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Found 43 papers, 5 papers with code
KEViN: A Knowledge Enhanced Validity and Novelty Classifier for Arguments
The ArgMining 2022 Shared Task is concerned with predicting the validity and novelty of an inference for a given premise and conclusion pair.
Ranked #2 on
ValNov
on ValNov Subtask A
Prokaryotic genome editing based on the subtype I-B-Svi CRISPR-Cas system
Type I CRISPR-Cas systems are the most common among six types of CRISPR-Cas systems, however, non-self-targeting genome editing based on a single Cas3 of type I CRISPR-Cas systems has not been reported.
Revisiting Long-tailed Image Classification: Survey and Benchmarks with New Evaluation Metrics
We set up novel evaluation benchmarks based on a series of testing sets with evolving distributions.
Ranked #55 on
Long-tail Learning
on CIFAR-100-LT (ρ=100)
Tired of Over-smoothing? Stress Graph Drawing Is All You Need!
In designing and applying graph neural networks, we often fall into some optimization pitfalls, the most deceptive of which is that we can only build a deep model by solving over-smoothing.
Causal Coupled Mechanisms: A Control Method with Cooperation and Competition for Complex System
Complex systems are ubiquitous in the real world and tend to have complicated and poorly understood dynamics.
TEDL: A Two-stage Evidential Deep Learning Method for Classification Uncertainty Quantification
In this paper, we propose TEDL, a two-stage learning approach to quantify uncertainty for deep learning models in classification tasks, inspired by our findings in experimenting with Evidential Deep Learning (EDL) method, a recently proposed uncertainty quantification approach based on the Dempster-Shafer theory.
KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction
Recently, with the rise of high dynamic range (HDR) display devices, there is a great demand to transfer traditional low dynamic range (LDR) images into HDR versions.
Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map
Place recognition technology endows a SLAM algorithm with the ability to eliminate accumulated errors and to relocalize itself.
Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation
Reinforcement learning has shown a wide usage in robotics tasks, such as insertion and grasping.
Integrating Dependency Tree Into Self-attention for Sentence Representation
Recent progress on parse tree encoder for sentence representation learning is notable.
Recovery Time Metric Demonstrated on Real-world Electric Grid for Hurricane Impacted Outages
This work proposes a methodology for estimating recovery times for transmission lines and substations, and is demonstrated on a real-world 1269-bus power system model of Puerto Rico under 20 hurricane scenarios, or stochastic realizations of asset failure under the meteorological conditions of Hurricane Maria.
Signature Entrenchment and Conceptual Changes in Automated Theory Repair
Human beliefs change, but so do the concepts that underpin them.
Camera-aware Style Separation and Contrastive Learning for Unsupervised Person Re-identification
Unsupervised person re-identification (ReID) is a challenging task without data annotation to guide discriminative learning.
Secure Control of Networked Control Systems Using Dynamic Watermarking
We here investigate secure control of networked control systems developing a new dynamic watermarking (DW) scheme.
Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning
We prove that less information is exposed in CGD compared to that of traditional FL.
An ensemble solver for segregated cardiovascular FSI
Computational models are increasingly used for diagnosis and treatment of cardiovascular disease.
Computational Engineering, Finance, and Science Numerical Analysis Numerical Analysis
TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search
Text encoders based on C-DSSM or transformers have demonstrated strong performance in many Natural Language Processing (NLP) tasks.
The Tsinghua University-Ma Huateng Telescopes for Survey: Overview and Performance of the System
The TMTS system can have a FoV of about 9 deg2 when monitoring the sky with two bands (i. e., SDSS g and r filters) at the same time, and a maximum FoV of ~18 deg2 when four telescopes monitor different sky areas in monochromatic filter mode.
Instrumentation and Methods for Astrophysics
Learning Causal Bayesian Networks from Text
Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems.
A Unified Model for Recommendation with Selective Neighborhood Modeling
To address this issue, we propose a novel neighborhood-based recommender, where a hybrid gated network is designed to automatically separate similar neighbors from dissimilar (noisy) ones, and aggregate those similar neighbors to comprise neighborhood representations.
Understanding the Message Passing in Graph Neural Networks via Power Iteration Clustering
The mechanism of message passing in graph neural networks (GNNs) is still mysterious.
Learning Various Length Dependence by Dual Recurrent Neural Networks
Many variants of RNN have been proposed to solve the gradient problems of training RNNs and process long sequences.
Disentanglement Then Reconstruction: Learning Compact Features for Unsupervised Domain Adaptation
By this reconstructor, we can construct prototypes for the original features using class prototypes and domain prototypes correspondingly.
Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment.
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning
Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization.
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking
Across many areas, from neural tracking to database entity resolution, manual assessment of clusters by human experts presents a bottleneck in rapid development of scalable and specialized clustering methods.
Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks
In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record.
Sequence-Aware Factorization Machines for Temporal Predictive Analytics
As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics.
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
Suicide is a critical issue in modern society.
DBRec: Dual-Bridging Recommendation via Discovering Latent Groups
In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations.
Learning Fast Matching Models from Weak Annotations
The proposed training framework targets on mitigating both issues, by treating the stronger but undeployable models as annotators, and learning a deployable model from both human provided relevance labels and weakly annotated search log data.
Learning Private Neural Language Modeling with Attentive Aggregation
Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.
Where-and-When to Look: Deep Siamese Attention Networks for Video-based Person Re-identification
Video-based person re-identification (re-id) is a central application in surveillance systems with significant concern in security.
YH Technologies at ActivityNet Challenge 2018
This notebook paper presents an overview and comparative analysis of our systems designed for the following five tasks in ActivityNet Challenge 2018: temporal action proposals, temporal action localization, dense-captioning events in videos, trimmed action recognition, and spatio-temporal action localization.
The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing
We propose a novel deep neural network architecture for the challenging problem of single image dehazing, which aims to recover the clear image from a degraded hazy image.
When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity
Inspired by point process in modeling temporal point process, in this paper we present a deep prediction method based on two recurrent neural networks (RNNs) to jointly model each user's continuous browsing history and asynchronous event sequences in the context of inter-user behavioral mutual infectivity.
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
To address \emph{what} to match, our deep network emphasizes common local patterns by learning joint representations in a multiplicative way.
PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking
Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge.
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding.
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection
The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time.
Uncovering Locally Discriminative Structure for Feature Analysis
We propose a method that utilizes both the manifold structure of data and local discriminant information.
Unsupervised Feature Analysis with Class Margin Optimization
In this paper, we propose an unsupervised feature selection method seeking a feature coefficient matrix to select the most distinctive features.
Image Tag Completion by Low-rank Factorization with Dual Reconstruction Structure Preserved
A novel tag completion algorithm is proposed in this paper, which is designed with the following features: 1) Low-rank and error s-parsity: the incomplete initial tagging matrix D is decomposed into the complete tagging matrix A and a sparse error matrix E. However, instead of minimizing its nuclear norm, A is further factor-ized into a basis matrix U and a sparse coefficient matrix V, i. e. D=UV+E.
