Search Results for author:
Found 33 papers, 9 papers with code
Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis
On the other hand, we improve the model's fit to normal data distributions through Fourier Frequency Focus, thereby enhancing the effectiveness of anomaly detection.
A Deep Reinforcement Learning Framework for Dynamic Portfolio Optimization: Evidence from China's Stock Market
Artificial intelligence is transforming financial investment decision-making frameworks, with deep reinforcement learning demonstrating substantial potential in robo-advisory applications.
Meta-Reflection: A Feedback-Free Reflection Learning Framework
Despite the remarkable capabilities of large language models (LLMs) in natural language understanding and reasoning, they often display undesirable behaviors, such as generating hallucinations and unfaithful reasoning.
ELMS: Elasticized Large Language Models On Mobile Devices
To address this issue, we introduce ELMS, an on-device LLM service designed to provide elasticity in both the model and prompt dimensions of an LLMaaS.
Fast On-device LLM Inference with NPUs
On-device inference for Large Language Models (LLMs), driven by increasing privacy concerns and advancements of mobile-sized models, has gained significant interest.
LLM-Powered Test Case Generation for Detecting Tricky Bugs
Conventional automated test generation tools struggle to generate test oracles and tricky bug-revealing test inputs.
Identifying the Largest RoCoF and Its Implications
This finding facilitates the development of the optimal nodal inertia dispatch method and the nodal inertia market mechanism in a convex and concise form.
QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines
Variational quantum circuits (VQCs) have become a powerful tool for implementing Quantum Neural Networks (QNNs), addressing a wide range of complex problems.
Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation
This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation.
Contrastive Learning
Weakly supervised Semantic Segmentation
+1
Fast Distributed Inference Serving for Large Language Models
Large language models (LLMs) power a new generation of interactive AI applications exemplified by ChatGPT.
A Soft Coordination Method of Heterogeneous Devices in Distribution System Voltage Control
The proposed method achieves the soft coordination by establishing a modified actor-critic algorithm to train a proxy model of inverters.
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation
Personalized Federated Learning (PFL) aims to learn personalized models for each client based on the knowledge across all clients in a privacy-preserving manner.
Federated Semi-Supervised Learning with Annotation Heterogeneity
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data.
Compound Batch Normalization for Long-tailed Image Classification
It can model the feature space more comprehensively and reduce the dominance of head classes.
PriorLane: A Prior Knowledge Enhanced Lane Detection Approach Based on Transformer
Lane detection is one of the fundamental modules in self-driving.
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning
An important type of FL is cross-silo FL, which enables a small scale of organizations to cooperatively train a shared model by keeping confidential data locally and aggregating weights on a central parameter server.
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features
Experiments on several benchmark datasets show that the proposed CReFF is an effective solution to obtain a promising FL model under heterogeneous and long-tailed data.
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search.
Deep Reinforcement Learning for Long-Short Portfolio Optimization
Key innovations include: (1) Development of a comprehensive short-selling mechanism in continuous trading that accounts for dynamic evolution of transactions across time periods; (2) Design of a long-short optimization framework integrating deep neural networks for processing multi-dimensional financial time series with mean Sharpe ratio reward functions.
Federated Mutual Learning
The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.
Federated Neural Architecture Search
To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data.
Approximate Query Service on Autonomous IoT Cameras
Elf is a runtime for an energy-constrained camera to continuously summarize video scenes as approximate object counts.
Databases
Galaxy Learning -- A Position Paper
Model training, as well as the communication, is achieved with blockchain and its smart contracts.
Comment on "All-optical machine learning using diffractive deep neural networks"
Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications.
Neural Article Pair Modeling for Wikipedia Sub-article Matching
Nowadays, editors tend to separate different subtopics of a long Wiki-pedia article into multiple sub-articles.
Block-wise Lensless Compressive Camera
2) The coding patterns used in each block can be the same, therefore the sensing matrix is only of the block size compared to the entire image size in existing $\text{L}^2\text{C}^2$.
Multi-resolution Compressive Sensing Reconstruction
We consider the problem of reconstructing an image from compressive measurements using a multi-resolution grid.
Compressive Sensing via Low-Rank Gaussian Mixture Models
We develop a new compressive sensing (CS) inversion algorithm by utilizing the Gaussian mixture model (GMM).
Lensless Compressive Imaging
We develop a lensless compressive imaging architecture, which consists of an aperture assembly and a single sensor, without using any lens.
Noise Analysis for Lensless Compressive Imaging
In this paper, we perform noise analysis of this lensless compressive imaging architecture and compare it with pinhole aperture imaging and lens aperture imaging.
Signal to Noise Ratio in Lensless Compressive Imaging
We will show that the SNR in the LCI is independent of the image resolution, while the SNR in either pinhole aperture imaging or lens aperture imaging decreases as the image resolution increases.
Multi-view in Lensless Compressive Imaging
Multi-view images are acquired by a lensless compressive imaging architecture, which consists of an aperture assembly and multiple sensors.
