research
∙
09/07/2023
DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions
As it is empirically observed that Vision Transformers (ViTs) are quite ...
share
research
∙
06/27/2023
Wasserstein Generative Regression
In this paper, we propose a new and unified approach for nonparametric r...
research
∙
01/04/2023
Fragment-based t-SMILES for de novo molecular generation
At present, sequence-based and graph-based models are two of popular use...
research
∙
12/13/2022
Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning
In contrastive self-supervised learning, the common way to learn discrim...
research
∙
11/25/2022
GPT-3-driven pedagogical agents for training children's curious question-asking skills
Students' ability to ask curious questions is a crucial skill that impro...
research
∙
11/23/2022
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022
In the technical report, we provide our solution for OGB-LSC 2022 Graph ...
research
∙
11/06/2022
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
While deep reinforcement learning has proven to be successful in solving...
research
∙
10/23/2022
Learning to Advise Humans By Leveraging Algorithm Discretion
Expert decision-makers (DMs) in high-stakes AI-advised (AIDeT) settings ...
research
∙
10/20/2022
Low-Latency Hybrid NOMA-TDMA: QoS-Driven Design Framework
Enabling ultra-reliable and low-latency communication services while pro...
research
∙
09/22/2022
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation
Large Language Models (LLMs) have in recent years demonstrated impressiv...
research
∙
08/20/2022
General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation
Training keyphrase generation (KPG) models requires a large amount of an...
research
∙
08/13/2022
Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer
Backdoor attacks have been shown to be a serious security threat against...
research
∙
06/16/2022
Dual Contrastive Attributed Graph Clustering Network
Attributed graph clustering is one of the most important tasks in graph ...
research
∙
04/27/2022
Towards Exploring the Code Reuse from Stack Overflow during Software Development
As one of the most well-known programmer Q A websites, Stack Overflow ...
research
∙
03/28/2022
Multi-View Substructure Learning for Drug-Drug Interaction Prediction
Drug-drug interaction (DDI) prediction provides a drug combination strat...
research
∙
03/21/2022
Better Language Model with Hypernym Class Prediction
Class-based language models (LMs) have been long devised to address cont...
research
∙
03/18/2022
Efficient Estimation of the Additive Risks Model for Interval-Censored Data
In contrast to the popular Cox model which presents a multiplicative cov...
research
∙
02/03/2022
Direct Molecular Conformation Generation
Molecular conformation generation aims to generate three-dimensional coo...
research
∙
12/13/2021
DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points
Establishing dense correspondence between two images is a fundamental co...
research
∙
11/22/2021
Backdoor Attack through Frequency Domain
Backdoor attacks have been shown to be a serious threat against deep lea...
research
∙
11/02/2021
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP
Meta-learning considers the problem of learning an efficient learning pr...
research
∙
10/14/2021
Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer
The identification of active binding drugs for target proteins (termed a...
research
∙
10/09/2021
Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis
This work presents a lifelong learning approach to train a multilingual ...
research
∙
09/14/2021
Incentivizing Mobile Edge Caching and Sharing: An Evolutionary Game Approach
Mobile Edge Caching is a promising technique to enhance the content deli...
research
∙
06/04/2021
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations
Lending decisions are usually made with proprietary models that provide ...
research
∙
05/31/2021
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents
Faceted summarization provides briefings of a document from different pe...
research
∙
05/06/2021
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning
We propose Partially Interpretable Estimators (PIE) which attribute a pr...
research
∙
04/02/2021
Adaptive Class Suppression Loss for Long-Tail Object Detection
To address the problem of long-tail distribution for the large vocabular...
research
∙
01/12/2021
A SOM-based Gradient-Free Deep Learning Method with Convergence Analysis
As gradient descent method in deep learning causes a series of questions...
research
∙
11/17/2020
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness
We propose a model-agnostic approach for mitigating the prediction bias ...
research
∙
09/22/2020
An Empirical Study on Neural Keyphrase Generation
Recent years have seen a flourishing of neural keyphrase generation work...
research
∙
09/16/2020
Monetizing Edge Service in Mobile Internet Ecosystem
In mobile Internet ecosystem, Mobile Users (MUs) purchase wireless data ...
research
∙
07/19/2020
Same-Day Delivery with Fairness
The demand for same-day delivery (SDD) has increased rapidly in the last...
research
∙
07/03/2020
Interpretable Sequence Classification Via Prototype Trajectory
We propose a novel interpretable recurrent neural network (RNN) model, c...
research
∙
05/02/2020
Exploring and Predicting Transferability across NLP Tasks
Recent advances in NLP demonstrate the effectiveness of training large-s...
research
∙
04/01/2020
Task-adaptive Asymmetric Deep Cross-modal Hashing
Supervised cross-modal hashing aims to embed the semantic correlations o...
research
∙
03/10/2020
Crowd-MECS: A Novel Crowdsourcing Framework for Mobile Edge Caching and Sharing
Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as...
research
∙
02/23/2020
Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication
We consider a system to optimize duration of traffic signals using multi...
research
∙
02/10/2020
Interpretable Companions for Black-Box Models
We present an interpretable companion model for any pre-trained black-bo...
research
∙
12/01/2019
Efficient Estimation of Mixture Cure Frailty Model for Clustered Current Status Data
Current status data abounds in the field of epidemiology and public heal...
research
∙
09/23/2019
Model-Agnostic Linear Competitors – When Interpretable Models Compete and Collaborate with Black-Box Models
Driven by an increasing need for model interpretability, interpretable m...
research
∙
09/09/2019
Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence
Recently, concatenating multiple keyphrases as a target sequence has bee...
research
∙
07/23/2019
Metalearned Neural Memory
We augment recurrent neural networks with an external memory mechanism t...
research
∙
05/10/2019
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model
Interpretable machine learning has become a strong competitor for tradit...
research
∙
12/13/2018
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,...
research
∙
11/30/2018
An Interpretable Model with Globally Consistent Explanations for Credit Risk
We propose a possible solution to a public challenge posed by the Fair I...
research
∙
10/11/2018
Generating Diverse Numbers of Diverse Keyphrases
Existing keyphrase generation studies suffer from the problems of genera...
research
∙
07/06/2018
Interpretable Patient Mortality Prediction with Multi-value Rule Sets
We propose a Multi-vAlue Rule Set (MRS) model for in-hospital predicting...
research
∙
02/12/2018
Hybrid Decision Making: When Interpretable Models Collaborate With Black-Box Models
Interpretable machine learning models have received increasing interest ...
research
∙
12/28/2017
