Search Results for author:
Found 37 papers, 8 papers with code
LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
Attempting to complement this deficiency, we investigate layerwise properties of LoRA on fine-tuning tasks and observe an uncommon skewness of weight norms across different layers.
Are Large Language Models Rational Investors?
Large Language Models (LLMs) are progressively being adopted in financial analysis to harness their extensive knowledge base for interpreting complex market data and trends.
ParZC: Parametric Zero-Cost Proxies for Efficient NAS
Recent advancements in Zero-shot Neural Architecture Search (NAS) highlight the efficacy of zero-cost proxies in various NAS benchmarks.
Fast Implicit Neural Representation Image Codec in Resource-limited Devices
With different parameter settings, our method can outperform popular AE-based codecs in constrained environments in terms of both quality and decoding time, or achieve state-of-the-art reconstruction quality compared to other INR codecs.
MAPO: Advancing Multilingual Reasoning through Multilingual Alignment-as-Preference Optimization
To enhance reasoning abilities in non-dominant languages, we propose a Multilingual-Alignment-as-Preference Optimization framework (MAPO), aiming to align the reasoning processes in other languages with the dominant language.
Plum: Prompt Learning using Metaheuristic
Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models.
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
For end users, our benchmark and findings help better understand different optimization techniques, training and inference frameworks, together with hardware platforms in choosing configurations for deploying LLMs.
FedLPA: Personalized One-shot Federated Learning with Layer-Wise Posterior Aggregation
Efficiently aggregating trained neural networks from local clients into a global model on a server is a widely researched topic in federated learning.
Torsion Graph Neural Networks
This demonstrates that analytic torsion is a highly efficient topological invariant in the characterization of graph structures and can significantly boost the performance of GNNs.
Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach
Depression is a common disease worldwide.
AI of Brain and Cognitive Sciences: From the Perspective of First Principles
Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation.
Research on College Students' Innovation and Entrepreneurship Education from The Perspective of Artificial Intelligence Knowledge-Based Crowdsourcing
Based on the practical process of innovation and entrepreneurship education for college students in the author's university, this study analyzes and deconstructs the key concepts of AI knowledge-based crowdsourcing on the basis of literature research, and analyzes the objective fitting needs of combining AI knowledge-based crowdsourcing with college students' innovation and entrepreneurship education practice through a survey and research of a random sample of college students, and verifies that college students' knowledge and application of AI knowledge-based crowdsourcing in the learning and practice of innovation and entrepreneurship The study also verifies the awareness and application of AI knowledge-based crowdsourcing knowledge by university students in the learning and practice of innovation and entrepreneurship.
Cross-Modality Transformer for Visible-Infrared Person Re-Identification
To alleviate the above issues, we propose a novel Cross-Modality Transformer (CMT) to jointly explore a modality-level alignment module and an instance-level module for VI-ReID.
Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT
To connect human expertise in the decision-making, safeguard trust for this high-stake prediction, and ensure algorithm transparency, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet).
Spacecraft depth completion based on the gray image and the sparse depth map
In this way, the background interference to foreground spacecraft depth completion is effectively avoided.
DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder
Our work proposes a VQ-VAE architecture model with a diffusion decoder (DiVAE) to work as the reconstructing component in image synthesis.
Transmit Design for Joint MIMO Radar and Multiuser Communications with Transmit Covariance Constraint
With this constraint, we formulate and solve the signal-to-interference-plus-noise ratio (SINR) balancing problem for multiuser transmit beamforming via convex optimization.
Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer
To address these issues, we propose a novel end-to-end Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a transformer encoderdecoder architecture, including a pixel context based transformer encoder and a part prototype based transformer decoder.
Newly observed $X(4630)$: a new charmoniumlike molecule
Very recently, the LHCb Collaboration at the Large Hadron Collider at CERN observed new resonance $X(4630)$.
High Energy Physics - Phenomenology
Hidden-charm pentaquarks with triple strangeness due to the $Ω_{c}^{(*)}\bar{D}_s^{(*)}$ interactions
Our results suggest that the $\Omega_{c}\bar D_s^*$ state with $J^P={3}/{2}^{-}$ and the $\Omega_{c}^{*}\bar D_s^*$ state with $J^P={5}/{2}^{-}$ can be recommended as the candidates of the hidden-charm molecular pentaquark with triple strangeness.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Universal behavior of mass gaps existing in the single heavy baryon family
The mass gaps existing in the discovered single heavy flavor baryons are analyzed, which show some universal behaviors.
High Energy Physics - Phenomenology
Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation
Out of this consideration, an enhanced RUL framework focusing on data self-generation is put forward for both non-cyclic and cyclic degradation patterns for the first time.
Unambiguous Delay-Doppler Recovery from Random Phase Coded Pulses
Our method encodes each pulse with a random phase, varying from pulse to pulse, and then processes the received samples jointly to resolve the range ambiguity.
Multi-Features Guidance Network for partial-to-partial point cloud registration
In the correspondences credibility computation module, based on the conflicted relationship between the features matching matrix and the coordinates matching matrix, we score the reliability for each correspondence, which can reduce the impact of mismatched or non-matched points.
Establishing the first hidden-charm pentaquark with strangeness
We study the $P_{cs}(4459)^0$ recently observed by LHCb using the method of QCD sum rules.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Neural Network-based Automatic Factor Construction
This paper proposes Neural Network-based Automatic Factor Construction (NNAFC), a tailored neural network framework that can automatically construct diversified financial factors based on financial domain knowledge and a variety of neural network structures.
Deep Learning Based Defect Detection for Solder Joints on Industrial X-Ray Circuit Board Images
With some advanced algorithms, the new technologies are expected to control the production quality based on the digital images.
FSD-10: A Dataset for Competitive Sports Content Analysis
To promote the research on action recognition from competitive sports video clips, we introduce a Figure Skating Dataset (FSD-10) for finegrained sports content analysis.
PENet: Object Detection using Points Estimation in Aerial Images
Aerial imagery has been increasingly adopted in mission-critical tasks, such as traffic surveillance, smart cities, and disaster assistance.
Alpha Discovery Neural Network based on Prior Knowledge
This paper proposes Alpha Discovery Neural Network (ADNN), a tailored neural network structure which can automatically construct diversified financial technical indicators based on prior knowledge.
Multiple Learning for Regression in big data
Results showed our approaches can achieve closed-form solutions of multiple models at the cost of half training time of the traditional methods for a single model.
Color Recognition for Rubik's Cube Robot
We finally design a Rubik's cube robot and construct a dataset to illustrate the efficiency and effectiveness of our online methods and to indicate the ineffectiveness of offline method by color drifting in our dataset.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Perceptual Visual Interactive Learning
Supervised learning methods are widely used in machine learning.
A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction
We proposed the sparse graph-structured linear mixed model (sGLMM) that can incorporate the relatedness information from traits in a dataset with confounding correction.
A New Medical Diagnosis Method Based on Z-Numbers
In this paper, a new decision making methodology based on Z-numbers is presented.
