Search Results for author:
Found 32 papers, 15 papers with code
Where to Attack: A Dynamic Locator Model for Backdoor Attack in Text Classifications
Based on the predicted P2P, four effective strategies are introduced to show the BDA performance.
CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data
In the realm of medical 3D data, such as CT and MRI images, prevalent anisotropic resolution is characterized by high intra-slice but diminished inter-slice resolution.
Incorporating Graph Attention Mechanism into Geometric Problem Solving Based on Deep Reinforcement Learning
However, adding auxiliary components automatically is challenging due to the complexity in selecting suitable auxiliary components especially when pivotal decisions have to be made.
Optimal ANN-SNN Conversion with Group Neurons
For instance, while converting artificial neural networks (ANNs) to SNNs circumvents the need for direct training of SNNs, it encounters issues related to conversion errors and high inference time delays.
Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket
To the best of our knowledge, this is the first time that the SNN achieves 80+% accuracy on ImageNet.
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties.
Analyzing and controlling diversity in quantum-behaved particle swarm optimization
Then, the correlations between the two types of diversities and the search performance are tested and analyzed on several benchmark functions, and the distance-to-average-point diversity is showed to have stronger association with the search performance during the evolving processes.
Auto-Spikformer: Spikformer Architecture Search
The integration of self-attention mechanisms into Spiking Neural Networks (SNNs) has garnered considerable interest in the realm of advanced deep learning, primarily due to their biological properties.
Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering
We propose EAR, a query Expansion And Reranking approach for improving passage retrieval, with the application to open-domain question answering.
Temporal Contrastive Learning for Spiking Neural Networks
However, in this work, we find the method above is not ideal for the SNNs training as it omits the temporal dynamics of SNNs and degrades the performance quickly with the decrease of inference time steps.
Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal dynamics, which can only be simulated serially and can hardly learn long-time dependencies.
Interpretable Unified Language Checking
Despite recent concerns about undesirable behaviors generated by large language models (LLMs), including non-factual, biased, and hateful language, we find LLMs are inherent multi-task language checkers based on their latent representations of natural and social knowledge.
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
In this paper, we theoretically analyze ANN-SNN conversion error and derive the estimated activation function of SNNs.
A Unified Framework for Soft Threshold Pruning
In this work, we reformulate soft threshold pruning as an implicit optimization problem solved using the Iterative Shrinkage-Thresholding Algorithm (ISTA), a classic method from the fields of sparse recovery and compressed sensing.
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases.
Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning
We design Semantic Attention (SA) and generative Knowledge Disentanglement (KD) to learn the dependence of feasibility and contextuality, respectively.
Neuron with Steady Response Leads to Better Generalization
Based on the analysis results, we articulate the Neuron Steadiness Hypothesis: the neuron with similar responses to instances of the same class leads to better generalization.
Pruning of Deep Spiking Neural Networks through Gradient Rewiring
Our key innovation is to redefine the gradient to a new synaptic parameter, allowing better exploration of network structures by taking full advantage of the competition between pruning and regrowth of connections.
A Symbolic Regression Method for Dynamic Modeling and Control of Quadrotor UAVs
This paper presents a mathematic dynamic model of a quadrotor unmanned aerial vehicle (QUAV) by using the symbolic regression approach and then proposes a hierarchical control scheme for trajectory tracking.
Both qubits of the singlet state can be steered simultaneously by multiple independent observers via sequential measurement
Quantum correlation is a fundamental property which distinguishes quantum systems from classical ones, and it is also a fragile resource under projective measurement.
Quantum Physics
Deep Residual Learning in Spiking Neural Networks
Previous Spiking ResNet mimics the standard residual block in ANNs and simply replaces ReLU activation layers with spiking neurons, which suffers the degradation problem and can hardly implement residual learning.
Policy Gradient for items Recommendation on Virtual Taobao
Recent years have witnessed digital content appear with plenty of forms (including online courses, online shopping and e-news) in daily life of people, which has provided with opportunities as well as challenges for systems to provide users with personalized services and information.
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs.
Neural Multi-Task Learning for Stance Prediction
We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction.
Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models
For an input text, it is simultaneously passed into BERT and the Tacotron-2 encoder.
FAKTA: An Automatic End-to-End Fact Checking System
We present FAKTA which is a unified framework that integrates various components of a fact checking process: document retrieval from media sources with various types of reliability, stance detection of documents with respect to given claims, evidence extraction, and linguistic analysis.
Dynamical systems applied to cosmology: dark energy and modified gravity
This review is a comprehensive and detailed study of dynamical systems applications to cosmological models focusing on the late-time behaviour of our Universe, and in particular on its accelerated expansion.
General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics High Energy Physics - Theory Dynamical Systems
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance.
Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions.
Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content
Multimedia or spoken content presents more attractive information than plain text content, but the former is more difficult to display on a screen and be selected by a user.
Random Drift Particle Swarm Optimization
The free electron model considers that electrons have both a thermal and a drift motion in a conductor that is placed in an external electric field.
