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Found 9 papers, 5 papers with code
Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables
Different from previous fact extraction and verification tasks that only consider evidence of a single format, FEVEROUS brings further challenges by extending the evidence format to both plain text and tables.
ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context
Our work underscores the importance of the planning and reasoning abilities towards a model over tabular tasks with generalizability and interpretability.
Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data
To address this gap, we introduce the Quantitative Reasoning with Data (QRData) benchmark, aiming to evaluate Large Language Models' capability in statistical and causal reasoning with real-world data.
MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving
We expect this modular design to boost academic progress and industrial deployment of NeRF-based autonomous driving simulation.
Lawyer LLaMA Technical Report
Specifically, we inject domain knowledge during the continual training stage and teach the model to learn professional skills using properly designed supervised fine-tuning tasks.
Self-Aligning Depth-regularized Radiance Fields for Asynchronous RGB-D Sequences
It has been shown that learning radiance fields with depth rendering and depth supervision can effectively promote the quality and convergence of view synthesis.
City-scale Incremental Neural Mapping with Three-layer Sampling and Panoptic Representation
Neural implicit representations are drawing a lot of attention from the robotics community recently, as they are expressive, continuous and compact.
OmniNeRF: Hybriding Omnidirectional Distance and Radiance fields for Neural Surface Reconstruction
3D reconstruction from images has wide applications in Virtual Reality and Automatic Driving, where the precision requirement is very high.
LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF
In this paper, we present LATITUDE: Global Localization with Truncated Dynamic Low-pass Filter, which introduces a two-stage localization mechanism in city-scale NeRF.
