Search Results for author:
Found 42 papers, 12 papers with code
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing
Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET).
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing
In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance.
SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
We hope that our work can serve as a valuable addition to existing MLLM benchmarks, providing insightful observations and inspiring further research in the area of text-rich visual comprehension with MLLMs.
Variance-Dependent Regret Bounds for Non-stationary Linear Bandits
We investigate the non-stationary stochastic linear bandit problem where the reward distribution evolves each round.
Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models
2) Role Prompting assigns a central prompt to the general domain and a unique role prompt to each specific domain to minimize inter-domain confusion during training.
EgoPlan-Bench: Benchmarking Egocentric Embodied Planning with Multimodal Large Language Models
Given diverse environmental inputs, including real-time task progress, visual observations, and open-form language instructions, a proficient task planner is expected to predict feasible actions, which is a feat inherently achievable by Multimodal Large Language Models (MLLMs).
A Method to Improve the Performance of Reinforcement Learning Based on the Y Operator for a Class of Stochastic Differential Equation-Based Child-Mother Systems
This paper introduces a novel operator, termed the Y operator, to elevate control performance in Actor-Critic(AC) based reinforcement learning for systems governed by stochastic differential equations(SDEs).
A Benchmark for Text Expansion: Datasets, Metrics, and Baselines
This work presents a new task of Text Expansion (TE), which aims to insert fine-grained modifiers into proper locations of the plain text to concretize or vivify human writings.
Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting
Numerous works are proposed to align large language models (LLMs) with human intents to better fulfill instructions, ensuring they are trustful and helpful.
Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study
Evaluating the quality of generated text is a challenging task in NLP, due to the inherent complexity and diversity of text.
Scaling Multi-Objective Security Games Provably via Space Discretization Based Evolutionary Search
Then, a many-objective EA is used for optimization in the low-dimensional discrete solution space to obtain a well-spaced Pareto front.
A High-Frequency Focused Network for Lightweight Single Image Super-Resolution
We propose a novel High-Frequency Focused Network (HFFN) through High-Frequency Focused Blocks (HFFBs) that selectively enhance high-frequency information while minimizing redundant feature computation of low-frequency information.
Multi-Semantic Interactive Learning for Object Detection
Single-branch object detection methods use shared features for localization and classification, yet the shared features are not fit for the two different tasks simultaneously.
Class-level Multiple Distributions Representation are Necessary for Semantic Segmentation
Meanwhile, we design a class multiple distributions consistency strategy to construct discriminative multiple distribution representations of embedded pixels.
Understanding Impacts of Task Similarity on Backdoor Attack and Detection
With extensive studies on backdoor attack and detection, still fundamental questions are left unanswered regarding the limits in the adversary's capability to attack and the defender's capability to detect.
THz ISAC: A Physical-Layer Perspective of Terahertz Integrated Sensing and Communication
In this article, challenges from THz channel and transceiver perspectives, as well as difficulties of ISAC are elaborated.
Can We Do Better Than Random Start? The Power of Data Outsourcing
Based on three different scenarios, we propose simulation-based algorithms that can utilize a small amount of outsourced data to find good initial points accordingly.
A Preliminary Research on Space Situational Awareness Based on Event Cameras
Event camera is a new type of sensor that is different from traditional cameras.
Event-Based Dense Reconstruction Pipeline
The trigger event is the change of the brightness irradiated on the pixel.
DICP: Doppler Iterative Closest Point Algorithm
We propose a new Doppler velocity objective function that exploits the compatibility of each point's Doppler measurement and the sensor's current motion estimate.
Improved YOLOv5 network for real-time multi-scale traffic sign detection
Moreover, in practical application, it is difficult for common methods to improve the detection accuracy of multi-scale traffic signs while ensuring real-time detection.
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance.
Ranked #1 on
Learning with noisy labels
on CIFAR-100N
Research on Event Accumulator Settings for Event-Based SLAM
The experiment results show that our method can achieve better performance in most sequences compared with the state-of-the-art event frame based SLAM algorithm.
Packet Routing with Graph Attention Multi-agent Reinforcement Learning
Packet routing is a fundamental problem in communication networks that decides how the packets are directed from their source nodes to their destination nodes through some intermediate nodes.
Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learning
Furthermore, we leverage a variational auto-encoder (VAE) model to capture the life-long novelty of states, which is combined with the global JFI score to form multimodal intrinsic rewards.
FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users
The framework runs on the phones of the users and also on a server that coordinates learning from all users in the system.
Channel Measurement and Ray-Tracing-Statistical Hybrid Modeling for Low-Terahertz Indoor Communications
Numerical results demonstrate that the proposed hybrid channel model shows good agreement with the measurement and outperforms the conventional statistical and geometric-based stochastic channel model in terms of the temporal-spatial characteristics.
Information Theory Information Theory
A Primal-Dual Approach to Constrained Markov Decision Processes
In many operations management problems, we need to make decisions sequentially to minimize the cost while satisfying certain constraints.
Optimization and Control
Variable Division and Optimization for Constrained Multiobjective Portfolio Problems
For example, a variable of portfolio problem can be divided into two partial variables, i. e. the selection of assets and the allocation of capital.
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning
In this work, we develop practical user scheduling algorithms for downlink bursty traffic with emphasis on user fairness.
TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis
On one hand, we propose to discriminate ground-truth waveform from synthetic one in frequency domain for offering more consistency guarantees instead of only in time domain.
Reverse-engineering Bar Charts Using Neural Networks
Reverse-engineering bar charts extracts textual and numeric information from the visual representations of bar charts to support application scenarios that require the underlying information.
Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection
We consider the stochastic contextual bandit problem under the high dimensional linear model.
Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes
Adverse drug events (ADEs) are a serious health problem that can be life-threatening.
Accelerating Nonconvex Learning via Replica Exchange Langevin Diffusion
To attain the advantages of both regimes, we propose to use replica exchange, which swaps between two Langevin diffusions with different temperatures.
Towards in-store multi-person tracking using head detection and track heatmaps
In addition, we describe an illustrative example of the use of this dataset for tracking participants based on a head tracking model in an effort to minimize errors due to occlusion.
Utilizing Dependence among Variables in Evolutionary Algorithms for Mixed-Integer Programming: A Case Study on Multi-Objective Constrained Portfolio Optimization
Consequently, we point out one challenge, faced by a direct coding scheme for MOEAs, to this problem.
Contrastive Learning from Pairwise Measurements
In this paper, we study a semiparametric model where the pairwise measurements follow a natural exponential family distribution with an unknown base measure.
Alternative Technique to Asymmetry Analysis-Based Overlapping for Foot Ulcer Examination: Scalable Scanning
Asymmetry analysis based on the overlapping of thermal images proved able to detect inflammation and, predict foot ulceration.
A 128 channel Extreme Learning Machine based Neural Decoder for Brain Machine Interfaces
The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99. 3% for movement type.
Discriminative Local Sparse Representations for Robust Face Recognition
We propose a probabilistic graphical model framework to explicitly mine the conditional dependencies between these distinct sparse local features.
