Search Results for author:
Found 20 papers, 4 papers with code
CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation
We observe models' poor performance on CodeFlowBench, with a substantial performance drop in the iterative codeflow scenario.
Bootstrapped Model Predictive Control
In this work, we introduce Bootstrapped Model Predictive Control (BMPC), a novel algorithm that performs policy learning in a bootstrapped manner.
Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
This technical report presents Prithvi-EO-2. 0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1. 0.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification
This study introduces a novel approach to terrain feature classification by incorporating spatial point pattern statistics into deep learning models.
BPO: Towards Balanced Preference Optimization between Knowledge Breadth and Depth in Alignment
Building on this, we further propose Balanced Preference Optimization (BPO), designed to dynamically augment the knowledge depth of each sample.
ShifCon: Enhancing Non-Dominant Language Capabilities with a Shift-based Contrastive Framework
Although fine-tuning Large Language Models (LLMs) with multilingual data can rapidly enhance the multilingual capabilities of LLMs, they still exhibit a performance gap between the dominant language (e. g., English) and non-dominant ones due to the imbalance of training data across languages.
The KnowWhereGraph Ontology
KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs.
Prediction of Brent crude oil price based on LSTM model under the background of low-carbon transition
In the field of global energy and environment, crude oil is an important strategic resource, and its price fluctuation has a far-reaching impact on the global economy, financial market and the process of low-carbon development.
Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi's domain adaptability
A series of experiments were designed to assess Prithvi's performance as compared to other pre-trained task-specific AI models in geospatial image analysis.
Optimizing Language Model's Reasoning Abilities with Weak Supervision
Therefore, we present \textsc{PuzzleBen}, a weakly supervised benchmark that comprises 25, 147 complex questions, answers, and human-generated rationales across various domains, such as brainteasers, puzzles, riddles, parajumbles, and critical reasoning tasks.
GeoAI Reproducibility and Replicability: a computational and spatial perspective
We then discuss the factors that may cause the lack of R&R in GeoAI research, with an emphasis on (1) the selection and use of training data; (2) the uncertainty that resides in the GeoAI model design, training, deployment, and inference processes; and more importantly (3) the inherent spatial heterogeneity of geospatial data and processes.
Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoning
We conduct a series of experiments to prove LLMs can obtain benefits from mistakes in both directions.
A2CI: A Cloud-based, Service-oriented Geospatial Cyberinfrastructure to Support Atmospheric Research
Big earth science data offers the scientific community great opportunities.
Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping
To evaluate the performance of large AI vision models, especially Meta's Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model.
Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking
Chain-of-Thought(CoT) prompting and its variants explore equipping large language models (LLMs) with high-level reasoning abilities by emulating human-like linear cognition and logic.
Assessment of a new GeoAI foundation model for flood inundation mapping
Vision foundation models are a new frontier in Geospatial Artificial Intelligence (GeoAI), an interdisciplinary research area that applies and extends AI for geospatial problem solving and geographic knowledge discovery, because of their potential to enable powerful image analysis by learning and extracting important image features from vast amounts of geospatial data.
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models
The dataset contains 8, 351 questions categorized into four different key areas: Financial Academic Knowledge, Financial Industry Knowledge, Financial Security Knowledge, and Financial Agent.
Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features
This paper introduces a real-time GeoAI workflow for large-scale image analysis and the segmentation of Arctic permafrost features at a fine-granularity.
Semantic Similarity Measure of Natural Language Text through Machine Learning and a Keyword-Aware Cross-Encoder-Ranking Summarizer -- A Case Study Using UCGIS GIS&T Body of Knowledge
Initiated by the University Consortium of Geographic Information Science (UCGIS), GIS&T Body of Knowledge (BoK) is a community-driven endeavor to define, develop, and document geospatial topics related to geographic information science and technologies (GIS&T).
Combining Implicit-Explicit View Correlation for Light Field Semantic Segmentation
The other is explicit feature propagation that directly warps features of other views to central view under the guidance of disparity.
