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Found 532 papers, 216 papers with code
PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, that directly processes 3D point cloud scenes with large motions in a coarse-to-fine fashion.
CIST@CL-SciSumm 2020, LongSumm 2020: Automatic Scientific Document Summarization
In the LongSumm shared task, we integrate both the extractive and abstractive summarization ways.
End-to-end Speech Translation System Description of LIT for IWSLT 2019
We propose layer-tied self-attention for end-to-end speech translation.
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation
Experimental results on five benchmarks and four cross-domain datasets show the lexicon-based graph convolutional network successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010).
LexiClean: An annotation tool for rapid multi-task lexical normalisation
LexiClean’s main contribution is support for simultaneous in situ token-level modification and annotation that can be rapidly applied corpus wide.
QuickGraph: A Rapid Annotation Tool for Knowledge Graph Extraction from Technical Text
Acquiring high-quality annotated corpora for complex multi-task information extraction (MT-IE) is an arduous and costly process for human-annotators.
Improving Fuzzy Rule Classifier with Brain Storm Optimization and Rule Modification
The expanding complexity and dimensionality in the search space can adversely affect inductive learning in fuzzy rule classifiers, thus impacting the scalability and accuracy of fuzzy systems.
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs. Clinical Teams
Employing RadOnc-GPT for in-basket message draft generation has the potential to alleviate the workload of clinical care teams and reduce healthcare costs by producing high-quality, timely responses.
PMSS: Pretrained Matrices Skeleton Selection for LLM Fine-tuning
Low-rank adaptation (LoRA) and its variants have recently gained much interest due to their ability to avoid excessive inference costs.
ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback
Recently, tool-augmented LLMs have gained increasing attention.
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding
These fine-tuned VLMs may still ignore the relationships between UI pages, neglect the roles of elements in page transitions and lack inter-UI understanding.
Mixture of Diverse Size Experts
The Sparsely-Activated Mixture-of-Experts (MoE) has gained increasing popularity for scaling up large language models (LLMs) without exploding computational costs.
OPUS: Occupancy Prediction Using a Sparse Set
Mainstream occupancy prediction works first discretize the 3D environment into voxels, then perform classification on such dense grids.
Investigation of Hierarchical Spectral Vision Transformer Architecture for Classification of Hyperspectral Imagery
To address this issue, a unified hierarchical spectral vision Transformer architecture, specifically tailored for HSI classification, is investigated.
Length Desensitization in Directed Preference Optimization
Direct Preference Optimization (DPO) is widely utilized in the Reinforcement Learning from Human Feedback (RLHF) phase to align Large Language Models (LLMs) with human preferences, thereby enhancing both their harmlessness and efficacy.
SX-Stitch: An Efficient VMS-UNet Based Framework for Intraoperative Scoliosis X-Ray Image Stitching
In scoliosis surgery, the limited field of view of the C-arm X-ray machine restricts the surgeons' holistic analysis of spinal structures . This paper presents an end-to-end efficient and robust intraoperative X-ray image stitching method for scoliosis surgery, named SX-Stitch.
CD-NGP: A Fast Scalable Continual Representation for Dynamic Scenes
We present CD-NGP, which is a fast and scalable representation for 3D reconstruction and novel view synthesis in dynamic scenes.
Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings
Sequential recommendation models have achieved state-of-the-art performance using self-attention mechanism.
Dreaming is All You Need
In classification tasks, achieving a harmonious balance between exploration and precision is of paramount importance.
Follow-Your-Canvas: Higher-Resolution Video Outpainting with Extensive Content Generation
Coupling with these two designs enables us to generate higher-resolution outpainting videos with rich content while keeping spatial and temporal consistency.
SWE-bench-java: A GitHub Issue Resolving Benchmark for Java
GitHub issue resolving is a critical task in software engineering, recently gaining significant attention in both industry and academia.
PAGE: Parametric Generative Explainer for Graph Neural Network
Due to the dimensionality reduction of features in the latent space of the auto-encoder, it becomes easier to extract causal features leading to the model's output, which can be easily employed to generate explanations.
