Search Results for author:
Found 28 papers, 8 papers with code
Deep Spiking Neural Network: Energy Efficiency Through Time based Coding
The real-valued ReLU activations in ANN are encoded using the spike-times of the TSC neurons in the converted TSC-SNN.
Brain-inspired and Self-based Artificial Intelligence
In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm.
Integrating Large Language Models with Graphical Session-Based Recommendation
SBR has been primarily dominated by Graph Neural Networks, which have achieved many successful outcomes due to their ability to capture both the implicit and explicit relationships between adjacent behaviors.
Natural Language Understanding
Session-Based Recommendations
+2
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems.
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
To address these challenges under open-world assumptions, we design an \textbf{A}daptive \textbf{M}ulti-\textbf{I}nterest \textbf{D}ebiasing framework for cross-domain sequential recommendation (\textbf{AMID}), which consists of a multi-interest information module (\textbf{MIM}) and a doubly robust estimator (\textbf{DRE}).
Market Crowds' Trading Behaviors, Agreement Prices, and the Implications of Trading Volume
It has been long that literature in financial academics focuses mainly on price and return but much less on trading volume.
Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition
Current speaker recognition systems primarily rely on supervised approaches, constrained by the scale of labeled datasets.
Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks
In this paper, we propose a brain-inspired continual learning algorithm with adaptive reorganization of neural pathways, which employs Self-Organizing Regulation networks to reorganize the single and limited Spiking Neural Network (SOR-SNN) into rich sparse neural pathways to efficiently cope with incremental tasks.
InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models
We tested InstructME in instrument-editing, remixing, and multi-round editing.
Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks
In addition, the overlapping shared structure helps to quickly leverage all acquired knowledge to new tasks, empowering a single network capable of supporting multiple incremental tasks (without the separate sub-network mask for each task).
Exploring Binary Classification Loss For Speaker Verification
The mismatch between close-set training and open-set testing usually leads to significant performance degradation for speaker verification task.
Wespeaker baselines for VoxSRC2023
This report showcases the results achieved using the wespeaker toolkit for the VoxSRC2023 Challenge.
Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks
For structural evolution, an adaptive evolvable LSM model is developed to optimize the neural architecture design of liquid layer with separation property.
Automated Task-Time Interventions to Improve Teamwork using Imitation Learning
To improve teamwork in these and other domains, we present TIC: an automated intervention approach for improving coordination between team members.
Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks
The proposed DPAP model considers multiple biologically realistic mechanisms (such as dendritic spine dynamic plasticity, activity-dependent neural spiking trace, and local synaptic plasticity), with the addition of an adaptive pruning strategy, so that the network structure can be dynamically optimized during learning without any pre-training and retraining.
Adaptive Sparse Structure Development with Pruning and Regeneration for Spiking Neural Networks
Experimental results on spatial (MNIST, CIFAR-10) and temporal neuromorphic (N-MNIST, DVS-Gesture) datasets demonstrate that our method can flexibly learn appropriate compression rate for various tasks and effectively achieve superior performance while massively reducing the network energy consumption.
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems
HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users' current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs.
Cross-Architecture Self-supervised Video Representation Learning
In this paper, we present a new cross-architecture contrastive learning (CACL) framework for self-supervised video representation learning.
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation
In this paper, we propose a Poincar\'{e}-based heterogeneous graph neural network named PHGR to model the sequential pattern information as well as hierarchical information contained in the data of SR scenarios simultaneously.
AdaMixer: A Fast-Converging Query-Based Object Detector
The recent query-based object detectors break this convention by decoding image features with a set of learnable queries.
Semi-Supervised Clustering with Contrastive Learning for Discovering New Intents
To make DCSC fully utilize the limited known intents, we propose a two-stage training procedure for DCSC, in which DCSC will be trained on both labeled samples and unlabeled samples, and achieve better text representation and clustering performance.
Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing
To address these challenges, we propose a novel neuron model that has cosine activation with a time varying component for sequential processing.
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation
Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors.
Underwater Target Recognition based on Multi-Decision LOFAR Spectrum Enhancement: A Deep Learning Approach
To overcome this issue and to further improve the recognition performance, we adopt a deep learning approach for underwater target recognition and propose a LOFAR spectrum enhancement (LSE)-based underwater target recognition scheme, which consists of preprocessing, offline training, and online testing.
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network
We find that performance degradation in the converted SNN stems from using "hard reset" spiking neuron that is driven to fixed reset potential once its membrane potential exceeds the firing threshold, leading to information loss during SNN inference.
iQIYI-VID: A Large Dataset for Multi-modal Person Identification
Multi-modal person identification is a more promising way that we can jointly utilize face, head, body, audio features, and so on.
Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays
In this paper, we demonstrate how deep binary networks can be accelerated in modified von-Neumann machines by enabling binary convolutions within the SRAM array.
Emerging Technologies
