Search Results for author:
Found 42 papers, 11 papers with code
A Copy-Augmented Generative Model for Open-Domain Question Answering
Open-domain question answering is a challenging task with a wide variety of practical applications.
Quasi-Newton OMP Approach for Super-Resolution Channel Estimation and Extrapolation
Channel estimation and extrapolation are fundamental issues in MIMO communication systems.
DMRA: An Adaptive Line Spectrum Estimation Method through Dynamical Multi-Resolution of Atoms
To reduce computational complexity and improve frequency estimation accuracy, a two-stage strategy was further introduced to dynamically adjust the number of the optimized degrees of freedom.
DePrompt: Desensitization and Evaluation of Personal Identifiable Information in Large Language Model Prompts
Prompt serves as a crucial link in interacting with large language models (LLMs), widely impacting the accuracy and interpretability of model outputs.
Leveraging Adaptive Implicit Representation Mapping for Ultra High-Resolution Image Segmentation
To address these limitations, we propose a novel approach that leverages the newly proposed Adaptive Implicit Representation Mapping (AIRM) for ultra-high-resolution Image Segmentation.
CLII: Visual-Text Inpainting via Cross-Modal Predictive Interaction
In this work, we identify the visual-text inpainting task to achieve high-quality scene text image restoration and text completion: Given a scene text image with unknown missing regions and the corresponding text with unknown missing characters, we aim to complete the missing information in both images and text by leveraging their complementary information.
EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems
To mitigate these issues, we propose our enhanced web and efficient knowledge graph (KG) retrieval solution (EWEK-QA) to enrich the content of the extracted knowledge fed to the system.
Evaluating the External and Parametric Knowledge Fusion of Large Language Models
Integrating external knowledge into large language models (LLMs) presents a promising solution to overcome the limitations imposed by their antiquated and static parametric memory.
Retrieval-based Full-length Wikipedia Generation for Emergent Events
In today's fast-paced world, the growing demand to quickly generate comprehensive and accurate Wikipedia documents for emerging events is both crucial and challenging.
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions
To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses.
Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer
The present study introduces the knowledge-augmented generator, which is specifically designed to produce information that remains grounded in contextual knowledge, regardless of alterations in the context.
PROXYQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models
Large Language Models (LLMs) have succeeded remarkably in understanding long-form contents.
"Knowing When You Don't Know": A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation
NoMIRACL includes both a non-relevant and a relevant subset.
SAIR: Learning Semantic-aware Implicit Representation
To this end, we propose a framework with two modules: (1) building a semantic implicit representation (SIR) for a corrupted image whose large regions miss.
CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
Using the aggregated similarity scores as the initial pseudo labels at the training stage, we propose an optimization framework to train the parameters of the classification network and refine pseudo labels for unobserved labels.
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting
Given the coordinates of a pixel we want to reconstruct, we first collect its neighboring pixels in the input image and extract their detail-enhanced semantic embeddings, unmask-attentional semantic embeddings, importance values, and spatial distances to the desired pixel.
CNN-based Dendrite Core Detection from Microscopic Images of Directionally Solidified Ni-base Alloys
To make HSD only focus on the feature of hard samples of dendrite cores, we destroy the structure of the easy samples of dendrites which are detected by ESD and force HSD to learn the feature of hard samples.
Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal
State-of-the-art shadow removal methods train deep neural networks on collected shadow & shadow-free image pairs, which are desired to complete two distinct tasks via shared weights, i. e., data restoration for shadow regions and identical mapping for non-shadow regions.
Orthogonal Dictionary Guided Shape Completion Network for Point Cloud
Point cloud shape completion, which aims to reconstruct the missing regions of the incomplete point clouds with plausible shapes, is an ill-posed and challenging task that benefits many downstream 3D applications.
Ranked #1 on
Point Cloud Completion
on ShapeNet
Leveraging Inpainting for Single-Image Shadow Removal
In this work, we find that pretraining shadow removal networks on the image inpainting dataset can reduce the shadow remnants significantly: a naive encoder-decoder network gets competitive restoration quality w. r. t.
Retrieval-based Disentangled Representation Learning with Natural Language Supervision
In light of this, we present Vocabulary Disentangled Retrieval (VDR), a retrieval-based framework that harnesses natural language as proxies of the underlying data variation to drive disentangled representation learning.
Pre-training Language Models with Deterministic Factual Knowledge
Further experiments on question-answering datasets show that trying to learn a deterministic relationship with the proposed methods can also help other knowledge-intensive tasks.
Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages
MIRACL (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual dataset we have built for the WSDM 2023 Cup challenge that focuses on ad hoc retrieval across 18 different languages, which collectively encompass over three billion native speakers around the world.
How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis
We check the words that have three typical associations with the missing words: knowledge-dependent, positionally close, and highly co-occurred.
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering
To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR).
MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting
As a result, the final method takes the advantage of effective semantic & image-level filling for high-fidelity inpainting.
Read before Generate! Faithful Long Form Question Answering with Machine Reading
Long-form question answering (LFQA) aims to generate a paragraph-length answer for a given question.
Ranked #1 on
Question Answering
on KILT: ELI5
Unsupervised Open-Domain Question Answering
Open-domain Question Answering (ODQA) has achieved significant results in terms of supervised learning manner.
Machine Reading Comprehension
Open-Domain Question Answering
JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting
In this paper, for the first time, we formulate image inpainting as a mix of two problems, predictive filtering and deep generation.
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation
It is still an open question to leverage various types of information under the BERT framework.
HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions
In this paper, we propose a new retrieval target, hop, to collect the hidden reasoning evidence from Wikipedia for complex question answering.
Ranked #6 on
Question Answering
on HotpotQA
Monitoring-based Differential Privacy Mechanism Against Query-Flooding Parameter Duplication Attack
In MDP, we first propose a novel real-time model extraction status assessment scheme called Monitor to evaluate the situation of the model.
SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval
Term-based sparse representations dominate the first-stage text retrieval in industrial applications, due to its advantage in efficiency, interpretability, and exact term matching.
Blur-Attention: A boosting mechanism for non-uniform blurred image restoration
Our method can adaptively select the weights of the extracted features according to the spatially varying blur features, and dynamically restore the images.
A lateral semicircular canal segmentation based geometric calibration for human temporal bone CT Image
Computed Tomography (CT) of the temporal bone has become an important method for diagnosing ear diseases.
Dynamics of ferromagnetic bimerons driven by spin currents and magnetic fields
Numerical simulations demonstrate that two bimerons with opposite signs of topological numbers can be created simultaneously in a ferromagnetic thin film via current-induced spin torques.
Mesoscale and Nanoscale Physics
Mitigating Query-Flooding Parameter Duplication Attack on Regression Models with High-Dimensional Gaussian Mechanism
Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries.
DUMA: Reading Comprehension with Transposition Thinking
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question.
Ranked #3 on
Reading Comprehension
on RACE
Dual Multi-head Co-attention for Multi-choice Reading Comprehension
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question.
A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems
In this paper, we propose a phase shift deep neural network (PhaseDNN), which provides a uniform wideband convergence in approximating high frequency functions and solutions of wave equations.
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
PhaseDNN - A Parallel Phase Shift Deep Neural Network for Adaptive Wideband Learning
Due to the phase shift, each DNN achieves the speed of convergence as in the low frequency range.
