Multimodal Large Language Models (MLLMs) that integrate text and other
m...
Knowledge graph embedding (KGE) focuses on representing the entities and...
While significant progress has been made on Physics-Informed Neural Netw...
In multivariate time series systems, key insights can be obtained by
dis...
Due to the flexibility of prompting, foundation models have become the
d...
This work investigates pretrained audio representations for few shot Sou...
Negative sampling (NS) is widely used in knowledge graph embedding (KGE)...
Asymmetrical multiplayer (AMP) game is a popular game genre which involv...
A good metric, which promises a reliable comparison between solutions, i...
3D object detection is an essential perception task in autonomous drivin...
Deep learning models are vulnerable to adversarial examples. Transfer-ba...
The conference peer review process involves three constituencies with
di...
The rapid scaling of language models is motivating research using
low-bi...
Recent advances of data-driven machine learning have revolutionized fiel...
Recent years have seen an increasing amount of work on embodied AI agent...
In this work, we share our experience on tele-knowledge pre-training for...
Knowledge graphs (KGs) that modelings the world knowledge as structural
...
The R-learner has been popular in causal inference as a flexible and
eff...
Medical image segmentation is a fundamental and critical step in many
im...
This article illustrates a novel Quantum Secure Aggregation (QSA) scheme...
Randomized singular value decomposition (RSVD) is a class of computation...
Hyperdimensional computing (HDC) is an emerging learning paradigm that
c...
Medical image segmentation is a fundamental and critical step in many
cl...
Top-1 ImageNet optimization promotes enormous networks that may be
impra...
Despite great success on many machine learning tasks, deep neural networ...
Modern object detectors are vulnerable to adversarial examples, which br...
Neural network robustness has become a central topic in machine learning...
Large-scale, pre-trained language models (LMs) have achieved human-level...
Development of deep learning systems for biomedical segmentation often
r...
Nowadays, customer's demands for E-commerce are more diversified, which
...
Entity Matching (EM) aims at recognizing entity records that denote the ...
Despite recent progress, learning new tasks through language instruction...
Conversational Recommender Systems (CRSs) in E-commerce platforms aim to...
Multi-hop reasoning has been widely studied in recent years to obtain mo...
Sequential recommendation as an emerging topic has attracted increasing
...
The success of deep learning methods in medical image segmentation tasks...
Random-walk based network embedding algorithms like node2vec and DeepWal...
The novel Coronavirus disease (COVID-19) is a highly contagious virus an...
Automatic segmentation of myocardial contours and relevant areas like
in...
Binary neural networks (BNNs) have 1-bit weights and activations. Such
n...
With the unprecedented developments in deep learning, automatic segmenta...
With the unprecedented developments in deep learning, many methods are
p...
Multi-hop reasoning has been widely studied in recent years to seek an
e...
Structured belief states are crucial for user goal tracking and database...
Neural generative models have achieved promising performance on dialog
g...
Covariance estimation for matrix-valued data has received an increasing
...
Electronic Health Records (EHR) data, a rich source for biomedical resea...
We propose precision gating (PG), an end-to-end trainable dynamic
dual-p...
Conversations have an intrinsic one-to-many property, which means that
m...
Event detection (ED), a sub-task of event extraction, involves identifyi...