Search Results for author:
Found 92 papers, 29 papers with code
Sociolectal Analysis of Pretrained Language Models
Using data from English cloze tests, in which subjects also self-reported their gender, age, education, and race, we examine performance differences of pretrained language models across demographic groups, defined by these (protected) attributes.
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks.
Machine learning force-field models for metallic spin glass
Metallic spin glass systems, such as dilute magnetic alloys, are characterized by randomly distributed local moments coupled to each other through a long-range electron-mediated effective interaction.
Code Search Debiasing:Improve Search Results beyond Overall Ranking Performance
To mitigate biases, we develop a general debiasing framework that employs reranking to calibrate search results.
Enhancing Medical Text Evaluation with GPT-4
In the evaluation of medical text generation, it is essential to scrutinize each piece of information and ensure the utmost accuracy of the evaluation.
Dynamic Multimodal Information Bottleneck for Multimodality Classification
Specifically, our information bottleneck module serves to filter out the task-irrelevant information and noises in the fused feature, and we further introduce a sufficiency loss to prevent dropping of task-relevant information, thus explicitly preserving the sufficiency of prediction information in the distilled feature.
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys
In a comprehensive battery of tests on counterfactual medical image generation, BiomedJourney substantially outperforms prior state-of-the-art methods in instruction image editing and medical image generation such as InstructPix2Pix and RoentGen.
Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theory
To address the challenges posed by the heterogeneity inherent in federated learning (FL) and to attract high-quality clients, various incentive mechanisms have been employed.
Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment
To address this challenge, we propose the Self Structural Semantic Alignment (S^3A) framework, which extracts the structural semantic information from unlabeled data while simultaneously self-learning.
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition
Instruction tuning has proven effective for distilling LLMs into more cost-efficient models such as Alpaca and Vicuna.
Ranked #1 on
Named Entity Recognition
on ACE 2005
Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology
Clinical trial matching is a key process in health delivery and discovery.
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events
We find that while LLMs already possess decent competency in structuring biomedical text, by distillation into a task-specific student model through self-supervised learning, substantial gains can be attained over out-of-box LLMs, with additional advantages such as cost, efficiency, and white-box model access.
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge
The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge.
Benchmarking Diverse-Modal Entity Linking with Generative Models
Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables.
UNITE: A Unified Benchmark for Text-to-SQL Evaluation
A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures.
Semi-Asynchronous Federated Edge Learning Mechanism via Over-the-air Computation
However, existing FEEL systems with AirComp scheme often employ traditional synchronous aggregation mechanisms for local model aggregation in each global round, which suffer from the stragglers issues.
Machine learning for structure-property relationships: Scalability and limitations
Based on the locality assumption, ML model is developed for the prediction of intensive properties of a finite-size block.
Regularized Shallow Image Prior for Electrical Impedance Tomography
The results suggest that combining the shallow image prior and the hand-crafted regularization can achieve similar performance to the Deep Image Prior (DIP) but with less architectural dependency and complexity of the neural network.
Compositional Zero-Shot Domain Transfer with Text-to-Text Models
We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on NLI, text summarisation and embedding learning.
Context-faithful Prompting for Large Language Models
However, their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks (e. g., knowledge acquisition tasks).
Machine learning for phase ordering dynamics of charge density waves
We present a machine learning (ML) framework for large-scale dynamical simulations of charge density wave (CDW) states.
Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing
Our dataset (PMC-15M) is two orders of magnitude larger than existing biomedical image-text datasets such as MIMIC-CXR, and spans a diverse range of biomedical images.
Ranked #2 on
Medical Visual Question Answering
on SLAKE-English
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries.
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction
In this paper, we aim at bridging the gap and propose to pretrain and finetune the RE model using consistent objectives of contrastive learning.
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing
In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data.
PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery
The GNCD setting aims to categorize unlabeled training data coming from known and novel classes by leveraging the information of partially labeled known classes.
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain.
Ranked #1 on
Question Answering
on PubMedQA
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases
Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs.
Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning
We present a bi-encoder framework for named entity recognition (NER), which applies contrastive learning to map candidate text spans and entity types into the same vector representation space.
Ranked #1 on
Named Entity Recognition (NER)
on BC5CDR
Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles
Leg-UP learns user behavior patterns from real users in the sampled ``templates'' and constructs fake user profiles.
REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction
Our generative model is a unified framework to sequentially generate relational triplets under various relation extraction settings and explicitly utilizes relevant knowledge from Knowledge Graph (KG) to resolve ambiguities.
2-d signature of images and texture classification
We introduce a proper notion of 2-dimensional signature for images.
