Search Results for author:
Found 76 papers, 30 papers with code
Concept -- An Evaluation Protocol on Conversation Recommender Systems with System-centric and User-centric Factors
The conversational recommendation system (CRS) has been criticized regarding its user experience in real-world scenarios, despite recent significant progress achieved in academia.
Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models
We conduct GenAP on three popular code intelligence PLMs with three canonical code intelligence tasks including defect prediction, code summarization, and code translation.
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model
A natural expectation for PEFTs is that the performance of various PEFTs is positively related to the data size and fine-tunable parameter size.
Zero-Shot Aerial Object Detection with Visual Description Regularization
Instead of directly encoding the descriptions into class embedding space which suffers from the representation gap problem, we propose to infuse the prior inter-class visual similarity conveyed in the descriptions into the embedding learning.
Diffusion Posterior Proximal Sampling for Image Restoration
Specifically, we opt for a sample consistent with the measurement identity at each generative step, exploiting the sampling selection as an avenue for output stability and enhancement.
Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem
To this end, various methods have been proposed to create an adaptive filter by incorporating an extra filter (e. g., a high-pass filter) extracted from the graph topology.
DREditor: An Time-efficient Approach for Building a Domain-specific Dense Retrieval Model
Motivated by this, we develop a time-efficient approach called DREditor to edit the matching rule of an off-the-shelf dense retrieval model to suit a specific domain.
See the Unseen: Better Context-Consistent Knowledge-Editing by Noises
In this paper, we empirically find that the effects of different contexts upon LLMs in recalling the same knowledge follow a Gaussian-like distribution.
Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers
Our analysis show that constant approximations to the 1-center and 1-mean clustering problems with outliers provide near-optimal resilient aggregators for metric-based criteria, which have been proven to be crucial in the homogeneous and heterogeneous cases respectively.
Towards Equipping Transformer with the Ability of Systematic Compositionality
One of the key factors in language productivity and human cognition is the ability of systematic compositionality, which refers to understanding composed unseen examples of seen primitives.
Noisy Pair Corrector for Dense Retrieval
Most dense retrieval models contain an implicit assumption: the training query-document pairs are exactly matched.
Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation
The advent of Industry 4. 0 has precipitated the incorporation of Artificial Intelligence (AI) methods within industrial contexts, aiming to realize intelligent manufacturing, operation as well as maintenance, also known as industrial intelligence.
Image Clustering with External Guidance
The core of clustering is incorporating prior knowledge to construct supervision signals.
VCL Challenges 2023 at ICCV 2023 Technical Report: Bi-level Adaptation Method for Test-time Adaptive Object Detection
This report outlines our team's participation in VCL Challenges B Continual Test_time Adaptation, focusing on the technical details of our approach.
Attribute Localization and Revision Network for Zero-Shot Learning
Second, we propose Attribute Revision Module (ARM), which generates image-level semantics by revising the ground-truth value of each attribute, compensating for performance degradation caused by ignoring intra-class variation.
Federated cINN Clustering for Accurate Clustered Federated Learning
However, a significant challenge arises when coordinating FL with crowd intelligence which diverse client groups possess disparate objectives due to data heterogeneity or distinct tasks.
Fast and Stable Diffusion Inverse Solver with History Gradient Update
To address this issue, we propose to utilize the history information of the diffusion-based inverse solvers.
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals
The machine reading comprehension (MRC) of user manuals has huge potential in customer service.
GPT-NAS: Evolutionary Neural Architecture Search with the Generative Pre-Trained Model
Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically.
Deep Lifelong Cross-modal Hashing
To this end, we, in this paper, propose a novel deep lifelong cross-modal hashing to achieve lifelong hashing retrieval instead of re-training hash function repeatedly when new data arrive.
Differentiable Genetic Programming for High-dimensional Symbolic Regression
With these designs, the proposed DGP method can efficiently search for the GP trees with higher performance, thus being capable of dealing with high-dimensional SR. To demonstrate the effectiveness of DGP, we conducted various experiments against the state of the arts based on both GP and deep neural networks.
Dual Contrastive Prediction for Incomplete Multi-view Representation Learning
In this article, we propose a unified framework to solve the following two challenging problems in incomplete multi-view representation learning: i) how to learn a consistent representation unifying different views, and ii) how to recover the missing views.
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning.
Comprehensive and Delicate: An Efficient Transformer for Image Restoration
Vision Transformers have shown promising performance in image restoration, which usually conduct window- or channel-based attention to avoid intensive computations.
