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Found 11 papers, 2 papers with code
Relational Representation Learning Network for Cross-Spectral Image Patch Matching
Therefore, an innovative relational representation learning idea is proposed for the first time, which simultaneously focuses on sufficiently mining the intrinsic features of individual image patches and the relations between image patch features.
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning
Our solution, Layerwise Proximal Replay (LPR), balances learning from new and replay data while only allowing for gradual changes in the hidden activation of past data.
Nearest Neighbour Score Estimators for Diffusion Generative Models
Score function estimation is the cornerstone of both training and sampling from diffusion generative models.
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
Based on this, we propose a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system and discusses the corresponding mitigation strategies.
Self-supervision meets kernel graph neural models: From architecture to augmentations
Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data.
Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images
The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulations in the field of autonomous driving.
OBBStacking: An Ensemble Method for Remote Sensing Object Detection
Trying to address these problems, this paper proposes OBBStacking, an ensemble method that is compatible with OBBs and combines the detection results in a learned fashion.
Vehicle Type Specific Waypoint Generation
We develop a generic mechanism for generating vehicle-type specific sequences of waypoints from a probabilistic foundation model of driving behavior.
Conditional Permutation Invariant Flows
We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density.
Critic Sequential Monte Carlo
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors.