Locally Aggregated Feature Attribution on Natural Language Model Understanding
Some feature attribution methods have shown promising results in computer vision, especially the gradient-based methods where effectively smoothing the gradients with reference data is key to a robust and faithful result.
Machine learning predictions for local electronic properties of disordered correlated electron systems
We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron systems.
Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning
Large multilingual pretrained language models such as mBERT and XLM-RoBERTa have been found to be surprisingly effective for cross-lingual transfer of syntactic parsing models (Wu and Dredze 2019), but only between related languages.
FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks.
Rule Mining over Knowledge Graphs via Reinforcement Learning
Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community.
Descriptors for Machine Learning Model of Generalized Force Field in Condensed Matter Systems
A general theory of the descriptor for the classical fields is formulated, and two types of models are distinguished depending on the presence or absence of an internal symmetry for the classical field.
Knowledge-Rich Self-Supervision for Biomedical Entity Linking
Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia.
DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification
Specifically, we directly constrain the output from the convolutional neural network to be discrete binary codes and ensure the learned binary codes are optimal for classification.
Finite Sample Analysis of Average-Reward TD Learning and $Q$-Learning
The focus of this paper is on sample complexity guarantees of average-reward reinforcement learning algorithms, which are known to be more challenging to study than their discounted-reward counterparts.
A Review on Graph Neural Network Methods in Financial Applications
With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations.
Can depth-adaptive BERT perform better on binary classification tasks
In light of the success of transferring language models into NLP tasks, we ask whether the full BERT model is always the best and does it exist a simple but effective method to find the winning ticket in state-of-the-art deep neural networks without complex calculations.
Modular Self-Supervision for Document-Level Relation Extraction
Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining high recall of the latest findings is crucial for practical applications.
Real-time Keypoints Detection for Autonomous Recovery of the Unmanned Ground Vehicle
UGV-KPNet is computationally efficient with a small number of parameters and provides pixel-level accurate keypoints detection results in real-time.
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching
Order dispatch is one of the central problems to ride-sharing platforms.
Anomalous phase separation dynamics in a correlated electron system: machine-learning enabled large-scale kinetic Monte Carlo simulations
With the aid of modern machine learning methods, we demonstrate the first-ever large-scale kinetic Monte Carlo simulations of the phase separation process for the Falicov-Kimball model, which is one of the canonical strongly correlated electron systems.
TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval
Until now, the deep hashing for the image retrieval community has been dominated by convolutional neural network architectures, e. g. \texttt{Resnet}\cite{he2016deep}.
Joint Universal Syntactic and Semantic Parsing
While numerous attempts have been made to jointly parse syntax and semantics, high performance in one domain typically comes at the price of performance in the other.
L-SNet: from Region Localization to Scale Invariant Medical Image Segmentation
Coarse-to-fine models and cascade segmentation architectures are widely adopted to solve the problem of large scale variations in medical image segmentation.
A submetric characterization of Rolewicz's property ($β$)
The main result is a submetric characterization of the class of Banach spaces admitting an equivalent norm with Rolewicz's property ($\beta$).
Functional Analysis
Experimental demonstration of memory-enhanced scaling for entanglement connection of quantum repeater segments
The experimental realization of entanglement connection of two quantum repeater segments with an efficient memory-enhanced scaling demonstrates a key advantage of the quantum repeater protocol, which makes a cornerstone towards future large-scale quantum networks.
Quantum Physics
GraphCGAN: Convolutional Graph Neural Network with Generative Adversarial Networks
In a number of experiments on benchmark datasets, we show that the proposed GraphCGAN outperforms the baseline methods by a significant margin.
Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning
Effectively filtering out noisy articles as well as bad answers is the key to improving extraction accuracy.
Worst-Case-Aware Curriculum Learning for Zero and Few Shot Transfer
Multi-task transfer learning based on pre-trained language encoders achieves state-of-the-art performance across a range of tasks.
Augmented Gaussian Random Field: Theory and Computation
We prove that under certain conditions, the observable and its derivatives of any order are governed by a single Gaussian random field, which is the aforementioned AGRF.
Statistics Theory Probability Statistics Theory
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
Deepfake defense not only requires the research of detection but also requires the efforts of generation methods.
Ranked #1 on
Face Swapping
on FaceForensics++
Commonsense Inference in Natural Language Processing (COIN) - Shared Task Report
This paper reports on the results of the shared tasks of the COIN workshop at EMNLP-IJCNLP 2019.
Universal Decompositional Semantic Parsing
We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores.
The Universal Decompositional Semantics Dataset and Decomp Toolkit
We present the Universal Decompositional Semantics (UDS) dataset (v1. 0), which is bundled with the Decomp toolkit (v0. 1).