Adversarial Attack for Explanation Robustness of Rationalization Models
Based on this, we make a series of recommendations for improving rationalization models in terms of explanation.
ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area
We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks.
MSG-Chart: Multimodal Scene Graph for ChartQA
Automatic Chart Question Answering (ChartQA) is challenging due to the complex distribution of chart elements with patterns of the underlying data not explicitly displayed in charts.
SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions
We present SHIELD (Schema-based Hierarchical Induction for EV supply chain Disruption), a system integrating Large Language Models (LLMs) with domain expertise for EV battery supply chain risk assessment.
AutoFAIR : Automatic Data FAIRification via Machine Reading
Subsequently, FAIR Alignment is employed to make metadata comply with FAIR principles by ontology guidance and semantic matching.
Evaluating the Translation Performance of Large Language Models Based on Euas-20
In recent years, with the rapid development of deep learning technology, large language models (LLMs) such as BERT and GPT have achieved breakthrough results in natural language processing tasks.
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs
To our knowledge, this is the first work to integrate KANs into GNN architectures tailored for molecular representation learning.
MotionCraft: Crafting Whole-Body Motion with Plug-and-Play Multimodal Controls
However, employing a unified model to achieve various generation tasks with different condition modalities presents two main challenges: motion distribution drifts across different tasks (e. g., co-speech gestures and text-driven daily actions) and the complex optimization of mixed conditions with varying granularities (e. g., text and audio).
LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning
The early detection and precise diagnosis of liver tumors are tasks of critical clinical value, yet they pose significant challenges due to the high heterogeneity and variability of liver tumors.
Video-Language Alignment via Spatio-Temporal Graph Transformer
In this paper, we propose a novel Spatio-Temporal Graph Transformer module to uniformly learn spatial and temporal contexts for video-language alignment pre-training (dubbed STGT).
Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations
However, as the coarse-grained structured pruning poses large damage to the highly interconnected model, achieving a high compression ratio for scaled-up LLMs remains a challenge.
Sequential Manipulation Against Rank Aggregation: Theory and Algorithm
Since it is independent of rank aggregation and lacks effective protection mechanisms, we disrupt the data collection process by fabricating pairwise comparisons without knowledge of the future data or the true distribution.
Feynman-Kac Operator Expectation Estimator
This diffusion bridge model is universal and reduces the training time of the PINN.
Centerline Boundary Dice Loss for Vascular Segmentation
We validated cbDice's efficacy on three diverse vascular segmentation datasets, encompassing both 2D and 3D, and binary and multi-class segmentation.
Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents
With the remarkable advancements of large language models (LLMs), LLM-based agents have become a research hotspot in human-computer interaction.
GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization
The extraordinary ability of generative models emerges as a new trend in image editing and generating realistic images, posing a serious threat to the trustworthiness of multimedia data and driving the research of image manipulation detection and location(IMDL).
FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud
Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation.
Autonomous Agents for Collaborative Task under Information Asymmetry
Together with InfoNav, iAgents organizes human information in a mixed memory to provide agents with accurate and comprehensive information for exchange.
Hacking Encrypted Wireless Power: Cyber-Security of Dynamic Charging
To stimulate the progress of energy encryption technology and point out security holes, this paper proposes a decryption method for the fundamental principle of encrypted frequency-varying wireless power transfer.
Fine-Grained Urban Flow Inference with Multi-scale Representation Learning
In order to effectively learn multi-scale information across time and space, we propose an effective fine-grained urban flow inference model called UrbanMSR, which uses self-supervised contrastive learning to obtain dynamic multi-scale representations of neighborhood-level and city-level geographic information, and fuses multi-scale representations to improve fine-grained accuracy.
Multi-Agent Software Development through Cross-Team Collaboration
We anticipate that our work will guide LLM agents towards a cross-team paradigm and contribute to their significant growth in but not limited to software development.
CIMRL: Combining IMitation and Reinforcement Learning for Safe Autonomous Driving
Modern approaches to autonomous driving rely heavily on learned components trained with large amounts of human driving data via imitation learning.
