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arXiv 2011.01272cs.LGstat.ML
Modular-Relatedness for Continual Learning
Ammar Shaker, Shujian Yu, Francesco Alesiani
备注：
We realized one conclusion in the submission is erroneous and disconnected from the results shown in one theorem is. We decide to withdraw the current version to avoid misleading conclusion
In this paper, we propose a continual learning (CL) technique that is beneficial to sequential task learners by improving their retained accuracy and reducing catastrophic forgetting. The principal target of our approach is the automatic extraction of modular parts of the neural network and then estimating the relatedness between the tasks given these modular components. This technique is applicable to different families of CL methods such as regularization-based (e.g., the Elastic Weight Consolidation) or the rehearsal-based (e.g., the Gradient Episodic Memory) approaches where episodic memory is needed. Empirical results demonstrate remarkable performance gain (in terms of robustness to forgetting) for methods such as EWC and GEM based on our technique, especially when the memory budget is very limited.
Rehearsal-Free Modular and Compositional Continual Learning for Language Models
Bosch Center for Artificial Intelligence（博世人工智能中心）；LMU Munich（慕尼黑大学）；Hochschule der Medien（斯图加特媒体大学）；Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用技术大学）
A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities. We first introduce a visual interaction environment that allows many types of tasks to be unified in a single framework. We then describe a reward map prediction scheme that learns new tasks robustly in the very large state and action spaces required by such an environment. We investigate how properties of module architecture influence efficiency of task learning, showing that a module …
Brain-Inspired Hierarchical Modularity for General Continual Learning
Tsinghua University（清华大学）；IDG/McGovern Institute for Brain Research, Tsinghua University（清华大学IDG/麦戈文脑科学研究院）；Tsinghua-Peking Center for Life Sciences（清华-北大生命科学联合中心）
Existing literature in Continual Learning (CL) has focused on overcoming catastrophic forgetting, the inability of the learner to recall how to perform tasks observed in the past. There are however other desirable properties of a CL system, such as the ability to transfer knowledge from previous tasks and to scale memory and compute sub-linearly with the number of tasks. Since most current benchmarks focus only on forgetting using short streams of tasks, we first propose a new suite of benchmarks to probe CL algorithms across these new axes. Finally, we introduce a new modular architecture, wh…
NavInfo Europe（四维图新欧洲）；Eindhoven University of Technology（埃因霍温理工大学）
Real-world applications often require learning continuously from a stream of data under ever-changing conditions. When trying to learn from such non-stationary data, deep neural networks (DNNs) undergo catastrophic forgetting of previously learned information. Among the common approaches to avoid catastrophic forgetting, rehearsal-based methods have proven effective. However, they are still prone to forgetting due to task-interference as all parameters respond to all tasks. To counter this, we take inspiration from sparse coding in the brain and introduce dynamic modularity and sparsity (Dynam…
Modular Delta Merging with Orthogonal Constraints: A Scalable Framework for Continual and Reversible Model Composition
Rensselaer Polytechnic Institute（伦斯勒理工学院）；National University of Sciences and Technology (NUST)（国立科技大学）；Virginia Tech（弗吉尼亚理工大学）；University of New Mexico（新墨西哥大学）
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Google（谷歌）
Continual learning in neural networks suffers from a phenomenon called catastrophic forgetting, in which a network quickly forgets what was learned in a previous task. The human brain, however, is able to continually learn new tasks and accumulate knowledge throughout life. Neuroscience findings suggest that continual learning success in the human brain is potentially associated with its modular structure and memory consolidation mechanisms. In this paper we propose a novel topological regularization that penalizes cycle structure in a neural network during training using principled theory fro…
Position: Modular Memory is the Key to Continual Learning Agents
