6 citations · 19 across the 27 of their papers we have counts for
18 papers · 1 filter
C-Flat++: Towards a More Efficient and Powerful Framework for Continual Learning
Wei Li, Hangjie Yuan, Zixiang Zhao +4
Balancing sensitivity to new tasks and stability for retaining past knowledge is crucial in continual learning (CL). Recently, sharpness-aware minimization has proven effective in…
Loss Functions for Predictor-based Neural Architecture Search
Han Ji, Yuqi Feng, Jiahao Fan +1
Evaluation is a critical but costly procedure in neural architecture search (NAS). Performance predictors have been widely adopted to reduce evaluation costs by directly estimating…
Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective
Aojun Lu, Hangjie Yuan, Tao Feng +1
The quest for Continual Learning (CL) seeks to empower neural networks with the ability to learn and adapt incrementally. Central to this pursuit is addressing the stability-plasti…
CARL: Causality-guided Architecture Representation Learning for an Interpretable Performance Predictor
Han Ji, Yuqi Feng, Jiahao Fan +1
Performance predictors have emerged as a promising method to accelerate the evaluation stage of neural architecture search (NAS). These predictors estimate the performance of unsee…
Adapt before Continual Learning
Aojun Lu, Tao Feng, Hangjie Yuan +2
Continual Learning (CL) seeks to enable neural networks to incrementally acquire new knowledge (plasticity) while retaining existing knowledge (stability). Although pre-trained mod…
LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models
Long Chen, Xiaotian Song, Yanan Sun
Spiking Large Language Models (LLMs) have emerged as an energy-efficient alternative to conventional LLMs through their event-driven computation. To effectively obtain spiking LLMs…
E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing
Yuhao Zhou, Yuxin Tian, Mingjia Shi +4
The exponential growth in model sizes has significantly increased the communication burden in Federated Learning (FL). Existing methods to alleviate this burden by transmitting com…
Achieving Deep Continual Learning via Evolution
Aojun Lu, Junchao Ke, Chunhui Ding +3
Deep neural networks, despite their remarkable success, remain fundamentally limited in their ability to perform Continual Learning (CL). While most current methods aim to enhance…
FAS: Fast ANN-SNN Conversion for Spiking Large Language Models
Long Chen, Xiaotian Song, Andy Song +3
Spiking Large Language Models have been shown as a good alternative to LLMs in various scenarios. Existing methods for creating Spiking LLMs, i.e., direct training and ANN-SNN conv…
CAP: A Context-Aware Neural Predictor for NAS
Han Ji, Yuqi Feng, Yanan Sun
Neural predictors are effective in boosting the time-consuming performance evaluation stage in neural architecture search (NAS), owing to their direct estimation of unseen architec…
Revisiting Neural Networks for Continual Learning: An Architectural Perspective
Aojun Lu, Tao Feng, Hangjie Yuan +2
Efforts to overcome catastrophic forgetting have primarily centered around developing more effective Continual Learning (CL) methods. In contrast, less attention was devoted to ana…
Exploring the Robustness of Decentralized Training for Large Language Models
Lin Lu, Chenxi Dai, Wangcheng Tao +3
Decentralized training of large language models has emerged as an effective way to democratize this technology. However, the potential threats associated with this approach have no…
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence
Yuhao Zhou, Mingjia Shi, Yuanxi Li +3
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning. While methods utilizing spars…
Differentiable Search of Accurate and Robust Architectures
Yuwei Ou, Xiangning Xie, Shangce Gao +3
Deep neural networks (DNNs) are found to be vulnerable to adversarial attacks, and various methods have been proposed for the defense. Among these methods, adversarial training has…
DAS: Neural Architecture Search via Distinguishing Activation Score
Yuqiao Liu, Haipeng Li, Yanan Sun +1
Neural Architecture Search (NAS) is an automatic technique that can search for well-performed architectures for a specific task. Although NAS surpasses human-designed architecture…
Automating Neural Architecture Design without Search
Zixuan Liang, Yanan Sun
Neural structure search (NAS), as the mainstream approach to automate deep neural architecture design, has achieved much success in recent years. However, the performance estimatio…
PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks
Qing Ye, Yuxuan Han, Yanan sun +1
Parameter updating is an important stage in parallelism-based distributed deep learning. Synchronous methods are widely used in distributed training the Deep Neural Networks (DNNs)…
DBS: Dynamic Batch Size For Distributed Deep Neural Network Training
Qing Ye, Yuhao Zhou, Mingjia Shi +2
Synchronous strategies with data parallelism, such as the Synchronous StochasticGradient Descent (S-SGD) and the model averaging methods, are widely utilizedin distributed training…