Sample-Level Multi-View Graph Clustering
Multi-view clustering have hitherto been studied due to their effectiveness in dealing with heterogeneous data.
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective
Existing studies have empirically observed that the resolution of the low-frequency region is easier to enhance than that of the high-frequency one.
Differentiable Search of Accurate and Robust Architectures
Deep neural networks (DNNs) are found to be vulnerable to adversarial attacks, and various methods have been proposed for the defense.
Personalized Federated Learning with Hidden Information on Personalized Prior
Federated learning (FL for simplification) is a distributed machine learning technique that utilizes global servers and collaborative clients to achieve privacy-preserving global model training without direct data sharing.
Ranked #1 on
Image Classification
on Fashion-MNIST
(Accuracy metric)
Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognition
Incorporating large-scale pre-trained models with the prototypical neural networks is a de-facto paradigm in few-shot named entity recognition.
Partial Differential Equations Meet Deep Neural Networks: A Survey
Many problems in science and engineering can be represented by a set of partial differential equations (PDEs) through mathematical modeling.
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (\textit{e. g.}, gender, race, RNA sequencing technique) from dominating the clustering.
Ranked #1 on
Image Clustering
on HAR
Draft, Command, and Edit: Controllable Text Editing in E-Commerce
To address this challenge, we explore a new draft-command-edit manner in description generation, leading to the proposed new task-controllable text editing in E-commerce.
Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
To this end, we contribute to advance the study of the proactive dialogue policy to a more natural and challenging setting, i. e., interacting dynamically with users.
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg
Federated learning (FL) is identified as a crucial enabler for large-scale distributed machine learning (ML) without the need for local raw dataset sharing, substantially reducing privacy concerns and alleviating the isolated data problem.
Multi-Scale Adaptive Network for Single Image Denoising
AFuB devotes to adaptively sampling and transferring the features from one scale to another scale, which fuses the multi-scale features with varying characteristics from coarse to fine.
Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen classes by training only the seen classes, in which the instances of unseen classes tend to be biased towards the seen class.
All-in-One Image Restoration for Unknown Corruption
In this paper, we study a challenging problem in image restoration, namely, how to develop an all-in-one method that could recover images from a variety of unknown corruption types and levels.
Learning With Twin Noisy Labels for Visible-Infrared Person Re-Identification
To solve the TNL problem, we propose a novel method for robust VI-ReID, termed DuAlly Robust Training (DART).
Face Deblurring Based on Separable Normalization and Adaptive Denormalization
Face deblurring aims to restore a clear face image from a blurred input image with more explicit structure and facial details.
Adversarial Retriever-Ranker for dense text retrieval
To address these challenges, we present Adversarial Retriever-Ranker (AR2), which consists of a dual-encoder retriever plus a cross-encoder ranker.
POS-Constrained Parallel Decoding for Non-autoregressive Generation
However, in this work, we experimentally reveal that this assumption does not always hold for the text generation tasks like text summarization and story ending generation.
Unsupervised Neural Rendering for Image Hazing
To generate plausible haze, we study two less-touched but challenging problems in hazy image rendering, namely, i) how to estimate the transmission map from a single image without auxiliary information, and ii) how to adaptively learn the airlight from exemplars, i. e., unpaired real hazy images.
TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions.
Ranked #1 on
Question Answering
on TAT-QA
Poolingformer: Long Document Modeling with Pooling Attention
We first evaluate Poolingformer on two long sequence QA tasks: the monolingual NQ and the multilingual TyDi QA.
Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search
In this paper, we propose a novel approach, Heart-Darts, to efficiently classify the ECG signals by automatically designing the CNN model with the differentiable architecture search (i. e., Darts, a cell-based neural architecture search method).
Prediction, Selection, and Generation: Exploration of Knowledge-Driven Conversation System
The system includes modules such as dialogue topic prediction, knowledge matching and dialogue generation.
LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences
With pivot module reconstructed the decoder for individual students and leveled learning specialized encoders for groups, personalized DKT was achieved.
COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction
In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data.
Ranked #1 on
Incomplete multi-view clustering
on n-MNIST
Communication-Efficient Federated Learning with Compensated Overlap-FedAvg
Petabytes of data are generated each day by emerging Internet of Things (IoT), but only few of them can be finally collected and used for Machine Learning (ML) purposes due to the apprehension of data & privacy leakage, which seriously retarding ML's growth.
