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Found 13 papers, 3 papers with code
Reinforcement Learning with Generalizable Gaussian Splatting
The quality of the environment representation directly influences the achievement of the learning task.
Spiking Wavelet Transformer
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by mimicking the event-driven processing of the brain.
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model
Large Vision-Language Models (LVLMs) rely on vision encoders and Large Language Models (LLMs) to exhibit remarkable capabilities on various multi-modal tasks in the joint space of vision and language.
Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks
Spiking Neural Networks (SNNs) have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs).
Pursing the Sparse Limitation of Spiking Deep Learning Structures
It posits that within dense neural networks, there exist winning tickets or subnetworks that are sparser but do not compromise performance.
Fully Spiking Neural Network for Legged Robots
In recent years, legged robots based on deep reinforcement learning have made remarkable progress.
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias
Recently, there has been a surge of interest and attention in Transformer-based structures, such as Vision Transformer (ViT) and Vision Multilayer Perceptron (VMLP).
Gaining the Sparse Rewards by Exploring Lottery Tickets in Spiking Neural Network
Deploying energy-efficient deep learning algorithms on computational-limited devices, such as robots, is still a pressing issue for real-world applications.
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera
The ability to detect objects in all lighting (i. e., normal-, over-, and under-exposed) conditions is crucial for real-world applications, such as self-driving. Traditional RGB-based detectors often fail under such varying lighting conditions. Therefore, recent works utilize novel event cameras to supplement or guide the RGB modality; however, these methods typically adopt asymmetric network structures that rely predominantly on the RGB modality, resulting in limited robustness for all-day detection.
Spiking Denoising Diffusion Probabilistic Models
In our paper, we put forward Spiking Denoising Diffusion Probabilistic Models (SDDPM), a new class of SNN-based generative models that achieve high sample quality.
Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces
Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) Algebraic perspective, (2) Geometric perspective, and (3) Analytical perspective.
Masked Spiking Transformer
The combination of Spiking Neural Networks (SNNs) and Transformers has attracted significant attention due to their potential for high energy efficiency and high-performance nature.
Deep Learning for Stock Selection Based on High Frequency Price-Volume Data
Training a practical and effective model for stock selection has been a greatly concerned problem in the field of artificial intelligence.
