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Found 37 papers, 30 papers with code
HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese
We investigate the usage of entity linking (EL)in downstream tasks and present the first modularized EL toolkit for easy task adaptation.
OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases.
Uncovering the Impact of Chain-of-Thought Reasoning for Direct Preference Optimization: Lessons from Text-to-SQL
Direct Preference Optimization (DPO) has proven effective in complex reasoning tasks like math word problems and code generation.
Auto-Prompting SAM for Weakly Supervised Landslide Extraction
These informative prompts are able to identify the extent of landslide areas (box prompts) and denote the centers of landslide objects (point prompts), guiding SAM in landslide segmentation.
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Kolmogorov-Arnold Network for Remote Sensing Image Semantic Segmentation
Semantic segmentation plays a crucial role in remote sensing applications, where the accurate extraction and representation of features are essential for high-quality results.
CoT-based Synthesizer: Enhancing LLM Performance through Answer Synthesis
Current inference scaling methods, such as Self-consistency and Best-of-N, have proven effective in improving the accuracy of LLMs on complex reasoning tasks.
Dynamic Scaling of Unit Tests for Code Reward Modeling
Motivated by the observation that scaling the number of solutions improves LLM performance, we explore the impact of scaling unit tests to enhance reward signal quality.
DeepSeek-V3 Technical Report
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.
SAM Decoding: Speculative Decoding via Suffix Automaton
Speculative decoding (SD) has been demonstrated as an effective technique for lossless LLM inference acceleration.
P$^2$ Law: Scaling Law for Post-Training After Model Pruning
Pruning has become a widely adopted technique for reducing the hardware requirements of large language models (LLMs).
MANet: Fine-Tuning Segment Anything Model for Multimodal Remote Sensing Semantic Segmentation
Building upon recent advancements in vision foundation models, particularly the Segment Anything Model (SAM), this study introduces a novel Multimodal Adapter-based Network (MANet) for multimodal remote sensing semantic segmentation.
MineNetCD: A Benchmark for Global Mining Change Detection on Remote Sensing Imagery
First, we establish a global mining change detection dataset featuring more than 70k paired patches of bi-temporal high-resolution remote sensing images and pixel-level annotations from 100 mining sites worldwide.
SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation
We introduce SpreadsheetBench, a challenging spreadsheet manipulation benchmark exclusively derived from real-world scenarios, designed to immerse current large language models (LLMs) in the actual workflow of spreadsheet users.
PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery
Semantic segmentation, as a basic tool for intelligent interpretation of remote sensing images, plays a vital role in many Earth Observation (EO) applications.
Pedestrian Attribute Recognition as Label-balanced Multi-label Learning
Rooting in the scarcity of most attributes, realistic pedestrian attribute datasets exhibit unduly skewed data distribution, from which two types of model failures are delivered: (1) label imbalance: model predictions lean greatly towards the side of majority labels; (2) semantics imbalance: model is easily overfitted on the under-represented attributes due to their insufficient semantic diversity.
MaskCD: A Remote Sensing Change Detection Network Based on Mask Classification
Change detection (CD) from remote sensing (RS) images using deep learning has been widely investigated in the literature.
Ranked #4 on
Change Detection
on SYSU-CD
Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking
Detecting non-factual content is a longstanding goal to increase the trustworthiness of large language models (LLMs) generations.
Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation
Unsupervised domain adaptation (UDA) techniques are vital for semantic segmentation in geosciences, effectively utilizing remote sensing imagery across diverse domains.
RS3Mamba: Visual State Space Model for Remote Sensing Images Semantic Segmentation
Experimental results on two widely used datasets, ISPRS Vaihingen and LoveDA Urban, demonstrate the effectiveness and potential of the proposed RS3Mamba.
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
We introduce TableLLM, a robust large language model (LLM) with 13 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios.
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
However, the high-frequency details generated by DDPM often suffer from misalignment with HR images due to the model's tendency to overlook long-range semantic contexts.
Reverse That Number! Decoding Order Matters in Arithmetic Learning
Recent advancements in pretraining have demonstrated that modern Large Language Models (LLMs) possess the capability to effectively learn arithmetic operations.
CodeS: Towards Building Open-source Language Models for Text-to-SQL
To address the limitations, we introduce CodeS, a series of pre-trained language models with parameters ranging from 1B to 15B, specifically designed for the text-to-SQL task.
Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery
The presence of cloud layers severely compromises the quality and effectiveness of optical remote sensing (RS) images.
SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
Furthermore, the boundary loss capitalizes on the distinctive features of SGB by directing the model's attention to the boundary information of the object.
GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPM
Furthermore, the Difference Conditional Encoder (DCE), is designed to guide the generation of CD maps by exploiting multi-level difference features.
GCRDN: Global Context-Driven Residual Dense Network for Remote Sensing Image Superresolution
To cope with this problem, this work proposes a novel global context-driven residual dense network (GCRDN) for satellite image SR based on the encoder and decoder architecture.
GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation
We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge.
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning.
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions.
Gated SwitchGAN for multi-domain facial image translation
The existing methods generally provide a discriminator with an auxiliary classifier to impose domain translation.
Graph Contrastive Learning for Anomaly Detection
To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.
GuidedStyle: Attribute Knowledge Guided Style Manipulation for Semantic Face Editing
Although significant progress has been made in synthesizing high-quality and visually realistic face images by unconditional Generative Adversarial Networks (GANs), there still lacks of control over the generation process in order to achieve semantic face editing.
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking
In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMiner and external sources to enhance the models transferability in an adversarial manner.
A Comparative Analysis of Feature Selection Methods for Biomarker Discovery in Study of Toxicant-treated Atlantic Cod (Gadus morhua) Liver
Univariate and multivariate feature selection methods can be used for biomarker discovery in analysis of toxicant exposure.
EFSIS: Ensemble Feature Selection Integrating Stability
There are basically two strategies for ensemble feature selection, namely data perturbation and function perturbation.
