Continual learning aims to empower artificial intelligence (AI) with str...
Three-dimensional (3D) freehand ultrasound (US) reconstruction without u...
Adversarial training (AT) is widely considered the state-of-the-art tech...
Fairness-aware recommendation eliminates discrimination issues to build
...
Graph collaborative filtering (GCF) has gained considerable attention in...
This paper proposes BPNet, a novel end-to-end deep learning framework to...
The concentration of power in a few digital technology companies has bec...
Quantized Neural Networks (QNNs) receive increasing attention in
resourc...
The multi-modal entity alignment (MMEA) aims to find all equivalent enti...
Counterfactual explanation is a form of interpretable machine learning t...
Counterfactual fairness alleviates the discrimination between the model
...
Graph neural networks (GNNs) have been proposed for medical image
segmen...
Taking full advantage of the excellent performance of StyleGAN, style
tr...
The Pretrained Foundation Models (PFMs) are regarded as the foundation f...
Prompt tuning (PT) which only tunes the embeddings of an additional sequ...
Automatic knowledge graph construction aims to manufacture structured hu...
Rapidly learning from ongoing experiences and remembering past events wi...
We propose TROD, a novel transaction-oriented framework for debugging mo...
Violations of laws and regulations about food safety, production safety,...
Event detection in power systems aims to identify triggers and event typ...
Three-dimensional (3D) freehand ultrasound (US) reconstruction without a...
Sparsity of formal knowledge and roughness of non-ontological constructi...
Developers are increasingly using function-as-a-service (FaaS) platforms...
Federated learning (FL) facilitates multiple clients to jointly train a
...
Topology-imbalance is a graph-specific imbalance problem caused by the u...
We recently proposed a new cluster operating system stack, DBOS, centere...
We explore a new strategy for few-shot novel view synthesis based on a n...
Counterfactual explanations interpret the recommendation mechanism via
e...
Continual learning requires incremental compatibility with a sequence of...
Graph contrastive learning has emerged as a powerful tool for unsupervis...
Modeling forests using historical data allows for more accurately evolut...
We show how to distinguish circuits with log k negations (a.k.a
k-monoto...
In the paper, we present an approach for learning a single model that
un...
While Computed Tomography (CT) reconstruction from X-ray sinograms is
ne...
Physical unclonable function (PUF) has been proposed as a promising and
...
In a seminal paper (Moser and Tardos, JACM'10), Moser and Tardos develop...
Continual learning aims to learn a sequence of tasks from dynamic data
d...
Knowledge Graph Embedding (KGE) aims to learn representations for entiti...
Information security is of great importance for modern society with all
...
Event extraction (EE), which acquires structural event knowledge from te...
Fast arbitrary neural style transfer has attracted widespread attention ...
Schema-based event extraction is a critical technique to apprehend the
e...
Event extraction (EE) is a crucial information extraction task that aims...
Estimation of the precision matrix (or inverse covariance matrix) is of ...
Event extraction is a fundamental task for natural language processing.
...
Spine-related diseases have high morbidity and cause a huge burden of so...
Causal inference methods are widely applied in various decision-making
d...
Causal inference methods are widely applied in various decision-making
d...
In recommendation systems, the existence of the missing-not-at-random (M...
Counterfactual explanation is one branch of interpretable machine learni...