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Found 31 papers, 7 papers with code
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis
Then we propose a differential sentiment loss instead of the cross-entropy loss to better classify the sentiments by distinguishing the different distances between sentiments.
Dynamic Nonlinear Mixup with Distance-based Sample Selection
In this paper, we propose the dynamic nonlinear mixup with distance-based sample selection, which not only generates multiple sample pairs based on the distance between each sample but also enlarges the space of synthetic samples.
SSL-Cleanse: Trojan Detection and Mitigation in Self-Supervised Learning
By using a pre-trained SSL image encoder and training a downstream classifier on top of it, impressive performance can be achieved on various tasks with very little labeled data.
SePaint: Semantic Map Inpainting via Multinomial Diffusion
Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the current sensing range or resolution.
Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking
Pre-trained language models have achieved great success in various large-scale information retrieval tasks.
Multi-Feature Integration for Perception-Dependent Examination-Bias Estimation
Eliminating examination bias accurately is pivotal to apply click-through data to train an unbiased ranking model.
QTrojan: A Circuit Backdoor Against Quantum Neural Networks
We propose a circuit-level backdoor attack, \textit{QTrojan}, against Quantum Neural Networks (QNNs) in this paper.
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.
TrojViT: Trojan Insertion in Vision Transformers
The success of ViTs motivates adversaries to perform backdoor attacks on ViTs.
ATPL: Mutually enhanced adversarial training and pseudo labeling for unsupervised domain adaptation
Accordingly, ATPL will use the pseudo-labeled information to improve the adversarial training process, which can guarantee the feature transferability by generating adversarial data to fill in the domain gap.
Gradient Mask: Lateral Inhibition Mechanism Improves Performance in Artificial Neural Networks
Furthermore, we demonstrate analytically how lateral inhibition in artificial neural networks improves the quality of propagated gradients.
Self-Supervised Pretraining for Differentially Private Learning
When facing the lack of public datasets, we show the features generated by SSP on only one single image enable a private classifier to obtain much better utility than the non-learned handcrafted features under the same privacy budget.
Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory
To incorporate temporal information into the message passing scheme, we introduce a novel temporal-aware aggregator which assigns weights to neighbours according to an adaptive time exponential decay formula.
Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution Neural Network
In recent years, with the development of computing resources and LiDAR, point cloud semantic segmentation has attracted many researchers.
Transferring Knowledge Distillation for Multilingual Social Event Detection
Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.
HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture
Recently Homomorphic Encryption (HE) is used to implement Privacy-Preserving Neural Networks (PPNNs) that perform inferences directly on encrypted data without decryption.
SAFENet: A Secure, Accurate and Fast Neural Network Inference
A cryptographic neural network inference service is an efficient way to allow two parties to execute neural network inference without revealing either party’s data or model.
Falcon: Fast Spectral Inference on Encrypted Data
We observed that HENNs have to pay significant computing overhead on rotations, and each of rotations is $\sim 10\times$ more expensive than homomorphic multiplications between ciphertext and plaintext.
CryptoGRU: Low Latency Privacy-Preserving Text Analysis With GRU
Although prior secure networks combine homomorphic encryption (HE) and garbled circuit (GC) to preserve users' privacy, naively adopting the HE and GC hybrid technique to implement RNNs suffers from long inference latency due to slow activation functions.
Helix: Algorithm/Architecture Co-design for Accelerating Nanopore Genome Base-calling
From architecture perspective, we propose a low-power SOT-MRAM-based ADC array to process analog-to-digital conversion operations and improve power efficiency of prior DNN PIMs.
Kernel Learning for High-Resolution Time-Frequency Distribution
To break the current limitation, we propose a data-driven kernel learning model directly based on Wigner-Ville distribution (WVD).
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference
Prior HPPNNs over-pessimistically select huge HE parameters to maintain large noise budgets, since they use the same set of HE parameters for an entire network and ignore the error tolerance capability of a network.
Robust Time-Frequency Reconstruction by Learning Structured Sparsity
Time-frequency distributions (TFDs) play a vital role in providing descriptive analysis of non-stationary signals involved in realistic scenarios.
Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning
Experimental results on glomeruli segmentation from renal biopsy images indicate that our network is able to improve segmentation performance on target type of stained images and use unlabeled data to achieve similar accuracy to labeled data.
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Big data is one of the cornerstones to enabling and training deep neural networks (DNNs).
Improving Natural Language Understanding by Reverse Mapping Bytepair Encoding
We propose a method called reverse mapping bytepair encoding, which maps named-entity information and other word-level linguistic features back to subwords during the encoding procedure of bytepair encoding (BPE).
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data
Since the LTFHE ReLU activations, max poolings, shifts and accumulations have small multiplicative depth overhead, SHE can implement much deeper network architectures with more convolutional and activation layers.
AutoQ: Automated Kernel-Wise Neural Network Quantization
Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy, since the weight kernels in a layer exhibit different variances and hence have different amounts of redundancy.
Pose Invariant 3D Face Reconstruction
Our method solves the problem of face reconstruction of a single image of a traditional method in a large pose, works on arbitrary Pose and Expressions, greatly improves the accuracy of reconstruction.
DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework
To reduce the data storage and transfer overhead in smart resource-limited Internet-of-Thing (IoT) systems, effective data compression is a "must-have" feature before transferring real-time produced dataset for training or classification.
PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning
One of the most exciting advancements in AI over the last decade is the wide adoption of ANNs, such as DNN and CNN, in many real-world applications.
