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Found 14 papers, 7 papers with code
VCD-Texture: Variance Alignment based 3D-2D Co-Denoising for Text-Guided Texturing
Recent research on texture synthesis for 3D shapes benefits a lot from dramatically developed 2D text-to-image diffusion models, including inpainting-based and optimization-based approaches.
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
Predicting simple function classes has been widely used as a testbed for developing theory and understanding of the trained Transformer's in-context learning (ICL) ability.
Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making
In this paper, we consider the supervised pretrained transformer for a class of sequential decision-making problems.
Towards Better Statistical Understanding of Watermarking LLMs
We consider the trade-off between model distortion and detection ability and formulate it as a constrained optimization problem based on the green-red algorithm of Kirchenbauer et al. (2023a).
LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries
With the proliferation of spatio-textual data, Top-k KNN spatial keyword queries (TkQs), which return a list of objects based on a ranking function that evaluates both spatial and textual relevance, have found many real-life applications.
UnifiedSSR: A Unified Framework of Sequential Search and Recommendation
In this work, we propose a Unified framework of Sequential Search and Recommendation (UnifiedSSR) for joint learning of user behavior history in both search and recommendation scenarios.
RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model
In this work, we propose an open-source benchmark named RTLLM, for generating design RTL with natural language instructions.
When No-Rejection Learning is Consistent for Regression with Rejection
This paper studies the regression with rejection (RwR) problem and investigates a no-rejection learning strategy that uses all the data to learn the predictor.
Understanding Uncertainty Sampling
Uncertainty sampling is a prevalent active learning algorithm that queries sequentially the annotations of data samples which the current prediction model is uncertain about.
Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming
More importantly, our new approach only needs the observations of the optimal solution in the training data rather than the objective function, which makes it a new and natural approach to the inverse linear programming problem under both contextual and context-free settings; we also analyze the proposed method under both offline and online settings, and demonstrate its performance using numerical experiments.
Non-stationary Bandits with Knapsacks
Finally, we also extend the non-stationarity measure to the problem of online convex optimization with constraints and obtain new regret bounds accordingly.
Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks
As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers.
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
Ground State and Hidden Symmetry of Magic Angle Graphene at Even Integer Filling
This low energy manifold is also found in the Hartree-Fock numerical calculation.
Strongly Correlated Electrons Mesoscale and Nanoscale Physics