A$^{2}$-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder
A$^{2}$-MAE integrates an anchor-aware masking strategy and a geographic encoding module to comprehensively exploit the properties of RS images.
Scaling Large-Language-Model-based Multi-Agent Collaboration
Pioneering advancements in large language model-powered agents have underscored the design pattern of multi-agent collaboration, demonstrating that collective intelligence can surpass the capabilities of each individual.
A Parameter-efficient Language Extension Framework for Multilingual ASR
Covering all languages with a multilingual speech recognition model (MASR) is very difficult.
Integrating Text and Image Pre-training for Multi-modal Algorithmic Reasoning
Our model is based on two pre-trained models, dedicated to extract features from text and image respectively.
Follow-Your-Pose v2: Multiple-Condition Guided Character Image Animation for Stable Pose Control
While existing character image animation methods using pose sequences and reference images have shown promising performance, they tend to struggle with incoherent animation in complex scenarios, such as multiple character animation and body occlusion.
Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents Assisted by Large Language Models
Even for a conservative estimate, 80% of enterprise data reside in unstructured files, stored in data lakes that accommodate heterogeneous formats.
Follow-Your-Emoji: Fine-Controllable and Expressive Freestyle Portrait Animation
We present Follow-Your-Emoji, a diffusion-based framework for portrait animation, which animates a reference portrait with target landmark sequences.
SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM
In inference, it boosts speeds by up to 37% and reduces memory by 1GB per GPU.
Active Use of Latent Constituency Representation in both Humans and Large Language Models
Based on the word deletion behaviors, we can reconstruct the latent constituency tree representation of a sentence for both humans and LLMs.
$\text{Di}^2\text{Pose}$: Discrete Diffusion Model for Occluded 3D Human Pose Estimation
Continuous diffusion models have demonstrated their effectiveness in addressing the inherent uncertainty and indeterminacy in monocular 3D human pose estimation (HPE).
FreeTuner: Any Subject in Any Style with Training-free Diffusion
With the advance of diffusion models, various personalized image generation methods have been proposed.
Quality assurance of organs-at-risk delineation in radiotherapy
Automatic segmentation can be used to reduce the physician workload and improve the consistency.
High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates
HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.
Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images.
CleanGraph: Human-in-the-loop Knowledge Graph Refinement and Completion
CleanGraph allows users to perform Create, Read, Update, and Delete (CRUD) operations on their graphs, as well as apply models in the form of plugins for graph refinement and completion tasks.
Iterative Experience Refinement of Software-Developing Agents
We propose two fundamental patterns: the successive pattern, refining based on nearest experiences within a task batch, and the cumulative pattern, acquiring experiences across all previous task batches.
Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples
For verbose videos, a frame feature diversity loss is proposed to increase the feature diversity among frames.
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness Perspective
Graph representation learning has emerged as a machine learning go-to technique, outperforming traditional tabular view of data across many domains.
Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation
We design a Hierarchical Adaptive Alignment model to concurrently learn the fine-grained fragment correspondence between two modalities and align these representations of fragments in three levels.
Functional Protein Design with Local Domain Alignment
The core challenge of de novo protein design lies in creating proteins with specific functions or properties, guided by certain conditions.
Simulation-Free Determination of Microstructure Representative Volume Element Size via Fisher Scores
We propose a simulation-free alternative that determines RVE size based only on a micrograph.
What Causes the Failure of Explicit to Implicit Discourse Relation Recognition?
We consider an unanswered question in the discourse processing community: why do relation classifiers trained on explicit examples (with connectives removed) perform poorly in real implicit scenarios?
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification
Graph representation learning is a fundamental research issue in various domains of applications, of which the inductive learning problem is particularly challenging as it requires models to generalize to unseen graph structures during inference.
CodeS: Natural Language to Code Repository via Multi-Layer Sketch
For feedback-based evaluation, we develop a VSCode plugin for CodeS and engage 30 participants in conducting empirical studies.
Event-Triggered State Estimation Through Confidence Level
This paper considers the state estimation problem for discrete-time linear systems under event-triggered scheme.
