available in: https://github.com/Yingjun-Du/MetaVR

Wildfires enhance phytoplankton production in tropical oceans

Unidad de excelencia María de Maeztu CEX2019-000940-MWildfire magnitude and frequency have greatly escalated on a global scale. Wildfire products rich in biogenic elements can enter the ocean through atmospheric and river inputs, but their contribution to marine phytoplankton production is poorly understood. Here, using geochemical paleo-reconstructions, a century-long relationship between wildfire magnitude and marine phytoplankton production is established in a fire-prone region of Kimberley coast, Australia. A positive correlation is identified between wildfire and phytoplankton production on a decadal scale. The importance of wildfire on marine phytoplankton production is statistically higher than that of tropical cyclones and rainfall, when strong El Niño Southern Oscillation coincides with the positive phase of Indian Ocean Dipole. Interdecadal chlorophyll-a variation along the Kimberley coast validates the spatial connection of this phenomenon. Findings from this study suggest that the role of additional nutrients from wildfires has to be considered when projecting impacts of global warming on marine phytoplankton production

Multiple-relaxation-time lattice Boltzmann model for compressible fluids

We present an energy-conserving multiple-relaxation-time finite difference lattice Boltzmann model for compressible flows. This model is based on a 16-discrete-velocity model. The collision step is first calculated in the moment space and then mapped back to the velocity space. The moment space and corresponding transformation matrix are constructed according to the group representation theory. Equilibria of the nonconserved moments are chosen according to the need of recovering compressible Navier-Stokes equations through the Chapman-Enskog expansion. Numerical experiments showed that compressible flows with strong shocks can be well simulated by the present model. The used benchmark tests include (i) shock tubes, such as the Sod, Lax, Sjogreen, Colella explosion wave and collision of two strong shocks, (ii) regular and Mach shock reflections, and (iii) shock wave reaction on cylindrical bubble problems. The new model works for both low and high speeds compressible flows. It contains more physical information and has better numerical stability and accuracy than its single-relaxation-time version.Comment: 11 figures, Revte

Wildfires enhance phytoplankton production in tropical oceans

Wildfire magnitude and frequency have greatly escalated on a global scale. Wildfire products rich in biogenic elements can enter the ocean through atmospheric and river inputs, but their contribution to marine phytoplankton production is poorly understood. Here, using geochemical paleo-reconstructions, a century-long relationship between wildfire magnitude and marine phytoplankton production is established in a fire-prone region of Kimberley coast, Australia. A positive correlation is identified between wildfire and phytoplankton production on a decadal scale. The importance of wildfire on marine phytoplankton production is statistically higher than that of tropical cyclones and rainfall, when strong El Niño Southern Oscillation coincides with the positive phase of Indian Ocean Dipole. Interdecadal chlorophyll-a variation along the Kimberley coast validates the spatial connection of this phenomenon. Findings from this study suggest that the role of additional nutrients from wildfires has to be considered when projecting impacts of global warming on marine phytoplankton production

SNX14 deficiency-induced defective axonal mitochondrial transport in Purkinje cells underlies cerebellar ataxia and can be reversed by valproate

共济失调是一类以运动协调性紊乱为主要特征的神经系统症状，临床表现包括步态不稳、丧失平衡、吞咽困难、眼球运动异常、肌张力受损等。厦门大学医学院神经科学研究所王鑫教授团队首次从轴突线粒体运输这一全新视角揭示了一类遗传性共济失调的发病机制，并发现抗癫痫药--丙戊酸大幅度减缓模型小鼠的疾病进程，具有较强的转化应用价值，有望为共济失调提供新的治疗手段。 该研究工作由王鑫教授指导完成，厦门大学医学院助理教授张洪峰和博士生洪育娟共同完成主要实验工作。Loss-of-function mutations in SNX14 cause autosomal recessive spinocerebellar ataxia 20, which is a form of early-onset cerebellar ataxia that lacks molecular mechanisms and mouse models. We generated Snx14-deficient mouse models and observed severe motor deficits and cell-autonomous Purkinje cell degeneration. SNX14 deficiency disrupted microtubule organization and mitochondrial transport in axons by destabilizing the microtubule-severing enzyme spastin, which is implicated in dominant hereditary spastic paraplegia with cerebellar ataxia, and compromised axonal integrity and mitochondrial function. Axonal transport disruption and mitochondrial dysfunction further led to degeneration of high-energy-demanding Purkinje cells, which resulted in the pathogenesis of cerebellar ataxia. The antiepileptic drug valproate ameliorated motor deficits and cerebellar degeneration in Snx14-deficient mice via the restoration of mitochondrial transport and function in Purkinje cells. Our study revealed an unprecedented role for SNX14-dependent axonal transport in cerebellar ataxia, demonstrated the convergence of SNX14 and spastin in mitochondrial dysfunction, and suggests valproate as a potential therapeutic agent.We thank Tim Huang for helpful discussion, Wei Mo for sharing mouse lines, Li Zhong for sharing reagents, Aidong Han, Luming Yao, Caiming Wu, Mingxia Zhu, Qingfeng Liu, Lin Zhu, Shuo Zhang, Haiping Zheng, and Changchuan Xie for technical assistance, and Cui Li for providing bioinformatics software. We also thank Novogene Co., Ltd. and PTM Biolab Co., Ltd. for technical assistance in the transcriptomic and proteomic analyses, respectively. 厦门大学医学院许华曦、赵颖俊、张云武、杜丹教授在研究过程中给予大力帮助和支持。本研究工作得到国家重点研发计划项目、国家自然科学基金、福建省自然科学基金、厦门大学校长基金的资助和支持