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Found 19 papers, 2 papers with code
Near-Data Processing for Differentiable Machine Learning Models
The second is the popularity of NAND flash-based solid-state drives (SSDs) containing multicore processors that can accommodate extra computation for data processing.
An Efficient Approach to Boosting Performance of Deep Spiking Network Training
In order to eliminate this workaround, recently proposed is a new class of SNN named deep spiking networks (DSNs), which can be trained directly (without a mapping from conventional deep networks) by error backpropagation with stochastic gradient descent.
Quantized Memory-Augmented Neural Networks
In this paper, we identify memory addressing (specifically, content-based addressing) as the main reason for the performance degradation and propose a robust quantization method for MANNs to address the challenge.
Energy-Efficient Inference Accelerator for Memory-Augmented Neural Networks on an FPGA
Memory-augmented neural networks (MANNs) are designed for question-answering tasks.
Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks
The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability.
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications.
T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their applicability in practical machine learning problems.
Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence
The proposed model finds n-to-1 subject-object relations using a forward object decoder.
Ranked #1 on
Relation Extraction
on ACE 2005
(Relation classification F1 metric)
Noise-Robust Deep Spiking Neural Networks with Temporal Information
Spiking neural networks (SNNs) have emerged as energy-efficient neural networks with temporal information.
Training Energy-Efficient Deep Spiking Neural Networks with Time-to-First-Spike Coding
With TTFS coding, each neuron generates one spike at most, which leads to a significant improvement in energy efficiency.
Energy-efficient Knowledge Distillation for Spiking Neural Networks
On MNIST dataset, our proposed student SNN achieves up to 0. 09% higher accuracy and produces 65% less spikes compared to the student SNN trained with conventional knowledge distillation method.
Improving Sentence-Level Relation Extraction through Curriculum Learning
Sentence-level relation extraction mainly aims to classify the relation between two entities in a sentence.
Ranked #1 on
Relation Extraction
on Re-TACRED
Scalable Smartphone Cluster for Deep Learning
We implemented large-batch synchronous training of DNNs based on Caffe, a deep learning library.
AutoSNN: Towards Energy-Efficient Spiking Neural Networks
We investigate the design choices used in the previous studies in terms of the accuracy and number of spikes and figure out that they are not best-suited for SNNs.
E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations
In terms of image quality, the LPIPS score improves by up to 12% and the reconstruction speed is 87% higher than that of ET-Net.
SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild
One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambiguity of facial images.
Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information
Deep spiking neural networks (SNNs) are promising neural networks for their model capacity from deep neural network architecture and energy efficiency from SNNs' operations.
M2Former: Multi-Scale Patch Selection for Fine-Grained Visual Recognition
Therefore, we propose multi-scale patch selection (MSPS) to improve the multi-scale capabilities of existing ViT-based models.
Pipeline Coreference Resolution Model for Anaphoric Identity in Dialogues
Therefore, we proceed with the anaphora resolution as a two-stage pipeline model.