Reversible Jump Attack to Textual Classifiers with Modification Reduction
Recent studies on adversarial examples expose vulnerabilities of natural language processing (NLP) models.
Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model
While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge.
LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models
Customization generation techniques have significantly advanced the synthesis of specific concepts across varied contexts.
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model
Benefiting from the popularity and scalably usability of Segment Anything Model (SAM), we first extract different regions according to semantic information and then track them through the video stream to maintain the temporal consistency.
Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples
In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models.
OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models
We also observe that the initiation denoising timestep for noise blending is the key to identity preservation and layout.
Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts
Despite recent advances in image-to-video generation, better controllability and local animation are less explored.
DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation
However, many of these works face challenges in identifying correct output modalities and generating coherent images accordingly as the number of output modalities increases and the conversations go deeper.
Category-Agnostic Pose Estimation for Point Clouds
The goal of object pose estimation is to visually determine the pose of a specific object in the RGB-D input.
ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval
Specifically, our proposed ToolRerank includes Adaptive Truncation, which truncates the retrieval results related to seen and unseen tools at different positions, and Hierarchy-Aware Reranking, which makes retrieval results more concentrated for single-tool queries and more diverse for multi-tool queries.
RIS-Enabled Joint Near-Field 3D Localization and Synchronization in SISO Multipath Environments
Reconfigurable Intelligent Surfaces (RIS) show great promise in the realm of 6th generation (6G) wireless systems, particularly in the areas of localization and communication.
Large Language Models are In-Context Molecule Learners
Specifically, ICMA incorporates the following three stages: Hybrid Context Retrieval, Post-retrieval Re-ranking, and In-context Molecule Tuning.
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
Existing multi-modality image registration algorithms rely on statistical-based similarity measures or local structural image representations.
A Comprehensive Evaluation of Quantization Strategies for Large Language Models
Our experimental results indicate that LLMs with 4-bit quantization can retain performance comparable to their non-quantized counterparts, and perplexity can serve as a proxy metric for quantized LLMs on most benchmarks.
Uniformly Safe RL with Objective Suppression for Multi-Constraint Safety-Critical Applications
Safe reinforcement learning tasks are a challenging domain despite being very common in the real world.
Analysing The Impact of Sequence Composition on Language Model Pre-Training
In this work, we find that applying causal masking can lead to the inclusion of distracting information from previous documents during pre-training, which negatively impacts the performance of the models on language modelling and downstream tasks.
AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization
This paper presents a novel adversarial attack strategy, AICAttack (Attention-based Image Captioning Attack), designed to attack image captioning models through subtle perturbations on images.
ChemLLM: A Chemical Large Language Model
However, the community lacks an LLM specifically designed for chemistry.
Poisson Process for Bayesian Optimization
BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function through a probabilistic surrogate model, including Tree-structured Parzen Estimator (TPE), random forest (SMAC), and Gaussian process (GP).
NFT1000: A Visual Text Dataset For Non-Fungible Token Retrieval
With the rise of 'Metaverse' and 'Web3. 0', NFT ( Non-Fungible Token ) has emerged as a kind of pivotal digital asset, garnering significant attention.
Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain
We expect that agents should learn to enhance the extent to which humans achieve these goals while maintaining agents' original abilities (e. g., winning games).
A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research
The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment across multiple sectors, including Software Engineering (SE).
Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images
Once attackers maliciously induce high energy consumption and latency time (energy-latency cost) during inference of VLMs, it will exhaust computational resources.
The Radiation Oncology NLP Database
ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration.
LUPET: Incorporating Hierarchical Information Path into Multilingual ASR
Toward high-performance multilingual automatic speech recognition (ASR), various types of linguistic information and model design have demonstrated their effectiveness independently.
Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA
The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology.
DrugAssist: A Large Language Model for Molecule Optimization
Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery.
Experiential Co-Learning of Software-Developing Agents
Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents.
What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning
We present deita (short for Data-Efficient Instruction Tuning for Alignment), a series of models fine-tuned from LLaMA and Mistral models using data samples automatically selected with our proposed approach.
Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
Therefore, current works first train a model on source domains with sufficiently labeled data, and then transfer the model to target domains where only rarely labeled data is available.
DreamTuner: Single Image is Enough for Subject-Driven Generation
Diffusion-based models have demonstrated impressive capabilities for text-to-image generation and are expected for personalized applications of subject-driven generation, which require the generation of customized concepts with one or a few reference images.
T-Code: Simple Temporal Latent Code for Efficient Dynamic View Synthesis
Novel view synthesis for dynamic scenes is one of the spotlights in computer vision.
Local Conditional Controlling for Text-to-Image Diffusion Models
It focuses on controlling specific local region according to user-defined image conditions, while the remaining regions are only conditioned by the original text prompt.
Enhancing the Rationale-Input Alignment for Self-explaining Rationalization
Rationalization empowers deep learning models with self-explaining capabilities through a cooperative game, where a generator selects a semantically consistent subset of the input as a rationale, and a subsequent predictor makes predictions based on the selected rationale.
MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation
This work revisits the relationship between CNNs and Transformers in lightweight universal networks for medical image segmentation, aiming to integrate the advantages of both worlds at the infrastructure design level.
SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution
In this paper, we present a high-performance deep neural network for weak target image segmentation, including medical image segmentation and infrared image segmentation.
Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: physics enhances generalizability
The noisy probing dose method showed better generalizability in the 6 outlier cases than the ROI-based and beam mask-based methods with 3D Gamma passing rates (for prostate cancer, targets: 89. 32%$\pm$1. 45% vs. 93. 48%$\pm$1. 51% vs. 96. 79%$\pm$0. 83%, OARs: 85. 87%$\pm$1. 73% vs. 91. 15%$\pm$1. 13% vs. 94. 29%$\pm$1. 01%).
SmoothVideo: Smooth Video Synthesis with Noise Constraints on Diffusion Models for One-shot Video Tuning
By applying the loss to existing one-shot video tuning methods, we significantly improve the overall consistency and smoothness of the generated videos.
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP
Contrastive Vision-Language Pre-training, known as CLIP, has shown promising effectiveness in addressing downstream image recognition tasks.
FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication
This article proposes Blockchain-based Federated Learning (FBChain) model for federated learning parameter communication to overcome the above two problems.
Damped Proximal Augmented Lagrangian Method for weakly-Convex Problems with Convex Constraints
For the former case, DPALM achieves the complexity of $\widetilde{\mathcal{O}}\left(\varepsilon^{-2. 5} \right)$ to produce an $\varepsilon$-KKT point by applying an accelerated proximal gradient (APG) method to each DPALM subproblem.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
Evaluating multiple large language models in pediatric ophthalmology
IMPORTANCE The response effectiveness of different large language models (LLMs) and various individuals, including medical students, graduate students, and practicing physicians, in pediatric ophthalmology consultations, has not been clearly established yet.
Evaluating Large Language Models in Ophthalmology
Methods: A 100-item ophthalmology single-choice test was administered to three different LLMs (GPT-3. 5, GPT-4, and PaLM2) and three different professional levels (medical undergraduates, medical masters, and attending physicians), respectively.
Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions
Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education.
Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications
The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods.
DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy
Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks.
Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks
Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeling could be beneficial. In this work, we propose HyperGraph neural network for ERE ($\hgnn{}$), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model).
Simple Hardware-Efficient PCFGs with Independent Left and Right Productions
Scaling dense PCFGs to thousands of nonterminals via a low-rank parameterization of the rule probability tensor has been shown to be beneficial for unsupervised parsing.
Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report
Conclusions: Given the accuracy of GPT-4 in re-labeling structure names of both target volumes and normal tissues as presented in this work, LLMs are poised to be the preferred method for standardizing structure names in radiation oncology, especially considering the rapid advancements in LLM capabilities that are likely to continue.
LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
We thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M.
Ranked #1 on
Zero-shot Audio Classification
on VGG-Sound
(using extra training data)