Recent advancements in language models (LMs) have gained substantial
att...
Logs play a crucial role in system monitoring and debugging by recording...
Intelligent robots are designed to effectively navigate dynamic and
unpr...
Autonomous robotic systems, like autonomous vehicles and robotic search ...
Estimating the shape and motion state of the myocardium is essential in
...
We hypothesize that large language models (LLMs) based on the transforme...
Recently, visual-language learning has shown great potential in enhancin...
Recent research has demonstrated the potential of reinforcement learning...
Transformer-based pretrained language models (PLMs) have achieved great
...
Future intelligent robots are expected to process multiple inputs
simult...
DL compiler's primary function is to translate DNN programs written in
h...
A large number of annotated training images is crucial for training
succ...
Although pre-trained language models (PLMs) have recently advanced the
r...
LiDAR and Radar are two complementary sensing approaches in that LiDAR
s...
Deep Learning (DL) models have been popular nowadays to execute differen...
Modeling multi-party conversations (MPCs) with graph neural networks has...
Zero-shot cross-lingual information extraction(IE) aims at constructing ...
Despite much success in natural language processing (NLP), pre-trained
l...
Lifelong learning agents aim to learn multiple tasks sequentially over a...
Addressing the issues of who saying what to whom in multi-party conversa...
The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st Internat...
Jointly extracting entity pairs and their relations is challenging when
...
Pre-trained transformers are popular in state-of-the-art dialogue genera...
Historical manuscript processing poses challenges like limited annotated...
The problem of document structure reconstruction refers to converting di...
A hard challenge in developing practical face recognition (FR) attacks i...
The Multi-modal Information based Speech Processing (MISP) challenge aim...
Table structure recognition is an indispensable element for enabling mac...
Deep learning models are challenged by the distribution shift between th...
Adversarial attacks are valuable for evaluating the robustness of deep
l...
Deep neural networks (DNNs) are vulnerable to backdoor attacks, where
ad...
Most recent studies on neural constituency parsing focus on encoder
stru...
Zero-shot cross-lingual named entity recognition (NER) aims at transferr...
Speech pre-training has shown great success in learning useful and gener...
Self-supervised learning (SSL) models have achieved considerable improve...
Multilingual end-to-end models have shown great improvement over monolin...
In this work, we present a novel method, named AV2vec, for learning
audi...
In constituency parsing, span-based decoding is an important direction.
...
We propose a novel algorithm that improves on the previous neural span-b...
The attention mechanism requires huge computational efforts to process
u...
Today, an increasing number of Adaptive Deep Neural Networks (AdNNs) are...
Neural Machine Translation (NMT) systems have received much recent atten...
Asynchronous frameworks for distributed embedded systems, like ROS and M...
This paper aims to advance the mathematical intelligence of machines by
...
With the privatization deployment of DNNs on edge devices, the security ...
Neural image caption generation (NICG) models have received massive atte...
Recently, Transformers have shown promising performance in various visio...
Recently, various Deep Neural Network (DNN) models have been proposed fo...
Knowledge graph embedding (KGE) models learn the representation of entit...
While the real world application of reinforcement learning (RL) is becom...