University of Bremen（不莱梅大学）；Seoul National University（首尔国立大学）；Computer Vision Center Barcelona（巴塞罗那计算机视觉中心）；University of Florence（佛罗伦萨大学）；Microsoft Research（微软研究院）；HEC Montreal, Mila--Quebec AI Institute, Canada CIFAR AI Chair（蒙特利尔HEC学院、魁北克人工智能研究所、加拿大CIFAR人工智能 chair）；Bielefeld University（比勒海姆大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Rochester（罗切斯特大学）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Nankai University（南开大学）；LUISS University（卢西亚诺大学）；Stony Brook University（石溪大学）；University of Tübingen（图宾根大学）；University of Trento, FBK（特伦托大学，FBK）；Tsinghua University（清华大学）；University of Groningen（Groningen大学）
Human intelligence is characterized not only by the capacity to learn complex skills, but the ability to rapidly adapt and acquire new skills within an ever-changing environment. In this work we study how the learning of modular solutions can allow for effective generalization to both unseen and potentially differently distributed data. Our main postulate is that the combination of task segmentation, modular learning and memory-based ensembling can give rise to generalization on an exponentially growing number of unseen tasks. We provide a concrete instantiation of this idea using a combinatio…
ServiceNow；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；HEC Montréal（蒙特利尔高等商学院）
Modularity is a compelling solution to continual learning (CL), the problem of modeling sequences of related tasks. Learning and then composing modules to solve different tasks provides an abstraction to address the principal challenges of CL including catastrophic forgetting, backward and forward transfer across tasks, and sub-linear model growth. We introduce local module composition (LMC), an approach to modular CL where each module is provided a local structural component that estimates a module's relevance to the input. Dynamic module composition is performed layer-wise based on local rel…
University of Pisa（比萨大学）；University of Bologna（博洛尼亚大学）；Scuola Normale Superiore（比萨高等师范学校）；University of St. Gallen（圣加仑大学）
Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time. Unfortunately, deep learning libraries only provide primitives for offline training, assuming that model's architecture and data are fixed. Avalanche is an open source library maintained by the ContinualAI non-profit organization that extends PyTorch by providing first-class support for dynamic architectures, streams of datasets, and incremental training and evaluation methods. Avalanche provides a large set of predefin…
Several families of continual learning techniques have been proposed to alleviate catastrophic interference in deep neural network training on non-stationary data. However, a comprehensive comparison and analysis of limitations remains largely open due to the inaccessibility to suitable datasets. Empirical examination not only varies immensely between individual works, it further currently relies on contrived composition of benchmarks through subdivision and concatenation of various prevalent static vision datasets. In this work, our goal is to bridge this gap by introducing a computer graphic…
Click-through rate(CTR) prediction is a core task in cost-per-click(CPC) advertising systems and has been studied extensively by machine learning practitioners. While many existing methods have been successfully deployed in practice, most of them are built upon i.i.d.(independent and identically distributed) assumption, ignoring that the click data used for training and inference is collected through time and is intrinsically non-stationary and drifting. This mismatch will inevitably lead to sub-optimal performance. To address this problem, we formulate CTR prediction as a continual learning t…
Knowledge Fusion of Large Language Models Via Modular SkillPacks
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The Hong Kong Polytechnic University（香港理工大学）；Nanyang Technological University（南洋理工大学）；Huazhong University of Science and Technology（华中科技大学）
The Future of Continual Learning in the Era of Foundation Models: Three Key Directions
Department of Computer Science, Università di Pisa（帕尔马大学计算机科学系）；Institute of Information Science and Technologies, National Research Council（信息科学与技术研究所，国家研究理事会）
Institute of Electronics, National Chiao Tung University（国立交通大学电子研究所）
Deep neural networks (DNNs) are known to perform well when deployed to test distributions that shares high similarity with the training distribution. Feeding DNNs with new data sequentially that were unseen in the training distribution has two major challenges -- fast adaptation to new tasks and catastrophic forgetting of old tasks. Such difficulties paved way for the on-going research on few-shot learning and continual learning. To tackle these problems, we introduce Attentive Independent Mechanisms (AIM). We incorporate the idea of learning using fast and slow weights in conjunction with the…
