6 citations · 19 across the 27 of their papers we have counts for
11 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…
Surrogate-Assisted Evolution for Efficient Multi-branch Connection Design in Deep Neural Networks
Fergal Stapleton, Daniel García Núñez, Yanan Sun +1
State-of-the-art Deep Neural Networks (DNNs) often incorporate multi-branch connections, enabling multi-scale feature extraction and enhancing the capture of diverse features. This…
Runtime Analysis of Evolutionary NAS for Multiclass Classification
Zeqiong Lv, Chao Qian, Yun Liu +2
Evolutionary neural architecture search (ENAS) is a key part of evolutionary machine learning, which commonly utilizes evolutionary algorithms (EAs) to automatically design high-pe…
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…