Search Results for author:
Found 63 papers, 19 papers with code
Large Batch Optimization for Object Detection: Training COCO in 12 Minutes
Most of existing object detectors usually adopt a small training batch size ( ~16), which severely hinders the whole community from exploring large-scale datasets due to the extremely long training procedure.
Transparency Promotion with Model-Agnostic Linear Competitors
We propose a novel type of hybrid model for multi-class classification, which utilizes competing linear models to collaborate with an existing black-box model, promoting transparency in the decision-making process.
Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations
We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings.
Dual Contrastive Attributed Graph Clustering Network
Attributed graph clustering is one of the most important tasks in graph analysis field, the goal of which is to group nodes with similar representations into the same cluster without manual guidance.
Multi-View Substructure Learning for Drug-Drug Interaction Prediction
Drug-drug interaction (DDI) prediction provides a drug combination strategy for systemically effective treatment.
Better Language Model with Hypernym Class Prediction
Class-based language models (LMs) have been long devised to address context sparsity in $n$-gram LMs.
Functional universality in slow-growing microbial communities arises from thermodynamic constraints
To understand the community structure in these energy-limited environments, we developed a microbial community consumer-resource model incorporating energetic and thermodynamic constraints on an interconnected metabolic network.
Direct Molecular Conformation Generation
In this work, we propose a method that directly predicts the coordinates of atoms.
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection
The decision boundary between any two categories is the angular bisector of their weight vectors.
DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points
To make the local features aware of the global context and improve their matching accuracy, we introduce DenseGAP, a new solution for efficient Dense correspondence learning with a Graph-structured neural network conditioned on Anchor Points.
Backdoor Attack through Frequency Domain
We also evaluate FTROJAN against state-of-the-art defenses as well as several adaptive defenses that are designed on the frequency domain.
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP
Meta-learning considers the problem of learning an efficient learning process that can leverage its past experience to accurately solve new tasks.
Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer
The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery.
Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis
This work presents a lifelong learning approach to train a multilingual Text-To-Speech (TTS) system, where each language was seen as an individual task and was learned sequentially and continually.
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations
We propose a framework for such decisions, including a globally interpretable machine learning model, an interactive visualization of it, and several types of summaries and explanations for any given decision.
Entity Resolution in Open-domain Conversations
In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest.
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems
In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved.
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents
Faceted summarization provides briefings of a document from different perspectives.
Ranked #1 on
Unsupervised Extractive Summarization
on FacetSum
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning
We propose Partially Interpretable Estimators (PIE) which attribute a prediction to individual features via an interpretable model, while a (possibly) small part of the PIE prediction is attributed to the interaction of features via a black-box model, with the goal to boost the predictive performance while maintaining interpretability.
Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations
For example, with "add milk to my cart", a customer may refer to a certain organic product, while some customers may want to re-order products they regularly purchase.
Adaptive Class Suppression Loss for Long-Tail Object Detection
To address the problem of long-tail distribution for the large vocabulary object detection task, existing methods usually divide the whole categories into several groups and treat each group with different strategies.
Quantum Transport in Two-Dimensional WS$_2$ with High-Efficiency Carrier Injection Through Indium Alloy Contacts
Here, temperature-dependent transfer length measurements are performed on chemical vapour deposition grown single-layer and bilayer WS$_2$ devices with indium alloy contacts.
Materials Science Mesoscale and Nanoscale Physics
Stochastic social behavior coupled to COVID-19 dynamics leads to waves, plateaus and an endemic state
It is well recognized that population heterogeneity plays an important role in the spread of epidemics.
A SOM-based Gradient-Free Deep Learning Method with Convergence Analysis
As gradient descent method in deep learning causes a series of questions, this paper proposes a novel gradient-free deep learning structure.
Exploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2 Spike Protein Through Molecular Dynamics Simulation
We performed molecular dynamics simulation on the S protein with a focus on the function of its N-terminal domains (NTDs).
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness
We formulate a multi-objective optimization for building a surrogate model, where we simultaneously optimize for both predictive performance and bias.
An Empirical Study on Neural Keyphrase Generation
Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them.
Same-Day Delivery with Fairness
The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particularly boomed during the COVID-19 pandemic.
Interpretable Sequence Classification Via Prototype Trajectory
We propose a novel interpretable deep neural network for text classification, called ProtoryNet, based on a new concept of prototype trajectories.