A Comprehensive Empirical Evaluation on Online Continual Learning
Computer Vision Center（计算机视觉中心）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；University of Pisa（比萨大学）；Scuola Normale Superiore（比萨高等师范学院）；University of St. Gallen（圣加仑大学）
CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion
Technical University of Munich（慕尼黑技术大学）；TUM School of Computation, Information and Technology（TUM计算、信息与技术学院）；Department of Computer Engineering, Learning Systems and Robotics Lab（计算机工程系、学习系统与机器人实验室）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI））；Robotics Institute Germany（德国机器人研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）
FlyPrompt: Brain-Inspired Random-Expanded Routing with Temporal-Ensemble Experts for General Continual Learning
School of Life Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University（生命科学学院，IDG/麦克格雷文脑科学研究院，清华大学）；Department of Psychological and Cognitive Sciences, Tsinghua University（心理学与认知科学系，清华大学）；School of Medicine, Shenzhen Campus of Sun Yat-Sen University（中山大学深圳校区医学学院）
Towards General Industrial Intelligence: A Survey of Continual Large Models in Industrial IoT
Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（华南理工大学伍舜德智能工程学院）；School of Mechanical and Automotive Engineering, South China University of Technology（华南理工大学机械与汽车工程学院）；Pazhou Lab（琶洲实验室）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC学院）；Key Laboratory of Maritime Intelligent Network Information Technology, Ministry of Education, Hohai University（河海大学海洋智能网络信息技术教育部重点实验室）
LibContinual: A Comprehensive Library towards Realistic Continual Learning
State Key Laboratory for Novel Software Technology, Nanjing University, China（南京大学新型软件技术国家重点实验室）；School of Computing and Information Technology, University of Wollongong, Australia（沃林戈大学计算与信息科技学院）；Department of Computer Science, University of Rochester, USA（罗切斯特大学计算机科学系）
Self-Composing Policies for Scalable Continual Reinforcement Learning
Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Donostia-San Sebastian, Spain（计算机科学与人工智能系，巴斯克国家大学UPV/EHU，圣地亚哥-桑塞班西斯，西班牙）；Basque Center for Applied Mathematics (BCAM), Bilbao, Spain（应用数学巴斯克中心（BCAM），毕尔巴鄂，西班牙）
Lifelong learning is a very important step toward realizing robust autonomous artificial agents. Neural networks are the main engine of deep learning, which is the current state-of-the-art technique in formulating adaptive artificial intelligent systems. However, neural networks suffer from catastrophic forgetting when stressed with the challenge of continual learning. We investigate how to exploit modular topology in neural networks in order to dynamically balance the information load between different modules by routing inputs based on the information content in each module so that informati…
Carnegie Mellon University（卡内基梅隆大学）；Georgia Institute of Technology（佐治亚理工学院）
The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries, and thus depend on human supervision. Our task-agnostic method, Self-Activating Neural Ensembles (SANE), uses a modular architecture designed to avoid catastrophic forgetting without making any such assumptions. At the beginning of each trajectory, a module in the SANE ensemble is activated to determine the agent'…
Dynamic Mixture of Progressive Parameter-Efficient Expert Library for Lifelong Robot Learning
The University of Hong Kong（香港大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究院（TeleAI），中国电信）；Huawei Cloud Computing Technologies（华为云计算技术）；Ola Dimensions；HKU Shanghai Intelligent Computing Research Center（香港大学上海智能计算研究中心）
Applied AI Institute, Deakin University（迪肯大学应用人工智能研究所）；Deakin University（迪肯大学）；Geelong, Australia（澳大利亚吉朗（此处为地址，按要求不提取，仅保留大学及所属研究所））
Artificial Neural Networks are uniquely adroit at machine learning by processing data through a network of artificial neurons. The inter-neuronal connection weights represent the learnt Neural Program that instructs the network on how to compute the data. However, without an external memory to store Neural Programs, they are restricted to only one, overwriting learnt programs when trained on new data. This is functionally equivalent to a special-purpose computer. Here we design Neurocoder, an entirely new class of general-purpose conditional computational machines in which the neural network "…
