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Found 44 papers, 22 papers with code
Visualizing How Embeddings Generalize
Deep metric learning is often used to learn an embedding function that captures the semantic differences within a dataset.
ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition
Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks.
Congestion-aware Evacuation Routing using Augmented Reality Devices
We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations while keeping tracks of all evacuees' locations.
A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization
Concept normalization, the task of linking textual mentions of concepts to concepts in an ontology, is challenging because ontologies are large.
Lineage Evolution Reinforcement Learning
In the process of agent evolution, we refer to the characteristics of natural genetic behavior, add lineage factor to ensure the retention of potential performance of agent, and comprehensively consider the current performance and lineage value when evaluating the performance of agent.
Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments
In this paper, we rethink the problem of scene reconstruction from an embodied agent's perspective: While the classic view focuses on the reconstruction accuracy, our new perspective emphasizes the underlying functions and constraints such that the reconstructed scenes provide \em{actionable} information for simulating \em{interactions} with agents.
A Dashboard for Mitigating the COVID-19 Misinfodemic
This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of and intervention on COVID-19 misinfodemic on Twitter.
Joint Models for Answer Verification in Question Answering Systems
This paper studies joint models for selecting correct answer sentences among the top $k$ provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems.
A Comparative Study on Robust Graph Neural Networks to Structural Noises
Although a series of robust GNNs have been proposed, they are evaluated with different structural noises, and it lacks a systematic comparison with consistent settings.
Double Retrieval and Ranking for Accurate Question Answering
Recent work has shown that an answer verification step introduced in Transformer-based answer selection models can significantly improve the state of the art in Question Answering.
In Situ Answer Sentence Selection at Web-scale
Current answer sentence selection (AS2) applied in open-domain question answering (ODQA) selects answers by ranking a large set of possible candidates, i. e., sentences, extracted from the retrieved text.
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures.
Understanding Physical Effects for Effective Tool-use
We present a robot learning and planning framework that produces an effective tool-use strategy with the least joint efforts, capable of handling objects different from training.
Sequential Manipulation Planning on Scene Graph
We devise a 3D scene graph representation, contact graph+ (cg+), for efficient sequential task planning.
Structural Alignment for Network Pruning through Partial Regularization
To mitigate this gap, we first learn a target sub-network during the model training process, and then we use this sub-network to guide the learning of model weights through partial regularization.
A Reconfigurable Data Glove for Reconstructing Physical and Virtual Grasps
In this work, we present a reconfigurable data glove design to capture different modes of human hand-object interactions, which are critical in training embodied artificial intelligence (AI) agents for fine manipulation tasks.
USER: Unsupervised Structural Entropy-based Robust Graph Neural Network
To this end, we propose USER, an unsupervised robust version of graph neural networks that is based on structural entropy.
Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning Framework that Supports Diverse Compositional Reasoning
Languages models have been successfully applied to a variety of reasoning tasks in NLP, yet the language models still suffer from compositional generalization.
Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution
Geocoding is the task of converting location mentions in text into structured data that encodes the geospatial semantics.
User Behavior Simulation with Large Language Model based Agents
Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism of human decision process.
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
Building upon this, we leverage offline RL techniques for off-policy LTR and propose the Click Model-Agnostic Unified Off-policy Learning to Rank (CUOLR) method, which could be easily applied to a wide range of click models.
X-VoE: Measuring eXplanatory Violation of Expectation in Physical Events
Intuitive physics is pivotal for human understanding of the physical world, enabling prediction and interpretation of events even in infancy.
BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors.
A Survey on Large Language Model based Autonomous Agents
In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation
Compared to the segment anything model, SPPNet shows roughly 20 times faster inference, with 1/70 parameters and computational cost.
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy
Learnersourcing offers great potential for scalable education through student content creation.
SGA: A Graph Augmentation Method for Signed Graph Neural Networks
Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links.
CSG: Curriculum Representation Learning for Signed Graph
Signed graphs are valuable for modeling complex relationships with positive and negative connections, and Signed Graph Neural Networks (SGNNs) have become crucial tools for their analysis.
SegReg: Segmenting OARs by Registering MR Images and CT Annotations
This manual process is highly time-consuming and expensive, limiting the number of patients who can receive timely radiotherapy.
S-T CRF: Spatial-Temporal Conditional Random Field for Human Trajectory Prediction
Trajectory prediction is of significant importance in computer vision.
PAD: Self-Supervised Pre-Training with Patchwise-Scale Adapter for Infrared Images
Self-supervised learning (SSL) for RGB images has achieved significant success, yet there is still limited research on SSL for infrared images, primarily due to three prominent challenges: 1) the lack of a suitable large-scale infrared pre-training dataset, 2) the distinctiveness of non-iconic infrared images rendering common pre-training tasks like masked image modeling (MIM) less effective, and 3) the scarcity of fine-grained textures making it particularly challenging to learn general image features.
On the Emergence of Symmetrical Reality
Artificial intelligence (AI) has revolutionized human cognitive abilities and facilitated the development of new AI entities capable of interacting with humans in both physical and virtual environments.
SheetAgent: A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models
To bridge the gap with the real-world requirements, we introduce $\textbf{SheetRM}$, a benchmark featuring long-horizon and multi-category tasks with reasoning-dependent manipulation caused by real-life challenges.
A Deep Learning Approach to Diabetes Diagnosis
Diabetes, resulting from inadequate insulin production or utilization, causes extensive harm to the body.
Motion Mamba: Efficient and Long Sequence Motion Generation with Hierarchical and Bidirectional Selective SSM
Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging.
Strong and Controllable Blind Image Decomposition
To address this need, we add controllability to the blind image decomposition process, allowing users to enter which types of degradation to remove or retain.
LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning
Through a series of simulations in a box-packing domain, we quantitatively demonstrate the effectiveness of LLM^3 in solving TAMP problems and the efficiency in selecting action parameters.
Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch
Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise, making their widespread adoption challenging.
Manifold Regularization Classification Model Based On Improved Diffusion Map
Manifold regularization model is a semi-supervised learning model that leverages the geometric structure of a dataset, comprising a small number of labeled samples and a large number of unlabeled samples, to generate classifiers.
Sine Activated Low-Rank Matrices for Parameter Efficient Learning
Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learning.
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis
The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph.
Closed-Loop Open-Vocabulary Mobile Manipulation with GPT-4V
Autonomous robot navigation and manipulation in open environments require reasoning and replanning with closed-loop feedback.
JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA
The oxygen saturation level in the blood (SaO2) is crucial for health, particularly in relation to sleep-related breathing disorders.
Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction
An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended.
