Search Results for author:
Found 26 papers, 6 papers with code
Derivative-Free Optimization for Low-Rank Adaptation in Large Language Models
Parameter-efficient tuning methods such as LoRA could achieve comparable performance to model tuning by tuning a small portion of the parameters.
Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks and exhibited impressive reasoning abilities by applying zero-shot Chain-of-Thought (CoT) prompting.
Multi-Objective Complementary Control
This paper proposes a novel multi-objective control framework for linear time-invariant systems in which performance and robustness can be achieved in a complementary way instead of trade-off.
Data-Driven Goal Recognition in Transhumeral Prostheses Using Process Mining Techniques
A transhumeral prosthesis restores missing anatomical segments below the shoulder, including the hand.
Edge-aware Hard Clustering Graph Pooling for Brain Imaging
In this paper, we propose a novel edge-aware hard clustering graph pool (EHCPool), which is tailored to dominant edge features and redefines the clustering process.
VCD: Visual Causality Discovery for Cross-Modal Question Reasoning
Existing visual question reasoning methods usually fail to explicitly discover the inherent causal mechanism and ignore jointly modeling cross-modal event temporality and causality.
Temporal Dynamic Synchronous Functional Brain Network for Schizophrenia Diagnosis and Lateralization Analysis
The available evidence suggests that dynamic functional connectivity (dFC) can capture time-varying abnormalities in brain activity in resting-state cerebral functional magnetic resonance imaging (rs-fMRI) data and has a natural advantage in uncovering mechanisms of abnormal brain activity in schizophrenia(SZ) patients.
Robust Tracking Control for Nonlinear Systems: Performance optimization via extremum seeking
This paper presents a controller design and optimization framework for nonlinear dynamic systems to track a given reference signal in the presence of disturbances when the task is repeated over a finite-time interval.
Event-triggered Hybrid Energy-aware Scheduling in Manufacturing Systems
When the model does not work well, it is triggered as the variable to tune to improve the performance.
On Robust Observer Design for System Motion on SE(3) Using Onboard Visual Sensors
The key design idea for this observer is to estimate the visible set and identify the mis-identified features from the measurements.
Robust output regulation of linear system subject to modeled and unmodeled uncertainty
The output regulation scheme is utilized in the framework to track the reference in the presence of modeled disturbance, and the effect of unmodeled disturbance is reduced by an $\mathcal{H}_\infty$ compensator.
ProtoCLIP: Prototypical Contrastive Language Image Pretraining
Based on this understanding, in this paper, Prototypical Contrastive Language Image Pretraining (ProtoCLIP) is introduced to enhance such grouping by boosting its efficiency and increasing its robustness against the modality gap.
Self-supervised Contrastive Learning for Audio-Visual Action Recognition
To learn supervised information from unlabeled videos, we propose a novel self-supervised contrastive learning module (SelfCL).
Optimization of Directional Landmark Deployment for Visual Observer on SE(3)
An optimization method is proposed in this paper for novel deployment of given number of directional landmarks (location and pose) within a given region in the 3-D task space.
A Weak Monotonicity Based Muscle Fatigue Detection Algorithm for a Short-Duration Poor Posture Using sEMG Measurements
Muscle fatigue is usually defined as a decrease in the ability to produce force.
Frequency Superposition -- A Multi-Frequency Stimulation Method in SSVEP-based BCIs
The steady-state visual evoked potential (SSVEP) is one of the most widely used modalities in brain-computer interfaces (BCIs) due to its many advantages.
Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations
The MolMapNet learned important features that are consistent with the literature-reported molecular features.
Multi-Frequency Canonical Correlation Analysis (MFCCA): A Generalised Decoding Algorithm for Multi-Frequency SSVEP
Stimulation methods that utilise more than one stimulation frequency have been developed for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) with the purpose of increasing the number of targets that can be presented simultaneously.
Clustering Bioactive Molecules in 3D Chemical Space with Unsupervised Deep Learning
We ex-plored the superior learning capability of deep autoencoders for unsupervised clustering of 1. 39 mil-lion bioactive molecules into band-clusters in a 3-dimensional latent chemical space.
Black-Box Attacks against RNN based Malware Detection Algorithms
Recent researches have shown that machine learning based malware detection algorithms are very vulnerable under the attacks of adversarial examples.
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
A generative network is trained to minimize the generated adversarial examples' malicious probabilities predicted by the substitute detector.
BIG-bench Machine Learning
Generative Adversarial Network
+1
Information Utilization Ratio in Heuristic Optimization Algorithms
However, the concept of information utilization has remained vague and abstract because there is no reliable metric to reflect the extent to which the information about the objective function is utilized by heuristic algorithms.
Variational Autoencoders for Semi-supervised Text Classification
Although semi-supervised variational autoencoder (SemiVAE) works in image classification task, it fails in text classification task if using vanilla LSTM as its decoder.
A Cooperative Framework for Fireworks Algorithm
This paper presents a cooperative framework for fireworks algorithm (CoFFWA).
Introduction and Ranking Results of the ICSI 2014 Competition on Single Objective Optimization
This technical report includes the introduction and ranking results of the ICSI 2014 Competition on Single Objective Optimization.
A CUDA-Based Real Parameter Optimization Benchmark
Benchmarking is key for developing and comparing optimization algorithms.
