Search Results for author:
Found 55 papers, 18 papers with code
Goal-guided Generative Prompt Injection Attack on Large Language Models
Although there is currently a large amount of research on prompt injection attacks, most of these black-box attacks use heuristic strategies.
Knowledge Graph Large Language Model (KG-LLM) for Link Prediction
The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques.
Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation
We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB camera.
Towards Training A Chinese Large Language Model for Anesthesiology
3) We introduce a standardized benchmark for evaluating medical LLM in Anesthesiology.
Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities
However, there is a research gap in the LLMs' preferences in this field.
Rethinking the Evaluation of Pre-trained Text-and-Layout Models from an Entity-Centric Perspective
Therefore, we claim the necessary standards for an ideal benchmark to evaluate the information extraction ability of PTLMs.
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System
Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve accuracy of disease prediction.
Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
Legged robots navigating cluttered environments must be jointly agile for efficient task execution and safe to avoid collisions with obstacles or humans.
AttackEval: How to Evaluate the Effectiveness of Jailbreak Attacking on Large Language Models
In our research, we pioneer a novel approach to evaluate the effectiveness of jailbreak attacks on Large Language Models (LLMs), such as GPT-4 and LLaMa2, diverging from traditional robustness-focused binary evaluations.
Bio-Image Informatics Index BIII: A unique database of image analysis tools and workflows for and by the bioimaging community
Software tools (> 1300), image databases for benchmarking (>20) and training materials (>70) for bio image analysis are referenced and curated following standards constructed by the community and then reaching a broader audience.
Loss Masking Is Not Needed in Decoder-only Transformer for Discrete-token-based ASR
We find that applying the conventional cross-entropy loss on input speech tokens does not consistently improve the ASR performance over the Loss Masking approach.
Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction
However, BIO-tagging scheme relies on the correct order of model inputs, which is not guaranteed in real-world NER on scanned VrDs where text are recognized and arranged by OCR systems.
Ranked #1 on
Entity Linking
on FUNSD
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End
In this paper, we model perception failures as invisible obstacles and pits, and train a reinforcement learning (RL) based local navigation policy to guide our legged robot.
SPGM: Prioritizing Local Features for enhanced speech separation performance
Dual-path is a popular architecture for speech separation models (e. g. Sepformer) which splits long sequences into overlapping chunks for its intra- and inter-blocks that separately model intra-chunk local features and inter-chunk global relationships.
Ranked #5 on
Speech Separation
on WSJ0-2mix
Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning
Generalized zero-shot learning (GZSL) aims to recognize samples from both seen and unseen classes using only seen class samples for training.
Lifelike Agility and Play on Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models
In this work, we propose a framework for driving legged robots act like real animals with lifelike agility and strategy in complex environments.
A Simple and Effective Baseline for Attentional Generative Adversarial Networks
Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task.
Image Blending Algorithm with Automatic Mask Generation
In recent years, image blending has gained popularity for its ability to create visually stunning content.
HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure.
ACA-Net: Towards Lightweight Speaker Verification using Asymmetric Cross Attention
In this paper, we propose ACA-Net, a lightweight, global context-aware speaker embedding extractor for Speaker Verification (SV) that improves upon existing work by using Asymmetric Cross Attention (ACA) to replace temporal pooling.
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings
Prior studies diagnose the anisotropy problem in sentence representations from pre-trained language models, e. g., BERT, without fine-tuning.
Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach
Public opinion is a crucial factor in shaping political decision-making.
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Ranked #1 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
Adaptive Knowledge Distillation between Text and Speech Pre-trained Models
Learning on a massive amount of speech corpus leads to the recent success of many self-supervised speech models.
DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks
Subsequently, we propose a bipartite graph neural network model, DoubleH, which aims to better utilize homogeneous and heterogeneous information in user stance detection tasks.
Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning
Terrain-aware locomotion has become an emerging topic in legged robotics.
A Holistic Approach to Undesired Content Detection in the Real World
We present a holistic approach to building a robust and useful natural language classification system for real-world content moderation.
Training language models to follow instructions with human feedback
In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback.
Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training
Aspect-based sentiment analysis aims to identify the sentiment polarity of a specific aspect in product reviews.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Hierarchical information matters: Text classification via tree based graph neural network
Inspired by the structural entropy, we construct the coding tree of the graph by minimizing the structural entropy and propose HINT, which aims to make full use of the hierarchical information contained in the text for the task of text classification.
A Partition Filter Network for Joint Entity and Relation Extraction
In our encoder, we leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition.
Ranked #1 on
Relation Extraction
on ADE Corpus
Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study
Moreover, images with different noise levels can be denoised to improve the reproducibility using these models without re-training, as long as the noise intensity is equal or lower than that in high-noise CTs.
Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics
In this article, we treat lung cancer diagnosis as a multiple instance learning (MIL) problem in order to better reflect the diagnosis process in the clinical setting and for the higher interpretability of the output.
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation
Our method is able to reveal the hidden model biases not directly shown in the test dataset.
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one.
DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration
One of the main challenges of transferring the policy learned in a simulated environment to real world, is the discrepancy between the dynamics of the two environments.
Robotics
Domain Adaptation Gaze Estimation by Embedding with Prediction Consistency
In domain adaption, we design an embedding representation with prediction consistency to ensure that the linear relationship between gaze directions in different domains remains consistent on gaze space and embedding space.
An Efficient Adversarial Attack for Tree Ensembles
We study the problem of efficient adversarial attacks on tree based ensembles such as gradient boosting decision trees (GBDTs) and random forests (RFs).
Parameter estimation in FACS-seq enables high-throughput characterization of phenotypic heterogeneity
Phenotypic heterogeneity is a most fascinating property of a population of cells, which shows the differences among individuals even with the same genetic background and extracellular environmental conditions.
Learning End-to-End Action Interaction by Paired-Embedding Data Augmentation
In recognition-based action interaction, robots' responses to human actions are often pre-designed according to recognized categories and thus stiff.
Attention-Oriented Action Recognition for Real-Time Human-Robot Interaction
Despite the notable progress made in action recognition tasks, not much work has been done in action recognition specifically for human-robot interaction.
Self-Guided Adaptation: Progressive Representation Alignment for Domain Adaptive Object Detection
In this work, we propose a Self-Guided Adaptation (SGA) model, target at aligning feature representation and transferring object detection models across domains while considering the instantaneous alignment difficulty.
Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research
Claims database and electronic health records database do not usually capture kinship or family relationship information, which is imperative for genetic research.
A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring
For fault diagnosis, a cost-sensitive deep belief network (namely ECS-DBN) is applied to deal with the imbalanced data problem for tool state estimation.
A Cost-Sensitive Deep Belief Network for Imbalanced Classification
Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge.
Efficient Column Generation for Cell Detection and Segmentation
To solve this integer program, we propose a column generation formulation where the pricing program is solved via exact optimization of very small scale integer programs.
Multi-Person Pose Estimation via Column Generation
We give a novel integer program formulation of the multi-person pose estimation problem, in which variables correspond to assignments of parts in the image to poses in a two-tier, hierarchical way.
The Candidate Multi-Cut for Cell Segmentation
Two successful approaches for the segmentation of biomedical images are (1) the selection of segment candidates from a merge-tree, and (2) the clustering of small superpixels by solving a Multi-Cut problem.
On Reject and Refine Options in Multicategory Classification
In many real applications of statistical learning, a decision made from misclassification can be too costly to afford; in this case, a reject option, which defers the decision until further investigation is conducted, is often preferred.
Efficient Pose and Cell Segmentation using Column Generation
We study the problems of multi-person pose segmentation in natural images and instance segmentation in biological images with crowded cells.
Multi-category Angle-based Classifier Refit
In particular, we give theoretical insights on why heavy regularization terms are often needed in high dimensional applications, and how this can lead to bias in probability estimation.
An active efficient coding model of the optokinetic nystagmus
This framework models the joint emergence of both perception and behavior, and accounts for the importance of the development of normal vergence control and binocular vision in achieving normal monocular OKN (mOKN) behaviors.
Intrinsically Motivated Learning of Visual Motion Perception and Smooth Pursuit
We extend the framework of efficient coding, which has been used to model the development of sensory processing in isolation, to model the development of the perception/action cycle.