Partially View-aligned Clustering
To solve this practical and challenging problem, we propose a novel multi-view clustering method termed partially view-aligned clustering (PVC).
GLGE: A New General Language Generation Evaluation Benchmark
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP).
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks.
AnchiBERT: A Pre-Trained Model for Ancient ChineseLanguage Understanding and Generation
Previous studies usually use the supervised models which deeply rely on parallel data.
Cultural Vocal Bursts Intensity Prediction
Language Modelling
+1
HPSGD: Hierarchical Parallel SGD With Stale Gradients Featuring
While distributed training significantly speeds up the training process of the deep neural network (DNN), the utilization of the cluster is relatively low due to the time-consuming data synchronizing between workers.
PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks
Synchronous methods are widely used in distributed training the Deep Neural Networks (DNNs).
DBS: Dynamic Batch Size For Distributed Deep Neural Network Training
Specifically, the performance of each worker is evaluatedfirst based on the fact in the previous epoch, and then the batch size and datasetpartition are dynamically adjusted in consideration of the current performanceof the worker, thereby improving the utilization of the cluster.
RikiNet: Reading Wikipedia Pages for Natural Question Answering
The representations are then fed into the predictor to obtain the span of the short answer, the paragraph of the long answer, and the answer type in a cascaded manner.
Let's be Humorous: Knowledge Enhanced Humor Generation
To our knowledge, this is the first attempt to generate punchlines with knowledge enhanced model.
Learning Event-Based Motion Deblurring
Recovering sharp video sequence from a motion-blurred image is highly ill-posed due to the significant loss of motion information in the blurring process.
Ranked #27 on
Image Deblurring
on GoPro
(using extra training data)
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation
Furthermore, we propose a simple and effective method to mine the keyphrases of interest in the news article and build a first large-scale keyphrase-aware news headline corpus, which contains over 180K aligned triples of $<$news article, headline, keyphrase$>$.
Generating Chinese Poetry from Images via Concrete and Abstract Information
We proposed an infilling-based Chinese poetry generation model which can infill the Concrete keywords into each line of poems in an explicit way, and an abstract information embedding to integrate the Abstract information into generated poems.
ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures
Data mining on existing CNN can discover useful patterns and fundamental sub-comments from their architectures, providing researchers with strong prior knowledge to design proper CNN architectures when they have no expertise in CNNs.
Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting
We firstly represent only face regions using the latent variable as the domain knowledge and combine it with the non-face parts textures to generate high-quality face images with plausible contents.
Deep Poetry: A Chinese Classical Poetry Generation System
For the user's convenience, we deploy the system at the WeChat applet platform, users can use the system on the mobile device whenever and wherever possible.
Deep Density-aware Count Regressor
We seek to improve crowd counting as we perceive limits of currently prevalent density map estimation approach on both prediction accuracy and time efficiency.
Deep Learning-Based Automatic Downbeat Tracking: A Brief Review
Thereinto, downbeat tracking has been a fundamental and continuous problem in Music Information Retrieval (MIR) area.
Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning
We propose a new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer.
TIGS: An Inference Algorithm for Text Infilling with Gradient Search
Text infilling is defined as a task for filling in the missing part of a sentence or paragraph, which is suitable for many real-world natural language generation scenarios.
mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation
It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problem.
Learning Discriminative Representation with Signed Laplacian Restricted Boltzmann Machine
We investigate the potential of a restricted Boltzmann Machine (RBM) for discriminative representation learning.
XAI Beyond Classification: Interpretable Neural Clustering
The second is implementing discrete $k$-means with a differentiable neural network that embraces the advantages of parallel computing, online clustering, and clustering-favorable representation learning.
Ancient-Modern Chinese Translation with a Large Training Dataset
Ancient Chinese brings the wisdom and spirit culture of the Chinese nation.
Cultural Vocal Bursts Intensity Prediction
Machine Translation
+2
A Multi-Modal Chinese Poetry Generation Model
Given a picture, the first line, the title and the other lines of the poem are successively generated in three stages.
BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation
Incorporating prior knowledge like lexical constraints into the model's output to generate meaningful and coherent sentences has many applications in dialogue system, machine translation, image captioning, etc.
Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets
The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value. Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning. While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance. Due to these reasons, we propose a new approach based on using inverse generator ($IG$) model as encoder and pre-trained generator ($G$) as decoder of an AutoEncoder network to train the $IG$ model.