Broad-Coverage Semantic Parsing as Transduction
We unify different broad-coverage semantic parsing tasks under a transduction paradigm, and propose an attention-based neural framework that incrementally builds a meaning representation via a sequence of semantic relations.
Ranked #2 on
UCCA Parsing
on SemEval 2019 Task 1
Quantum Communication between Multiplexed Atomic Quantum Memories
The use of multiplexed atomic quantum memories (MAQM) can significantly enhance the efficiency to establish entanglement in a quantum network.
Quantum Physics
SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations
We demonstrate how to use our algorithm step by step and compare our algorithm with threshold sparse Bayesian regression (TSBR) for the discovery of differential equations.
Neural Machine Reading Comprehension: Methods and Trends
Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years.
Omnidirectional Scene Text Detection with Sequential-free Box Discretization
Scene text in the wild is commonly presented with high variant characteristics.
Ranked #1 on
Scene Text Detection
on IC19-ReCTs
(using extra training data)
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning
To demonstrate the generality of our theoretical results on Markovian SA, we use it to derive the finite-sample bounds of the popular $Q$-learning with linear function approximation algorithm, under a condition on the behavior policy.
AMR Parsing as Sequence-to-Graph Transduction
Our experimental results outperform all previously reported SMATCH scores, on both AMR 2. 0 (76. 3% F1 on LDC2017T10) and AMR 1. 0 (70. 2% F1 on LDC2014T12).
Ranked #1 on
AMR Parsing
on LDC2014T12:
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning
Commonsense reasoning is fundamental to natural language understanding.
Ranked #3 on
Natural Language Understanding
on PDP60
Learning to Detect Instantaneous Changes with Retrospective Convolution and Static Sample Synthesis
Change detection has been a challenging visual task due to the dynamic nature of real-world scenes.
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning.
Cross-lingual Decompositional Semantic Parsing
We introduce the task of cross-lingual decompositional semantic parsing: mapping content provided in a source language into a decompositional semantic analysis based on a target language.
CRF-LSTM Text Mining Method Unveiling the Pharmacological Mechanism of Off-target Side Effect of Anti-Multiple Myeloma Drugs
Sequence labeling of biomedical entities, e. g., side effects or phenotypes, was a long-term task in BioNLP and MedNLP communities.
Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction
Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios.
Cross-lingual Semantic Parsing
We introduce the task of cross-lingual semantic parsing: mapping content provided in a source language into a meaning representation based on a target language.
Neural-Davidsonian Semantic Proto-role Labeling
We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call "Neural-Davidsonian": predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence.
Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds
Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions.
FPGA Implementations of 3D-SIMD Processor Architecture for Deep Neural Networks Using Relative Indexed Compressed Sparse Filter Encoding Format and Stacked Filters Stationary Flow
It is a challenging task to deploy computationally and memory intensive State-of-the-art deep neural networks (DNNs) on embedded systems with limited hardware resources and power budgets.
Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks
To address memory and computation resource limitations for hardware-oriented acceleration of deep convolutional neural networks (CNNs), we present a computation flow, stacked filters stationary flow (SFS), and a corresponding data encoding format, relative indexed compressed sparse filter format (CSF), to make the best of data sparsity, and simplify data handling at execution time.
Random Euler Complex-Valued Nonlinear Filters
Over the last decade, both the neural network and kernel adaptive filter have successfully been used for nonlinear signal processing.
Feature Enhancement Network: A Refined Scene Text Detector
In this paper, we propose a refined scene text detector with a \textit{novel} Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement.
Selective Decoding for Cross-lingual Open Information Extraction
Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language.
FuRongWang at SemEval-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering
We describes deep neural networks frameworks in this paper to address the community question answering (cQA) ranking task (SemEval-2017 task 3).
MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models
Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa).
Deep Generalized Canonical Correlation Analysis
We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other.
Ordinal Common-sense Inference
Humans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly.
Randomized Structural Sparsity based Support Identification with Applications to Locating Activated or Discriminative Brain Areas: A Multi-center Reproducibility Study
In this paper, we focus on how to locate the relevant or discriminative brain regions related with external stimulus or certain mental decease, which is also called support identification, based on the neuroimaging data.
Discrete Weierstrass Fourier Transform and Experiments
We established a new method called Discrete Weierstrass Fourier Transform, a faster and more generalized Discrete Fourier Transform, to approximate discrete data.
Numerical Analysis
Randomized Structural Sparsity via Constrained Block Subsampling for Improved Sensitivity of Discriminative Voxel Identification
In this paper, we consider voxel selection for functional Magnetic Resonance Imaging (fMRI) brain data with the aim of finding a more complete set of probably correlated discriminative voxels, thus improving interpretation of the discovered potential biomarkers.
