Search Results for author:
Found 61 papers, 17 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.
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-Supervised 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.
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
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
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.
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.
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.
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.
