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Found 11 papers, 8 papers with code
Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System
Catastrophic forgetting is a challenge for model deployment in industrial real-time systems, which requires the model to quickly master a new task without forgetting the old one.
Rethinking Residual Connection in Training Large-Scale Spiking Neural Networks
Spiking Neural Network (SNN) is known as the most famous brain-inspired model, but the non-differentiable spiking mechanism makes it hard to train large-scale SNNs.
Existence and Completeness of Bounded Disturbance Observers: A Set-Membership Viewpoint
We also prove that the proposed DO has the capability to achieve the worst-case optimality, which can provide a benchmark for the design of DOs.
VisorGPT: Learning Visual Prior via Generative Pre-Training
Experimental results demonstrate that VisorGPT can effectively model the visual prior, which can be employed for many vision tasks, such as customizing accurate human pose for conditional image synthesis models like ControlNet.
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.
Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Network
Spiking neural networks (SNNs) have made great progress on both performance and efficiency over the last few years, but their unique working pattern makes it hard to train a high-performance low-latency SNN. Thus the development of SNNs still lags behind traditional artificial neural networks (ANNs). To compensate this gap, many extraordinary works have been proposed. Nevertheless, these works are mainly based on the same kind of network structure (i. e. CNN) and their performance is worse than their ANN counterparts, which limits the applications of SNNs. To this end, we propose a novel Transformer-based SNN, termed "Spikeformer", which outperforms its ANN counterpart on both static dataset and neuromorphic dataset and may be an alternative architecture to CNN for training high-performance SNNs. First, to deal with the problem of "data hungry" and the unstable training period exhibited in the vanilla model, we design the Convolutional Tokenizer (CT) module, which improves the accuracy of the original model on DVS-Gesture by more than 16%. Besides, in order to better incorporate the attention mechanism inside Transformer and the spatio-temporal information inherent to SNN, we adopt spatio-temporal attention (STA) instead of spatial-wise or temporal-wise attention. With our proposed method, we achieve competitive or state-of-the-art (SOTA) SNN performance on DVS-CIFAR10, DVS-Gesture, and ImageNet datasets with the least simulation time steps (i. e. low latency). Remarkably, our Spikeformer outperforms other SNNs on ImageNet by a large margin (i. e. more than 5%) and even outperforms its ANN counterpart by 3. 1% and 2. 2% on DVS-Gesture and ImageNet respectively, indicating that Spikeformer is a promising architecture for training large-scale SNNs and may be more suitable for SNNs compared to CNN. We believe that this work shall keep the development of SNNs in step with ANNs as much as possible. Code will be available.
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback.
RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization
Finally, we propose a method to evaluate the object detection performance of the RODNet.
One Person, One Model, One World: Learning Continual User Representation without Forgetting
In this paper, we delve on research to continually learn user representations task by task, whereby new tasks are learned while using partial parameters from old ones.
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
