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Found 29 papers, 4 papers with code
Methods for Numeracy-Preserving Word Embeddings
We propose a new methodology to assign and learn embeddings for numbers.
Evaluating the Performance of ChatGPT for Spam Email Detection
To fill in the gap, this study attempts to evaluate ChatGPT's capabilities for spam identification in both English and Chinese email datasets.
Exploring the Capabilities of ChatGPT in Ancient Chinese Translation and Person Name Recognition
ChatGPT's proficiency in handling modern standard languages suggests potential for its use in understanding ancient Chinese.
Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification
Label smoothing is a widely used technique in various domains, such as text classification, image classification and speech recognition, known for effectively combating model overfitting.
On the Calibration and Uncertainty with Pólya-Gamma Augmentation for Dialog Retrieval Models
Deep neural retrieval models have amply demonstrated their power but estimating the reliability of their predictions remains challenging.
Efficient Document Retrieval by End-to-End Refining and Quantizing BERT Embedding with Contrastive Product Quantization
Efficient document retrieval heavily relies on the technique of semantic hashing, which learns a binary code for every document and employs Hamming distance to evaluate document distances.
Pushing the Efficiency Limit Using Structured Sparse Convolutions
Weight pruning is among the most popular approaches for compressing deep convolutional neural networks.
Pose Guided Human Image Synthesis with Partially Decoupled GAN
Most existing methods encode the texture of the whole reference human image into a latent space, and then utilize a decoder to synthesize the image texture of the target pose.
Machine Unlearning Method Based On Projection Residual
Even when data is removed from the dataset, the effects of these data persist in the model.
RL-MD: A Novel Reinforcement Learning Approach for DNA Motif Discovery
The extraction of sequence patterns from a collection of functionally linked unlabeled DNA sequences is known as DNA motif discovery, and it is a key task in computational biology.
Boosting Star-GANs for Voice Conversion with Contrastive Discriminator
Nonparallel multi-domain voice conversion methods such as the StarGAN-VCs have been widely applied in many scenarios.
Debias the Black-box: A Fair Ranking Framework via Knowledge Distillation
Deep neural networks can capture the intricate interaction history information between queries and documents, because of their many complicated nonlinear units, allowing them to provide correct search recommendations.
Uncertainty Calibration for Deep Audio Classifiers
In this work, we investigate the uncertainty calibration for deep audio classifiers.
Leveraging Causal Inference for Explainable Automatic Program Repair
The experiments on four programming languages (Java, C, Python, and JavaScript) show that CPR can generate causal graphs for reasonable interpretations and boost the performance of bug fixing in automatic program repair.
Cali3F: Calibrated Fast Fair Federated Recommendation System
Specific to recommendation systems, many federated recommendation algorithms have been proposed to realize the privacy-preserving collaborative recommendation.
A Fair Federated Learning Framework With Reinforcement Learning
More importantly, we apply the Gini coefficient and validation accuracy of clients in each communication round to construct a reward function for the reinforcement learning.
Federated Split BERT for Heterogeneous Text Classification
To address this issue, we propose a framework, FedSplitBERT, which handles heterogeneous data and decreases the communication cost by splitting the BERT encoder layers into local part and global part.
Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information
Non-negative matrix factorization (NMF) based topic modeling is widely used in natural language processing (NLP) to uncover hidden topics of short text documents.
Augmentation-induced Consistency Regularization for Classification
In this paper, we propose a consistency regularization framework based on data augmentation, called CR-Aug, which forces the output distributions of different sub models generated by data augmentation to be consistent with each other.
Improving Human Image Synthesis with Residual Fast Fourier Transformation and Wasserstein Distance
And the second is that the training of GAN is unstable and slow to converge, such as model collapse.
Towards Speaker Age Estimation with Label Distribution Learning
To address this, we utilize the ambiguous information among the age labels, convert each age label into a discrete label distribution and leverage the label distribution learning (LDL) method to fit the data.
VU-BERT: A Unified framework for Visual Dialog
The visual dialog task attempts to train an agent to answer multi-turn questions given an image, which requires the deep understanding of interactions between the image and dialog history.
Speech2Video: Cross-Modal Distillation for Speech to Video Generation
This paper investigates a novel task of talking face video generation solely from speeches.
FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders
Pretrained text encoders, such as BERT, have been applied increasingly in various natural language processing (NLP) tasks, and have recently demonstrated significant performance gains.
Reinforcement Learning for Flexibility Design Problems
Flexibility design problems are a class of problems that appear in strategic decision-making across industries, where the objective is to design a ($e. g.$, manufacturing) network that affords flexibility and adaptivity.
Integrating Task Specific Information into Pretrained Language Models for Low Resource Fine Tuning
Pretrained Language Models (PLMs) have improved the performance of natural language understanding in recent years.
Students Need More Attention: BERT-based AttentionModel for Small Data with Application to AutomaticPatient Message Triage
Small and imbalanced datasets commonly seen in healthcare represent a challenge when training classifiers based on deep learning models.
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization
Control variates are a well-established tool to reduce the variance of Monte Carlo estimators.
Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding
Attention-based models have shown significant improvement over traditional algorithms in several NLP tasks.
