Search Results for author:
Found 50 papers, 19 papers with code
Injecting Adrenaline into LLM Serving: Boosting Resource Utilization and Throughput via Attention Disaggregation
The memory-bound nature of decoding-phase attention computation inherently enables an effective offloading strategy, yielding two complementary advantages: 1) improved memory capacity and bandwidth utilization in prefill instances, and 2) increased decoding batch sizes that enhance compute utilization in decoding instances, collectively boosting overall system performance.
Micro Text Classification Based on Balanced Positive-Unlabeled Learning
In real-world text classification tasks, negative texts often contain a minimal proportion of negative content, which is especially problematic in areas like text quality control, legal risk screening, and sensitive information interception.
LawGPT: Knowledge-Guided Data Generation and Its Application to Legal LLM
To address these limitations, we study data generation for legal reasoning to improve the legal reasoning performance of open-source LLMs with the help of proprietary LLMs.
Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of Language Models
The integration of slow-thinking mechanisms into large language models (LLMs) offers a promising way toward achieving Level 2 AGI Reasoners, as exemplified by systems like OpenAI's o1.
Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning
Advances in artificial intelligence (AI) including foundation models (FMs), are increasingly transforming human society, with smart city driving the evolution of urban living. Meanwhile, vehicle crowdsensing (VCS) has emerged as a key enabler, leveraging vehicles' mobility and sensor-equipped capabilities.
Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning
In this paper, we present the first theoretical error decomposition analysis of these techniques, breaking down their error into estimation error and model error.
Contrast-Aware Calibration for Fine-Tuned CLIP: Leveraging Image-Text Alignment
Vision-language models (VLMs), such as CLIP, have demonstrated exceptional generalization capabilities and can quickly adapt to downstream tasks through prompt fine-tuning.
TabFSBench: Tabular Benchmark for Feature Shifts in Open Environment
TabFSBench evaluates impacts of four distinct feature-shift scenarios on four tabular model categories across various datasets and assesses the performance of large language models (LLMs) and tabular LLMs in the tabular benchmark for the first time.
Vision-Language Model Selection and Reuse for Downstream Adaptation
The proposal is highly computationally efficient and growable since the model labeling process is completed target task independent and the ability could grow with the number of candidate VLMs.
CGI: Identifying Conditional Generative Models with Example Images
In this paper, we propose Conditional Generative Model Identification (CGI), which aims to provide an effective way to identify the most suitable model using user-provided example images rather than requiring users to manually review a large number of models with example images.
BMIP: Bi-directional Modality Interaction Prompt Learning for VLM
Vision-language models (VLMs) have exhibited remarkable generalization capabilities, and prompt learning for VLMs has attracted great attention for the ability to adapt pre-trained VLMs to specific downstream tasks.
You Only Submit One Image to Find the Most Suitable Generative Model
In this paper, we propose a novel setting called Generative Model Identification (GMI), which aims to enable the user to identify the most appropriate generative model(s) for the user's requirements from a large number of candidate models efficiently.
Fully Test-time Adaptation for Tabular Data
To this end, we propose the Fully Test-time Adaptation for Tabular data, namely FTAT, which enables FTTA methods to robustly optimize the label distribution of predictions, adapt to shifted covariate distributions, and suit a variety of tasks and models effectively.
TexLiverNet: Leveraging Medical Knowledge and Spatial-Frequency Perception for Enhanced Liver Tumor Segmentation
Integrating textual data with imaging in liver tumor segmentation is essential for enhancing diagnostic accuracy.
Enabling Small Models for Zero-Shot Selection and Reuse through Model Label Learning
In this paper, we attempt to demonstrate that by constructing a model hub and aligning models with their functionalities using model labels, new tasks can be solved in a zero-shot manner by effectively selecting and reusing models in the hub.
Point Source Identification Using Singularity Enriched Neural Networks
The inverse problem of recovering point sources represents an important class of applied inverse problems.
LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model
Large language models (LLMs), including both proprietary and open-source models, have showcased remarkable capabilities in addressing a wide range of downstream tasks.
Robustness Assessment of Mathematical Reasoning in the Presence of Missing and Contradictory Conditions
To further study this problem, we develop a benchmark called Problems with Missing and Contradictory conditions (PMC) and introduce two novel metrics to evaluate the performance of few-shot prompting methods in these scenarios.
DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection
In this paper, we explore a problem setting called Open-world Prompt Tuning (OPT), which involves tuning prompts on base classes and evaluating on a combination of base and new classes.
Online Resource Allocation for Edge Intelligence with Colocated Model Retraining and Inference
This highlights the advantages and applicable scenarios of colocating model retraining and inference.
Opara: Exploiting Operator Parallelism for Expediting DNN Inference on GPUs
GPUs have become the \emph{defacto} hardware devices for accelerating Deep Neural Network (DNN) inference workloads.
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset
Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data.
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models.
Ranked #1 on
Long-tail Learning
on CIFAR-100-LT (ρ=100)
(using extra training data)
Fine-Grained Image Classification
Long-tail learning with class descriptors
Solving Elliptic Optimal Control Problems via Neural Networks and Optimality System
In this work, we investigate a neural network based solver for optimal control problems (without / with box constraint) for linear and semilinear second-order elliptic problems.
Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services
Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures.
Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon Markets
Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among accuracy loss, carbon emission, and unknown future costs.
Electrical Impedance Tomography with Deep Calderón Method
Electrical impedance tomography (EIT) is a noninvasive medical imaging modality utilizing the current-density/voltage data measured on the surface of the subject.
Conductivity Imaging from Internal Measurements with Mixed Least-Squares Deep Neural Networks
In this work we develop a novel approach using deep neural networks to reconstruct the conductivity distribution in elliptic problems from one measurement of the solution over the whole domain.
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally.
GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers
Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques.
Solving Elliptic Problems with Singular Sources using Singularity Splitting Deep Ritz Method
Extensive numerical experiments in two- and multi-dimensional spaces with point sources, line sources or their combinations are presented to illustrate the efficiency of the proposed approach, and a comparative study with several existing approaches based on neural networks is also given, which shows clearly its competitiveness for the specific class of problems.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
LAMDA-SSL: Semi-Supervised Learning in Python
The second part shows the usage of LAMDA-SSL by abundant examples in detail.
Robust Deep Semi-Supervised Learning: A Brief Introduction
Semi-supervised learning (SSL) is the branch of machine learning that aims to improve learning performance by leveraging unlabeled data when labels are insufficient.
Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping
With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the community.
STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data
However, in many real-world applications, it is desirable to have SSL algorithms that not only classify the samples drawn from the same distribution of labeled data but also detect out-of-distribution (OOD) samples drawn from an unknown distribution.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control
To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and capacity.
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally.
CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices
CoEdge utilizes available computation and communication resources at the edge and dynamically partitions the DNN inference workload adaptive to devices' computing capabilities and network conditions.
When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network
Ultra-dense edge computing (UDEC) has great potential, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: i) efficient utilization of multiple 5G resources (e. g., computation, communication, storage and service resources); ii) low overhead offloading decision making and resource allocation strategies; and iii) privacy and security protection schemes.
The energy technique for the six-step BDF method
In combination with the Grenander--Szeg\"o theorem, we observe that a relaxed positivity condition on multipliers, milder than the basic %fundamental requirement of the Nevanlinna--Odeh multipliers that the sum of the absolute values of their components is strictly less than $1$, makes the energy technique applicable to the stability analysis of BDF methods for parabolic equations with selfadjoint elliptic part.
Numerical Analysis Numerical Analysis
HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing
We develop a novel \textit{hybrid parallelism} method, which is the key to HierTrain, to adaptively assign the DNN model layers and the data samples across the three levels of edge device, edge server and cloud center.
DeepCP: Deep Learning Driven Cascade Prediction Based Autonomous Content Placement in Closed Social Network
Accordingly, we further propose a novel autonomous content placement mechanism CP-GAN which adopts the generative adversarial network (GAN) for agile placement decision making to reduce the content access latency and enhance users' QoE.
HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
We further formulate a joint computation and communication resource allocation and edge association problem for device users under HFEL framework to achieve global cost minimization.
Distributed, Parallel, and Cluster Computing
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing
As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention.
TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain
Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection.
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence.
Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing
To address this challenge in terms of the performance-cost trade-off, in this paper we study the mobile edge service performance optimization problem under long-term cost budget constraint.
Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy
As the backbone technology of machine learning, deep neural networks (DNNs) have have quickly ascended to the spotlight.