Exploring and Predicting Transferability across NLP Tasks
We also develop task embeddings that can be used to predict the most transferable source tasks for a given target task, and we validate their effectiveness in experiments controlled for source and target data size.
Task-adaptive Asymmetric Deep Cross-modal Hashing
Unlike previous cross-modal hashing approaches, our learning framework jointly optimizes semantic preserving that transforms deep features of multimedia data into binary hash codes, and the semantic regression which directly regresses query modality representation to explicit label.
Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication
We consider a system to optimize duration of traffic signals using multi-agent deep reinforcement learning and Vehicle-to-Everything (V2X) communication.
Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence
We used the three models to study each community's limits of robustness to perturbations such as variations in resource supply, antibiotic treatments and invasion by other "cheaters" species.
Interpretable Companions for Black-Box Models
We present an interpretable companion model for any pre-trained black-box classifiers.
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs
The prediction model and the interpretable insights can be applied to assist fundraisers with better promoting their fundraising campaigns and can potentially help crowdfunding platforms to provide more timely feedback to all fundraisers.
Model-Agnostic Linear Competitors -- When Interpretable Models Compete and Collaborate with Black-Box Models
Driven by an increasing need for model interpretability, interpretable models have become strong competitors for black-box models in many real applications.
Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence
Recently, concatenating multiple keyphrases as a target sequence has been proposed as a new learning paradigm for keyphrase generation.
Metalearned Neural Memory
We augment recurrent neural networks with an external memory mechanism that builds upon recent progress in metalearning.
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model
The interpretable model substitutes the black-box model on a subset of data where the black-box is overkill or nearly overkill, gaining transparency at no or low cost of the predictive accuracy.
Next Hit Predictor - Self-exciting Risk Modeling for Predicting Next Locations of Serial Crimes
Our goal is to predict the location of the next crime in a crime series, based on the identified previous offenses in the series.
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations
We present the Multi-value Rule Set (MRS) for interpretable classification with feature efficient presentations.
An Interpretable Model with Globally Consistent Explanations for Credit Risk
We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment.
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases
With both previous and new evaluation metrics, our model outperforms strong baselines on all datasets.
Interpretable Patient Mortality Prediction with Multi-value Rule Sets
We propose a Bayesian framework for formulating a MRS model and propose an efficient inference method for learning a maximum \emph{a posteriori}, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency.
Neural Models for Key Phrase Extraction and Question Generation
We propose a two-stage neural model to tackle question generation from documents.
Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute
This work addresses the situation where a black-box model with good predictive performance is chosen over its interpretable competitors, and we show interpretability is still achievable in this case.
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
The Bayesian model has tunable parameters that can characterize subgroups with various sizes, providing users with more flexible choices of models from the \emph{treatment efficient frontier}.
Multi-Value Rule Sets
MARS introduces a more generalized form of association rules that allows multiple values in a condition.
Annotating High-Level Structures of Short Stories and Personal Anecdotes
Stories are a vital form of communication in human culture; they are employed daily to persuade, to elicit sympathy, or to convey a message.
Predicting the Quality of Short Narratives from Social Media
An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality.
Neural Models for Key Phrase Detection and Question Generation
We propose a two-stage neural model to tackle question generation from documents.
A Joint Model for Question Answering and Question Generation
We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents.
Machine Comprehension by Text-to-Text Neural Question Generation
We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers.
A Semantic QA-Based Approach for Text Summarization Evaluation
In this paper, we will present some preliminary results on one especially useful and challenging problem in NLP system evaluation: how to pinpoint content differences of two text passages (especially for large pas-sages such as articles and books).
NewsQA: A Machine Comprehension Dataset
We present NewsQA, a challenging machine comprehension dataset of over 100, 000 human-generated question-answer pairs.
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering.
Topic Modeling over Short Texts by Incorporating Word Embeddings
Inferring topics from the overwhelming amount of short texts becomes a critical but challenging task for many content analysis tasks, such as content charactering, user interest profiling, and emerging topic detecting.
An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification
Text simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning.
Learning Optimized Or's of And's
Or's of And's (OA) models are comprised of a small number of disjunctions of conjunctions, also called disjunctive normal form.
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems
In both cases, there are prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domain-specific definition of interpretability.
