Search Results for author:
Found 20 papers, 4 papers with code
In-Context Learning State Vector with Inner and Momentum Optimization
In this paper, we address this gap by presenting a comprehensive analysis of these compressed vectors, drawing parallels to the parameters trained with gradient descent, and introduce the concept of state vector.
Improving Attributed Text Generation of Large Language Models via Preference Learning
Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content.
P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer
Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data.
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons
Deep Neural Networks (DNNs) have revolutionized a wide range of industries, from healthcare and finance to automotive, by offering unparalleled capabilities in data analysis and decision-making.
Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images
Furthermore, we demonstrate that our model can generalize to two unseen sites with 229 WSIs.
Dig-CSI: A Distributed and Generative Model Assisted CSI Feedback Training Framework
The advent of deep learning (DL)-based models has significantly advanced Channel State Information (CSI) feedback mechanisms in wireless communication systems.
A Survey of Large Language Models Attribution
Open-domain generative systems have gained significant attention in the field of conversational AI (e. g., generative search engines).
Explaining How a Neural Network Play the Go Game and Let People Learn
The AI model has surpassed human players in the game of Go, and it is widely believed that the AI model has encoded new knowledge about the Go game beyond human players.
Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation
To evaluate the effectiveness of our approach in terms of truthfulness and detoxification, we conduct extensive experiments on LLMs, encompassing additional abilities such as language modeling and mathematical reasoning.
HybridCVLNet: A Hybrid CSI Feedback System and its Domain Adaptation
Deep Learning (DL)-based channel state information (CSI) feedback is a promising technique for the transmitter to accurately acquire the CSI of massive multiple-input multiple-output (MIMO) systems.
Task-Balanced Distillation for Object Detection
To alleviate this problem, a novel Task-decoupled Feature Distillation (TFD) is proposed by flexibly balancing the contributions of classification and regression tasks.
Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention
Safety has become one of the main challenges of applying deep reinforcement learning to real world systems.
Multi-Faceted Representation Learning with Hybrid Architecture for Time Series Classification
Time series classification problems exist in many fields and have been explored for a couple of decades.
A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback
Forward channel state information (CSI) often plays a vital role in scheduling and capacity-approaching transmission optimization for massive multiple-input multiple-output (MIMO) communication systems.
Text Classification based on Multi-granularity Attention Hybrid Neural Network
Conventionally, there are two mainstream neural architectures for NLP tasks: the recurrent neural network (RNN) and the convolution neural network (ConvNet).
Human Activity Recognition based on Dynamic Spatio-Temporal Relations
In the proposed method, an activity is divided into several successive actions represented by spatio temporal patterns, and the evolution of these actions are captured by a sequential model.
Multichannel CNN with Attention for Text Classification
Alternatively, CNN is able to capture n-gram features of texts by utilizing convolutional filters.
A Novel Decision Tree for Depression Recognition in Speech
Depression is a common mental disorder worldwide which causes a range of serious outcomes.
MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis
The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.
Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design
The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution neural network on embedded platforms.
