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Found 45 papers, 10 papers with code
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training
Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel ``copy/new'' feedback paradigm to help shape the signal generation of the subject toward the optimal distribution; 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process.
Mitigating Communication Costs in Neural Networks: The Role of Dendritic Nonlinearity
Our comprehension of biological neuronal networks has profoundly influenced the evolution of artificial neural networks (ANNs).
ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks
To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training.
Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN
However, due to the quantization error and accumulating error, it often requires lots of time steps (high inference latency) to achieve high performance, which negates SNN's advantages.
Prompt ChatGPT In MNER: Improved multimodal named entity recognition method based on auxiliary refining knowledge from ChatGPT
To address these problems, we propose a conceptually simple two-stage framework called Prompt ChatGPT In MNER (PGIM) in this paper.
NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models
NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach.
Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks
The proposed method explores a novel dynamical way for structure learning from scratch in SNNs which could build a bridge to close the gap between deep learning and bio-inspired neural dynamics.
Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation
Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, close to the biological neural systems.
SceneCalib: Automatic Targetless Calibration of Cameras and Lidars in Autonomous Driving
Accurate camera-to-lidar calibration is a requirement for sensor data fusion in many 3D perception tasks.
DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering
Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance.
Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks
Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low energy consumption.
Constrained Update Projection Approach to Safe Policy Optimization
Compared to previous safe RL methods, CUP enjoys the benefits of 1) CUP generalizes the surrogate functions to generalized advantage estimator (GAE), leading to strong empirical performance.
NeIF: Representing General Reflectance as Neural Intrinsics Fields for Uncalibrated Photometric Stereo
Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by unknown light.
SPAIC: A Spike-based Artificial Intelligence Computing Framework
In this work, we present a Python based spiking neural network (SNN) simulation and training framework, aka SPAIC that aims to support brain-inspired model and algorithm researches integrated with features from both deep learning and neuroscience.
TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources
In this work, we propose TinyLight, the first DRL-based ATSC model that is designed for devices with extremely limited resources.
Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-machine Interface
In this way, DyEnsemble copes with variability in signals and improves the robustness of online control.
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning
Although using bounds as surrogate functions to design safe RL algorithms have appeared in some existing works, we develop them at least three aspects: (i) We provide a rigorous theoretical analysis to extend the surrogate functions to generalized advantage estimator (GAE).
Rethinking Sampling Strategies for Unsupervised Person Re-identification
While extensive research has focused on the framework design and loss function, this paper shows that sampling strategy plays an equally important role.
Indoor Lighting Estimation Using an Event Camera
Image-based methods for indoor lighting estimation suffer from the problem of intensity-distance ambiguity.
Thompson Sampling for Unimodal Bandits
In this paper, we propose a Thompson Sampling algorithm for \emph{unimodal} bandits, where the expected reward is unimodal over the partially ordered arms.
Optimize Neural Fictitious Self-Play in Regret Minimization Thinking
Optimization of deep learning algorithms to approach Nash Equilibrium remains a significant problem in imperfect information games, e. g. StarCraft and poker.
Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN
Reconstructing seeing images from fMRI recordings is an absorbing research area in neuroscience and provides a potential brain-reading technology.
On Convergence of Gradient Expected Sarsa($λ$)
We study the convergence of $\mathtt{Expected~Sarsa}(\lambda)$ with linear function approximation.
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points
However, due to the inherent non-concavity of its objective, convergence to a first-order stationary point (FOSP) can not guarantee the policy gradient methods finding a maximal point.
Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks
Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditional frame-based cameras.
Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons
This paper proposes an unsupervised address event representation (AER) object recognition approach.
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces
Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities.
Gradient Q$(σ, λ)$: A Unified Algorithm with Function Approximation for Reinforcement Learning
To address above problem, we propose a GQ$(\sigma,\lambda)$ that extends tabular Q$(\sigma,\lambda)$ with linear function approximation.
FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control
Alternatively, derivative-based methods treat the optimization process as a blackbox and show robustness and stability in learning continuous control tasks, but not data efficient in learning.
Expected Sarsa($λ$) with Control Variate for Variance Reduction
In this paper, for reducing the variance, we introduce control variate technique to $\mathtt{Expected}$ $\mathtt{Sarsa}$($\lambda$) and propose a tabular $\mathtt{ES}$($\lambda$)-$\mathtt{CV}$ algorithm.
Policy Optimization with Stochastic Mirror Descent
Improving sample efficiency has been a longstanding goal in reinforcement learning.
Brain Network Construction and Classification Toolbox (BrainNetClass)
The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods.
TBQ($σ$): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning
However, existing off-policy learning methods based on probabilistic policy measurement are inefficient when utilizing traces under a greedy target policy, which is ineffective for control problems.
Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games
Experimentally, we demonstrate that the proposed Monte Carlo Neural Fictitious Self Play can converge to approximate Nash equilibrium in games with large-scale search depth while the Neural Fictitious Self Play can't.
Field-aware Neural Factorization Machine for Click-Through Rate Prediction
This model can have strong second order feature interactive learning ability like Field-aware Factorization Machine, on this basis, deep neural network is used for higher-order feature combination learning.
Efficient Spiking Neural Networks with Logarithmic Temporal Coding
A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it into an SNN.
Qualitative Measurements of Policy Discrepancy for Return-Based Deep Q-Network
In this paper, we propose a general framework to combine DQN and most of the return-based reinforcement learning algorithms, named R-DQN.
Spiking Deep Residual Network
SNNs theoretically have at least the same computational power as traditional artificial neural networks (ANNs).
State Distribution-aware Sampling for Deep Q-learning
A critical and challenging problem in reinforcement learning is how to learn the state-action value function from the experience replay buffer and simultaneously keep sample efficiency and faster convergence to a high quality solution.
A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning
Results show that, with an intermediate value of $\sigma$, $Q(\sigma ,\lambda)$ creates a mixture of the existing algorithms that can learn the optimal value significantly faster than the extreme end ($\sigma=0$, or $1$).
Spectral-graph Based Classifications: Linear Regression for Classification and Normalized Radial Basis Function Network
The spectral graph theory provides us with a new insight into a fundamental aspect of classification: the tradeoff between fitting error and overfitting risk.
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
Object detection systems based on the deep convolutional neural network (CNN) have recently made ground- breaking advances on several object detection benchmarks.
Robust Face Recognition by Constrained Part-based Alignment
By assuming a human face as piece-wise planar surfaces, where each surface corresponds to a facial part, we develop in this paper a Constrained Part-based Alignment (CPA) algorithm for face recognition across pose and/or expression.
Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut
The methods include PCA, K-means, Laplacian eigenmap (LE), ratio cut (Rcut), and a new sparse representation method developed by us, called spectral sparse representation (SSR).
Sparse Principal Component Analysis via Rotation and Truncation
In contrast to most of existing work which deal with the problem by adding some sparsity penalties on various objectives of PCA, in this paper, we propose a new method SPCArt, whose motivation is to find a rotation matrix and a sparse basis such that the sparse basis approximates the basis of PCA after the rotation.
